AN INTRODUCTION
TO OPTIMIZATION
WILEY SERIES IN
DISCRETE MATHEMATICS AND OPTIMIZATION
A complete list of titles in this series appears at the end of this volume.
AN INTRODUCTION
TO OPTIMIZATION
Fourth Edition
Edwin K. P. Chong
Colorado State University
Stanislaw H. 2ak
Purdue University
®WILEY
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Library of Congress Cataloging-in-Publication Data
Chong, Edwin Kah Pin.
An introduction to optimization / Edwin K. P. Chong, Colorado State University, Stanislaw H. Zak,
Purdue University. — Fourth edition,
pages cm
Summary: "The purpose of the book is to give the reader a working knowledge of optimization theory
and methods" — Provided by publisher.
Includes bibliographical references and index.
ISBN 978-1-118-27901-4 (hardback)
1. Mathematical optimization. I. Zak, Stanislaw H. II. Title.
QA402.5.C476 2012
519.6—dc23
2012031772
Printed in the United States of America.
10 9 8 7 6 5 4 3 2 1
To my wife, Yat-Yee,
and to my parents, Paul
and Julienne Chong.
Edwin K. P. Chong
To JMJ; my wife,
Mary Ann; and my
parents, Janina and
Konstanty Zak.
Stanislaw H. Zak
CONTENTS
Preface
xiii
PART
1
2
M A T H E M A T I C A L REVIEW
Methods of Proof and Some Notation
3
1.1
1.2
3
5
6
Methods of Proof
Notation
Exercises
Vector Spaces and Matrices
2.1
2.2
2.3
2.4
3
I
Vector and Matrix
Rank of a Matrix
Linear Equations
Inner Products and Norms
Exercises
7
7
13
17
19
22
Transformations
25
3.1
25
Linear Transformations
viii
CONTENTS
3.2
3.3
3.4
3.5
Eigenvalues and Eigenvectors
Orthogonal Projections
Quadratic Forms
Matrix Norms
Exercises
26
29
31
35
40
Concepts from Geometry
45
4.1
4.2
4.3
4.4
4.5
45
46
48
50
52
53
Line Segments
Hyperplanes and Linear Varieties
Convex Sets
Neighborhoods
Poly topes and Polyhedra
Exercises
Elements of Calculus
55
5.1
5.2
5.3
5.4
5.5
5.6
55
62
63
67
68
72
77
Sequences and Limits
Differentiability
The Derivative Matrix
Differentiation Rules
Level Sets and Gradients
Taylor Series
Exercises
PART
II
UNCONSTRAINED O P T I M I Z A T I O N
Basics of Set-Constrained and Unconstrained Optimization
81
6.1
6.2
81
83
93
Introduction
Conditions for Local Minimizers
Exercises
One-Dimensional Search Methods
103
Introduction
Golden Section Search
Fibonacci Method
Bisection Method
Newton's Method
Secant Method
Bracketing
103
104
108
116
116
120
123
7.1
7.2
7.3
7.4
7.5
7.6
7.7
CONTENTS
7.8
8
9
10
11
12
Line Search in Multidimensional Optimization
Exercises
IX
124
126
Gradient Methods
131
8.1
8.2
8.3
131
133
141
153
Introduction
The Method of Steepest Descent
Analysis of Gradient Methods
Exercises
Newton's Method
161
9.1
9.2
9.3
9.4
161
164
168
168
171
Introduction
Analysis of Newton's Method
Levenberg-Marquardt Modification
Newton's Method for Nonlinear Least Squares
Exercises
Conjugate Direction Methods
175
10.1
10.2
10.3
10.4
175
177
182
Introduction
The Conjugate Direction Algorithm
The Conjugate Gradient Algorithm
The Conjugate Gradient Algorithm for Nonquadratic
Problems
Exercises
186
189
Quasi-Newton Methods
193
11.1
11.2
11.3
11.4
11.5
193
194
197
202
207
211
Introduction
Approximating the Inverse Hessian
The Rank One Correction Formula
The DFP Algorithm
The BFGS Algorithm
Exercises
Solving Linear Equations
217
12.1
12.2
12.3
12.4
12.5
217
227
231
232
236
Least-Squares Analysis
The Recursive Least-Squares Algorithm
Solution to a Linear Equation with Minimum Norm
Kaczmarz's Algorithm
Solving Linear Equations in General
X
CONTENTS
Exercises
13
14
Unconstrained Optimization and Neural Networks
253
13.1
13.2
13.3
253
256
258
270
Introduction
Single-Neuron Training
The Backpropagation Algorithm
Exercises
Global Search Algorithms
273
14.1
14.2
14.3
14.4
14.5
273
274
278
282
285
298
Introduction
The Nelder-Mead Simplex Algorithm
Simulated Annealing
Particle Swarm Optimization
Genetic Algorithms
Exercises
PART
15
16
244
III
LINEAR P R O G R A M M I N G
Introduction to Linear Programming
305
15.1
15.2
15.3
15.4
15.5
15.6
15.7
15.8
305
307
314
316
318
324
327
330
335
Brief History of Linear Programming
Simple Examples of Linear Programs
Two-Dimensional Linear Programs
Convex Polyhedra and Linear Programming
Standard Form Linear Programs
Basic Solutions
Properties of Basic Solutions
Geometric View of Linear Programs
Exercises
Simplex Method
339
16.1
16.2
16.3
16.4
16.5
16.6
16.7
339
346
349
350
357
361
364
369
Solving Linear Equations Using Row Operations
The Canonical Augmented Matrix
Updating the Augmented Matrix
The Simplex Algorithm
Matrix Form of the Simplex Method
Two-Phase Simplex Method
Revised Simplex Method
Exercises
CONTENTS
17
18
19
Duality
379
17.1
17.2
379
387
394
21
22
Dual Linear Programs
Properties of Dual Problems
Exercises
Nonsimplex Methods
403
18.1
18.2
18.3
18.4
Introduction
Khachiyan's Method
Affine Scaling Method
Karmarkar's Method
Exercises
403
405
408
413
426
Integer Linear Programming
429
19.1
19.2
19.3
429
430
437
447
PART
20
XI
Introduction
Unimodular Matrices
The Gomory Cutting-Plane Method
Exercises
IV
NONLINEAR CONSTRAINED O P T I M I Z A T I O N
Problems with Equality Constraints
453
20.1
20.2
20.3
20.4
20.5
20.6
453
455
456
463
472
476
481
Introduction
Problem Formulation
Tangent and Normal Spaces
Lagrange Condition
Second-Order Conditions
Minimizing Quadratics Subject to Linear Constraints
Exercises
Problems with Inequality Constraints
487
21.1
21.2
487
496
501
Karush-Kuhn-Tucker Condition
Second-Order Conditions
Exercises
Convex Optimization Problems
509
22.1
22.2
22.3
509
512
521
Introduction
Convex Functions
Convex Optimization Problems
xii
CONTENTS
22.4
23
24
Semidefinite Programming
Exercises
527
540
Algorithms for Constrained Optimization
549
23.1
23.2
23.3
23.4
23.5
549
549
553
557
564
571
Introduction
Projections
Projected Gradient Methods with Linear Constraints
Lagrangian Algorithms
Penalty Methods
Exercises
Multiobjective Optimization
577
24.1
24.2
24.3
24.4
24.5
577
578
581
585
588
596
Introduction
Pareto Solutions
Computing the Pareto Front
From Multiobjective to Single-Objective Optimization
Uncertain Linear Programming Problems
Exercises
References
599
Index
609
PREFACE
Optimization is central to any problem involving decision making, whether
in engineering or in economics. The task of decision making entails choosing
among various alternatives. This choice is governed by our desire to make the
"best" decision. The measure of goodness of the alternatives is described by an
objective function or performance index. Optimization theory and methods
deal with selecting the best alternative in the sense of the given objective
function.
The area of optimization has received enormous attention in recent years,
primarily because of the rapid progress in computer technology, including
the development and availability of user-friendly software, high-speed and
parallel processors, and artificial neural networks. A clear example of this
phenomenon is the wide accessibility of optimization software tools such as the
Optimization Toolbox of MATLAB 1 and the many other commercial software
packages.
There are currently several excellent graduate textbooks on optimization
theory and methods (e.g., [3], [39], [43], [51], [87], [88], [104], [129]), as well
as undergraduate textbooks on the subject with an emphasis on engineering
design (e.g., [1] and [109]). However, there is a need for an introductory
1
MATLAB is a registered trademark of The MathWorks, Inc.
xiii
XIV
PREFACE
textbook on optimization theory and methods at a senior undergraduate or
beginning graduate level. The present text was written with this goal in mind.
The material is an outgrowth of our lecture notes for a one-semester course in
optimization methods for seniors and beginning graduate students at Purdue
University, West Lafayette, Indiana. In our presentation, we assume a working
knowledge of basic linear algebra and multivariable calculus. For the reader's
convenience, a part of this book (Part I) is devoted to a review of the required
mathematical background material. Numerous figures throughout the text
complement the written presentation of the material. We also include a variety
of exercises at the end of each chapter. A solutions manual with complete
solutions to the exercises is available from the publisher to instructors who
adopt this text. Some of the exercises require using MATLAB. The student
edition of MATLAB is sufficient for all of the MATLAB exercises included in
the text. The MATLAB source listings for the MATLAB exercises are also
included in the solutions manual.
The purpose of the book is to give the reader a working knowledge of
optimization theory and methods. To accomplish this goal, we include many
examples that illustrate the theory and algorithms discussed in the text. However, it is not our intention to provide a cookbook of the most recent numerical
techniques for optimization; rather, our goal is to equip the reader with sufficient background for further study of advanced topics in optimization.
The field of optimization is still a very active research area. In recent years,
various new approaches to optimization have been proposed. In this text, we
have tried to reflect at least some of the flavor of recent activity in the area.
For example, we include a discussion of randomized search methods—these include particle swarm optimization and genetic algorithms, topics of increasing
importance in the study of complex adaptive systems. There has also been
a recent surge of applications of optimization methods to a variety of new
problems. An example of this is the use of descent algorithms for the training
of feedforward neural networks. An entire chapter in the book is devoted to
this topic. The area of neural networks is an active area of ongoing research,
and many books have been devoted to this subject. The topic of neural network training fits perfectly into the framework of unconstrained optimization
methods. Therefore, the chapter on feedforward neural networks not only provides an example of application of unconstrained optimization methods but
also gives the reader an accessible introduction to what is currently a topic of
wide interest.
The material in this book is organized into four parts. Part I contains a
review of some basic definitions, notations, and relations from linear algebra,
geometry, and calculus that we use frequently throughout the book. In Part II
we consider unconstrained optimization problems. We first discuss some theoretical foundations of set-constrained and unconstrained optimization, including necessary and sufficient conditions for minimizers and maximizers. This is
followed by a treatment of various iterative optimization algorithms, including line search methods, together with their properties. A discussion of global
PREFACE
XV
search algorithms is included in this part. We also analyze the least-squares
optimization problem and the associated recursive least-squares algorithm.
Parts III and IV are devoted to constrained optimization. Part III deals with
linear programming problems, which form an important class of constrained
optimization problems. We give examples and analyze properties of linear programs, and then discuss the simplex method for solving linear programs. We
also provide a brief treatment of dual linear programming problems. We then
describe some nonsimplex algorithms for solving linear programs: Khachiyan's
method, the affine scaling method, and Karmarkar's method. We wrap up
Part III by discussing integer linear programming problems. In Part IV we
treat nonlinear constrained optimization. Here, as in Part II, we first present
some theoretical foundations of nonlinear constrained optimization problems,
including convex optimization problems. We then discuss different algorithms
for solving constrained optimization problems. We also treat multiobjective
optimization.
Although we have made every effort to ensure an error-free text, we suspect
that some errors remain undetected. For this purpose, we provide online
updated errata that can be found at the Web site for the book, accessible via
http://www.wiley.com/mathematics
We are grateful to several people for their help during the course of writing
this book. In particular, we thank Dennis Goodman of Lawrence Livermore
Laboratories for his comments on early versions of Part II and for making
available to us his lecture notes on nonlinear optimization. We thank Moshe
Kam of Drexel University for pointing out some useful references on nonsimplex methods. We are grateful to Ed Silverman and Russell Quong for their
valuable remarks on Part I of the first edition. We also thank the students
of ECE 580 at Purdue University and ECE 520 and MATH 520 at Colorado
State University for their many helpful comments and suggestions. In particular, we are grateful to Christopher Taylor for his diligent proofreading of
early manuscripts of this book. This fourth edition incorporates many valuable suggestions of users of the first, second, and third editions, to whom we
are grateful.
E. K. P. CHONG AND S. H. ZAK
Fort Collins, Colorado, and West Lafayette, Indiana
PART I
MATHEMATICAL REVIEW
CHAPTER 1
METHODS OF PROOF AND SOME
NOTATION
1.1
Methods of Proof
Consider two statements, "A" and "B," which could be either true or false.
For example, let "A" be the statement "John is an engineering student," and
let "B" be the statement "John is taking a course on optimization." We can
combine these statements to form other statements, such as "A and B" or "A
or B." In our example, "A and B" means "John is an engineering student, and
he is taking a course on optimization." We can also form statements such as
"not A," "not B," "not (A and B)," and so on. For example, "not A" means
"John is not an engineering student." The truth or falsity of the combined
statements depend on the truth or falsity of the original statements, "A" and
"B." This relationship is expressed by means of truth tables; see Tables 1.1
and 1.2.
From the tables, it is easy to see that the statement "not (A and B)" is
equivalent to "(not A) or (not B)" (see Exercise 1.3). This is called DeMorgan's law.
In proving statements, it is convenient to express a combined statement by a
conditional, such as "A implies B," which we denote "A=>B." The conditional
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
3
4
METHODS OF PROOF AND SOME NOTATION
Table 1.1
Truth Table for "A and B" and "A or B"
A
F
F
T
T
B
F
T
F
T
Table 1.2
A and B
F
F
F
T
Truth Table for "not A"
A
F
T
Table 1.3
A or B
F
T
T
T
not A
Y~
F
Truth Table for Conditionals and Biconditionals
A
F
F
T
T
B
F
T
F
T
A^B
T
T
F
T
A <=B
T
F
T
T
A<^B
T
F
F
T
"A=>B" is simply the combined statement "(not A) or B" and is often also
read "A only if B," or "if A then B," or "A is sufficient for B," or "B is
necessary for A."
We can combine two conditional statements to form a biconditional statement of the form "A<i=>B," which simply means "(A=*-B) and (B=>A)." The
statement " Α Φ ^ Β " reads "A if and only if B," or "A is equivalent to B," or
"A is necessary and sufficient for B." Truth tables for conditional and biconditional statements are given in Table 1.3.
It is easy to verify, using the truth table, that the statement "A=>B" is
equivalent to the statement "(not B)=>(not A)." The latter is called the contrapositive of the former. If we take the contrapositive to DeMorgan's law, we
obtain the assertion that "not (A or B)" is equivalent to "(not A) and (not
B)."
Most statements we deal with have the form "A=>B." To prove such a
statement, we may use one of the following three different techniques:
1. The direct method
NOTATION
5
2. Proof by contraposition
3. Proof by contradiction or reductio ad absurdum
In the case of the direct method, we start with "A," then deduce a chain of
various consequences to end with "B."
A useful method for proving statements is proof by contraposition, based
on the equivalence of the statements "A=>B" and "(not B)=>(not A)." We
start with "not B," then deduce various consequences to end with "not A" as
a conclusion.
Another method of proof that we use is proof by contradiction, based on
the equivalence of the statements "A=>B" and "not (A and (not B))." Here
we begin with "A and (not B)" and derive a contradiction.
Occasionally, we use the principle of induction to prove statements. This
principle may be stated as follows. Assume that a given property of positive
integers satisfies the following conditions:
■ The number 1 possesses this property.
■ If the number n possesses this property, then the number n + 1 possesses
it too.
The principle of induction states that under these assumptions any positive
integer possesses the property.
The principle of induction is easily understood using the following intuitive
argument. If the number 1 possesses the given property, then the second
condition implies that the number 2 possesses the property. But, then again,
the second condition implies that the number 3 possesses this property, and so
on. The principle of induction is a formal statement of this intuitive reasoning.
For a detailed treatment of different methods of proof, see [130].
1.2
Notation
Throughout, we use the following notation. If X is a set, then we write x € X
to mean that x is an element of X. When an object x is not an element
of a set X, we write x $. X. We also use the "curly bracket notation" for
sets, writing down the first few elements of a set followed by three dots. For
example, {xi,X2,^3,. · ·} is the set containing the elements χ\,Χ2,χζ, and so
on. Alternatively, we can explicitly display the law of formation. For example,
{x : x £ R, x > 5} reads "the set of all x such that x is real and x is greater
than 5." The colon following x reads "such that." An alternative, notation
for the same set is {x £ M : x > 5}.
If X and Y are sets, then we write X C Y to mean that every element
of X is also an element of Y. In this case, we say that X is a subset of Y.
If X and Y are sets, then we denote by X \ Y ("X minus Y") the set of
all points in X that are not in Y. Note that X \ Y is a subset of X. The
6
METHODS OF PROOF AND SOME NOTATION
notation / : X —■> Y means " / is a function from the set X into the set V."
The symbol := denotes arithmetic assignment. Thus, a statement of the form
x := y means "x becomes y." The symbol = means "equals by definition."
Throughout the text, we mark the end of theorems, lemmas, propositions,
and corollaries using the symbol □. We mark the end of proofs, definitions,
and examples by | .
We use the IEEE style when citing reference items. For example, [77]
represents reference number 77 in the list of references at the end of the book.
EXERCISES
1.1 Construct the truth table for the statement "(not B)=>(not A)," and use
it to show that this statement is equivalent to the statement "A=^B."
1.2 Construct the truth table for the statement "not (A and (not B))," and
use it to show that this statement is equivalent to the statement "A=>B."
1.3 Prove DeMorgan's law by constructing the appropriate truth tables.
1.4 Prove that for any statements A and B, we have "A <^ (A and B) or
(A and (not B))." This is useful because it allows us to prove a statement A
by proving the two separate cases "(A and B)" and "(A and (not B))." For
example, to prove that \x\ > x for any x G M, we separately prove the cases
"|x| > x and x > 0" and "|x| > x and x < 0." Proving the two cases turns
out to be easier than proving the statement \x\ > x directly (see Section 2.4
and Exercise 2.7).
1.5 (This exercise is adopted from [22, pp. 80-81]) Suppose that you are
shown four cards, laid out in a row. Each card has a letter on one side and a
number on the other. On the visible side of the cards are printed the symbols
S
8
3
A
Determine which cards you should turn over to decide if the following rule
is true or false: "If there is a vowel on one side of the card, then there is an
even number on the other side."
CHAPTER 2
VECTOR SPACES AND MATRICES
2.1
Vector and Matrix
We define a column n-vector to be an array of n numbers, denoted
ai
a—
.
a<2
The number α^ is called the zth component of the vector a. Denote by R
the set of real numbers and by R n the set of column n-vectors with real
components. We call R n an n-dimensional real vector space. We commonly
denote elements of R n by lowercase bold letters (e.g., x). The components of
x £ R n are denoted # i , . . . , xn.
We define a row n-vector as
[αι,α 2 ,.. ·,α η ]·
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
8
VECTOR SPACES AND MATRICES
The transpose of a given column vector a is a row vector with corresponding
elements, denoted aT. For example, if
02
a
then
aT = [αι,α 2ii, - · · i
^n\
Equivalently, we may write a = [αχ, α 2 , . . . , α η ] τ . Throughout the text we
adopt the convention that the term vector (without the qualifier row or column) refers to a column vector.
Two vectors a = [ai, a 2 , . . . , an]T and b = [b\, 62, · · · ? M T a r e e Q u a l if
ai — bi, i = 1,2,... ,n.
The sum of the vectors a and 6, denoted a + 6, is the vector
a + b= [ai H-6i,a 2 + 6 2 , . . . , a n + 6 n ] T .
The operation of addition of vectors has the following properties:
1. The operation is commutative:
a + b = b + a.
2. The operation is associative:
(a + b)-\-c = a + (b + c).
3. There is a zero vector
such that
0 = [0,0,...,0]T
a + Q = 0-\-a = a.
The vector
[Oi -
&ι,α2 -
&2?· . · , Α η -
&n]
is called the difference between a and b and is denoted a — b.
The vector 0 - b is denoted —6. Note that
b + (a — b) — a,
— (a — b) = b — a.
VECTOR AND MATRIX
9
The vector b — a is the unique solution of the vector equation
a + x = b.
xn]T is a solution to a + x = b. Then,
Indeed, suppose that x = [xi, x2,...,
a\+x\
=h,
a2 + X2 = fo,
an -\- xn — o n ,
and thus
x = b — a.
We define an operation of multiplication of a vector a G Mn by a real scalar
aGRas
αα = [ααι, αα2,. · ·, α;αη] .
This operation has the following properties:
1. The operation is distributive: for any real scalars a and /?,
a(a + 6) = aa + α&,
(a + β)α — aa + /3a.
2. The operation is associative:
α(βα) = (α/3)α.
3. The scalar 1 satisfies
4. Any scalar a satisfies
l a = a.
a 0 = 0.
5. The scalar 0 satisfies
0a = 0.
6. The scalar —1 satisfies
(—l)a = —a.
Note that aa = 0 if and only if a = 0 or a = 0. To see this, observe that
aa = 0 is equivalent to ααι = aa2 = · · · = αα η = 0. If a = 0 or a = 0,
then aa = 0. If a ^ 0, then at least one of its components α^ φ 0. For this
component, αα^ = 0, and hence we must have a = 0. Similar arguments can
be applied to the case when a / 0 .
10
VECTOR SPACES AND MATRICES
A set of vectors {αχ,... ,ak} is said to be linearly independent if the equality
a\a\ + a2a2 +
l·
akak = 0
implies that all coefficients a*, i = 1 , . . . , fc, are equal to zero. A set of the
vectors {αχ,..., ak} is linearly dependent if it is not linearly independent.
Note that the set composed of the single vector 0 is linearly dependent, for
if a φ 0, then aO — 0. In fact, any set of vectors containing the vector 0 is
linearly dependent.
A set composed of a single nonzero vector a φ 0 is linearly independent
since aa = 0 implies that a = 0.
A vector a is said to be a linear combination of vectors αχ, a 2 , . . . , ak if
there are scalars α χ , . . . , α^ such that
a = OL\a\ + α 2 α 2 Η
+ QfcOfc.
Proposition 2.1 A set of vectors { α ι , α 2 , . . . ,ak} is linearly dependent if
and only if one of the vectors from the set is a linear combination of the
remaining vectors.
□
Proof. =>: If {αι, a 2 , . . . , a^} is linearly dependent, then
OLICLI
+ a2a2 H
l·
α^α^ = 0,
where at least one of the scalars α; Φ 0, whence
di =
OL\
αι
OL2
a2 — · · ·
OLk
α^.
<=: Suppose that
αχ = α 2 α 2 + α 3 α 3 Η
then
( - l ) a i + a2a2 H
l·
h α^α^,
akak = 0.
Because the first scalar is nonzero, the set of vectors { α ι , α 2 , . . . ,α/c} is linearly dependent. The same argument holds if α^, i = 2,...,/c, is a linear
combination of the remaining vectors.
I
A subset V of Rn is called a subspace of R n if V is closed under the operations of vector addition and scalar multiplication. That is, if a and b are
vectors in V, then the vectors a + b and aa are also in V for every scalar a.
Every subspace contains the zero vector 0, for if a is an element of the
subspace, so is (—I)a = — a. Hence, a — a — 0 also belongs to the subspace.
Let α ι , α 2 , . . . ,α^ be arbitrary vectors in W1. The set of all their linear
combinations is called the span of αχ, α 2 , . . . , ak and is denoted
span[ai,a 2 ,...,a/e] = < ^ α ^
: a x , . . . ,ak £ R > .
VECTOR AND MATRIX
11
Given a vector a, the subspace span [a] is composed of the vectors a o , where
a is an arbitrary real number (a G R). Also observe that if a is a linear
combination of αι, α 2 , . . . , α/~, then
s p a n [ a i , a 2 , . . . , α^,α] — s p a n [ a i , a 2 , . . . , α&].
The span of any set of vectors is a subspace.
Given a subspace V, any set of linearly independent vectors
{oi, C&2,. · ·, a>k} C V such that V = span[ai, a 2 , . . . , a/-] is referred to as a
basis of the subspace V. All bases of a subspace V contain the same number
of vectors. This number is called the dimension of V, denoted dim V.
Proposition 2.2 If {ai, a 2 , . . . , a / J zs a fraszs 0/ V, t/ien an?/ vector
can be represented uniquely as
a =
OL\CL\
+ a2a2 H
aofV
h α^α^,
where a^ G R, z = 1, 2 , . . . , k.
□
Proof To prove the uniqueness of the representation of a in terms of the basis
vectors, assume that
a = OL\a\ + α 2 α 2 + · · · + ο^α/c
and
α = βια,ι + ß 2 a 2 H
h Αα^.
We now show that ai — βι, i = 1 , . . . , k. We have
αιαι + a2a2 H
or
h α^α^ = / ^ « l H- β 2 α 2 Η
h /3fcafc
(ai - )3i)ai + (a 2 - Α ) α 2 + · · · + (afc ~ ßk)ak = 0.
Because the set {a* : z = 1,2,..., A:} is linearly independent, OL\ — β\ =
a2 — /?2 = · · · = a/e — ßk — 0, which implies that a* = /?*, 2 = 1 , . . . , fc. I
Suppose that we are given a basis {αχ, α 2 , . . . , α^} of V and a vector a G V
such that
α = αχθι + a 2 a 2 H
h α^α^.
The coefficients a*, i = 1 , . . . , /c, are called the coordinates of a with respect
to the basis {ai, a 2 , . . . , α^}.
The natural basis for R n is the set of vectors
"1"
0
0
0
_0.
Ό"
, e2 =
1
0
0
.0.
12
VECTOR SPACES AND MATRICES
The reason for calling these vectors the natural basis is that
Xl
X2
x
X\e\
+ £2^2 + · · · +
Xn^n
We can similarly define complex vector spaces. For this, let C denote the
set of complex numbers and C n the set of column n-vectors with complex
components. As the reader can easily verify, the set C n has properties similar
to those of R n , where scalars can take complex values.
A matrix is a rectangular array of numbers, commonly denoted by uppercase bold letters (e.g., A). A matrix with m rows and n columns is called an
m x n matrix, and we write
an
«12
a\n
Ö21
Ö22
d2n
Gml
Om2
The real number α^ located in the ith row and jth column is called the (i, j ) t h
entry. We can think of A in terms of its n columns, each of which is a column
vector in R m . Alternatively, we can think of A in terms of its m rows, each
of which is a row n-vector.
Consider the ra x n matrix A above. The transpose of matrix A, denoted
A , is the n x m matrix
an
A1 =
a2i
0>ml
«7712
Ö12
«22
Gin
0,2η
that is, the columns of A are the rows of A , and vice versa.
Let the symbol M m X n denote the set oimxn matrices whose entries are real
numbers. We treat column vectors in IRn as elements of R n x l . Similarly, we
treat row n-vectors as elements of R l x n . Accordingly, vector transposition is
simply a special case of matrix transposition, and we will no longer distinguish
between the two. Note that there is a slight inconsistency in the notation of
row vectors when identified as 1 x n matrices: We separate the components of
the row vector with commas, whereas in matrix notation we do not generally
use commas. However, the use of commas in separating elements in a row
helps to clarify their separation. We use use such commas even in separating
matrices arranged in a horizontal row.
RANK OF A MATRIX
2.2
13
Rank of a Matrix
Consider the m x n matrix
an
au
«In
&2η
A =
Let us denote the kth column of A by α^:
Q>ik
Q>k
Q>mk
The maximal number of linearly independent columns of A is called the rank
of the matrix A, denoted rank A. Note that rank A is the dimension of
span[ai,... , a n ] .
Proposition 2.3 The rank of a matrix A is invariant under the following
operations:
1. Multiplication of the columns of A by nonzero scalars.
2. Interchange of the columns.
3. Addition to a given column a linear combination of other columns.
D
Proof.
1. Let bk = a^a/e, where ctk φ 0, k = 1 , . . . , n, and let B = [61? f>2) · · ·, bn].
Obviously,
s p a n [ a i , a 2 , . . . , o n ] = span[öi,6 2 ,... , 6 n ] ,
and thus
rank A = rankf?.
2. The number of linearly independent vectors does not depend on their
order.
3. Let
b\ = a\ + C2a2 +
b2 = a 2 ,
Vn — Ö-n·
l·
cnan,
14
VECTOR SPACES AND MATRICES
So, for any OJI, . . . , a n ,
αι6ι + 0:262 H l· o; n 6 n = a\a\ + (c*2 + ^1^2)^2 H
and hence
l·
(a n +
aicn)an,
span[6i,6 2 ,··· ,&n] C s p a n [ a i , a 2 , . . . , a n ] .
On the other hand,
αι = 61 - c2b2
cn6n,
«2 = 6 2 ,
ttn — O n .
Hence,
s p a n [ a i , a 2 , . . . , a n ] C span[6i, 6 2 ,. · · ,&n]·
Therefore, rank A = ranki?.
|
A matrix A is said to be square if the number of its rows is equal to the
number of its columns (i.e., it is n x n). Associated with each square matrix
A is a scalar called the determinant of the matrix A, denoted d e t A or \A\.
The determinant of a square matrix is a function of its columns and has the
following properties:
1. The determinant of the matrix A = [αι, a2,...,
of each column; that is,
det[ai,...,Ofc_i,aa f c
+ ßak
an] is a linear function
,afc+i,... ,an]
= a d e t [ a i , . ..,a f c _i,a f c , a f c + i , . . . , a n ]
+ /?det[ai,... ,ak-\,ak
,α^+ι,.. .,an]
for each a, /? G R, a^1}, a^2) € R n .
2. If for some k we have α^ = α^+χ, then
d e t A = d e t [ a i , . . . , α^,α^+ι,... ,α η ] = d e t [ a i , . . . ,a,k,ak,
3. Let
In = [ e i , e 2 , . . . , e n ]
1
0
0 ...
1 ...
0
0
0
0···
1
, On] = 0.
RANK OF A MATRIX
15
where {βχ,..., en} is the natural basis for R n . Then
d e t / n = 1.
Note that if a — β = 0 in property 1, then
d e t [ a i , . . . ,afc_i,0,afc+i,.. . , o n ] = 0.
Thus, if one of the columns is 0, then the determinant is equal to zero.
The determinant does not change its value if we add to a column another
column multiplied by a scalar. This follows from properties 1 and 2 as shown
below:
d e t [ a i , . . . ,afc_i,a/fc + actj, Ofc+i,... , α ? , . . . , a n ]
= d e t [ a i , . . . ,afc_i,afc,afc+i, · · · , a?·,... , a n ]
+ a d e t [ o i , . . . , a fc _i, α^,α^+ι,.. . , α ? , . . . , a n ]
= det[ai,... ,an].
However, the determinant changes its sign if we interchange columns. To
show this property, note that
d e t [ o i , . . . ,afc_i,afc,afc+i,... , a n ]
=
=
=
=
=
=
det[ai,.. . ,a fc + afc+i,afc+i,... ,o n ]
det[ai,. ..,ak + ak+i,ak+i - (ak + a f c + i ) , . . . , a n ]
det[ai,. . .,a f c + α&+ι, - a * , . . . , a n ]
- d e t [ a i , . . ,a fc + a/ c + i,a f c ,... , a n ]
- ( d e t [ a i , . . . ,<ifc,afc,... ,o n ] + d e t [ a i , . . . , a f c + i , a f c , . . . ,a n ])
- d e t [ a i , . . . , a f c + i, ak,..., a„].
A pth-order minor of an m x n matrix A, with p < min{m, n}, is the
determinant of a p x p matrix obtained from A by deleting m — p rows and
n — p columns. (The notation min{m, n) represents the smaller of m and n.)
We can use minors to investigate the rank of a matrix. In particular, we
have the following proposition.
P r o p o s i t i o n 2.4 If an m x n (m > n) matrix A has a nonzero nth-order
minor, then the columns of A are linearly independent; that is, rank A = n.
D
Proof. Suppose that A has a nonzero nth-order minor. Without loss of generality, we assume that the nth-order minor corresponding to the first n rows
of A is nonzero. Let #;, i = 1 , . . . , n, be scalars such that
χιαι + x2a2 H
l·
xno>n = 0.
16
VECTOR SPACES AND MATRICES
The vector equality above is equivalent to the following set of m equations:
CLiiXi + a\2X2 +
+ «2n^n
021^1 + «22^2 +
an\X\ + an2X2 H
l·
ümlXl + Am2^2 +
annxn
h on
= 0
0.
For i = 1 , . . . , n, let
a>u
di
Then, χχαι + · · · -l· x n ön = 0.
The nth-order minor is det[di, ά 2 , . . . , α η ], assumed to be nonzero. From
the properties of determinants it follows that the columns άι, α<ι,..., an are
linearly independent. Therefore, all X{ = 0, i = 1 , . . . ,n. Hence, the columns
Oi, a 2 , . . . , an are linearly independent.
I
From the above it follows that if there is a nonzero minor, then the columns
associated with this nonzero minor are linearly independent.
If a matrix A has an rth-order minor | M | with the properties (i) \M\ φ 0
and (ii) any minor of A that is formed by adding a row and a column of A
to M is zero, then
rank A = r.
Thus, the rank of a matrix is equal to the highest order of its nonzero minor(s).
A nonsingular (or invertible) matrix is a square matrix whose determinant
is nonzero. Suppose that A is an n x n square matrix. Then, A is nonsingular
if and only if there is another n x n matrix B such that
AB = BA = J n ,
where In denotes the n x n identity matrix:
1 0
0 1
[0
0···
1J
We call the matrix B above the inverse matrix of A, and write B — A~ .
LINEAR EQUATIONS
2.3
17
Linear Equations
Suppose that we are given m equations in n unknowns of the form
CLnXi + ai2#2 H
a 2 lXl + a22#2 H
h CLln^n
l·
= &1,
«2n^n = h .
We can represent the set of equations above as a vector equation
X1O1 + X2«2 + · ' * + X n « n = 6,
where
h
aij
a2j
a
j
,
=
6=
b2
J>m
&mj
Associated with this system of equations is the matrix
A = [αι,θ2,.. · , α η ] ,
and an augmented matrix
[A, 6] = [ α ι , α 2 , . . . , α η , & ] .
We can also represent the system of equations above as
Ax — 6,
where
r
xi
x—
Theorem 2.1 The system of equations Ax — b has a solution if and only if
rank A = rank [A, b].
D
Proof =>: Suppose that the system Ax = b has a solution. Therefore, b is
a linear combination of the columns of A; that is, there exist # 1 , . . . , xn such
18
VECTOR SPACES AND MATRICES
that xia,i+X2a>2-\
and hence
VXnO'n — b. It follows that b belongs to s p a n [ a i , . . . , an]
rank A — dim spanfai,..., an]
= dim s p a n [ a i , . . . , an, b]
= rank [A, 6].
<=: Suppose that rank A = rank [A, 6] = r. Thus, we have r linearly
independent columns of A. Without loss of generality, let o i , a<i,..., ar be
these columns. Therefore, a\, a 2 , . . . , ar are also linearly independent columns
of the matrix [A, b]. Because rank[A, b] = r, the remaining columns of [A, 6]
can be expressed as linear combinations of o i , ei2,..., ar. In particular, b can
be expressed as a linear combination of these columns. Hence, there exist
# 1 , . . . ,xn such that x\a\ 4- #2^2 + l· xn^n — b.
I
Theorem 2.2 Consider the equation Ax — b, where A G ]R m x n and
rank A = m. A solution to Ax — b can be obtained by assigning arbitrary
values for n — m variables and solving for the remaining ones.
Ώ
Proof. We have rank A = m, and therefore we can find m linearly independent
columns of A. Without loss of generality, let αι, a 2 , . . . , a m be such columns.
Rewrite the equation Ax = b as
X\d\
+ #2^2 H
h XmO>m = b — X m + i a m + i — ·
%nQ"n·
Assign to x m + i , £m+2> · · ·, #n arbitrary values, say
%m+l ~ Üm+li
and let
%m+2
==
^ m + 2 i · · · 5 *^n ~ Urn
B = [oi,a2,...,am]GRmxm.
Note that det B φ 0. We can represent the system of equations above as
Xl
X2
— [b — ( i m + i a m + i — · · · — dnan].
The matrix B is invertible, and therefore we can solve for [x\,X2, · · · ,^m] T ·
Specifically,
X2
B
[b — dm_|_iam-j-i — · · · — d n a n ] .
INNER PRODUCTS AND NORMS
2.4
19
Inner Products and Norms
The absolute value of a real number a, denoted |a|, is defined as
| α |
ία
~[-α
if α > 0
ifa<0.
The following formulas hold:
1. |α| = | - α | .
2. -\a\ <a<
\a\.
3. \a + b\ < |a| + |6|.
4. | | a | - | 6 | | < | o - 6 | < | a | + |6|.
5. \ab\ = \a\\b\.
6. \a\ < c and \b\ < d imply that \a + b\ <c + d.
7. The inequality \a\ < b is equivalent to — b < a < b (i.e., a < b and
—a < b). The same holds if we replace every occurrence of "<" by "<."
8. The inequality \a\ > b is equivalent to a > b or —a > b. The same holds
if we replace every occurrence of ">" by ">."
For x, y G R n , we define the Euclidean inner product by
n
(x,y) = ^XiVi
=
xTy.
2=1
The inner product is a real-valued function ( · , · ) : M.n x R n —> R having the
following properties:
1. Positivity: (a?, x) > 0, (x, x) = 0 if and only if x = 0.
2. Symmetry: {x,y) =
(y,x).
3. Additivity: (x + y , z ) = (x, z) + (y, z).
4. Homogeneity: (rx,y) = r(x,y)
for every r G R.
The properties of additivity and homogeneity in the second vector also
hold; that is,
(x,y + z) = (x,y) + (x,z),
(x, ry) = r{x,y)
for every r G R.
20
VECTOR SPACES AND MATRICES
The above can be shown using properties 2 to 4. Indeed,
(sc,l/ + z) = (y + z,x)
= (y,x) + (z,x)
= (x,y) + (x,z)
and
(x, ry) = (ri/, a?) = r(y, a?) = r(x, y).
It is possible to define other real-valued functions on E n x R n that satisfy properties 1 to 4 above (see Exercise 2.8). Many results involving the Euclidean
inner product also hold for these other forms of inner products.
The vectors x and y are said to be orthogonal if (as, y) = 0.
The Euclidean norm of a vector x is defined as
||x|| = y/{x,x) = VxTx.
Theorem 2.3 Cauchy-Schwarz
Inequality.
in M71, the Cauchy-Schwarz inequality
For any two vectors x and y
\(x,y)\<\\x\\\\y\\
holds. Furthermore, equality holds if and only if x = ay for some a G i
□
Proof First assume that x and y are unit vectors; that is, ||x|| = \\y\\ = 1.
Then,
0 < ll^-t/H2 =
(x-y,x-y)
2
= \\x\\ -2(x,y)
=
or
+ \\yf
2-2(x,y)
fay) < i,
with equality holding if and only if x = y.
Next, assuming that neither x nor y is zero (for the inequality obviously
holds if one of them is zero), we replace x and y by the unit vectors #/||χ||
and 2//||ΐ/||. Then, apply property 4 to get
<*,»>< IMIIIi/ll.
Now replace x by — x and again apply property 4 to get
-(x,y)
< \\x\\\\y\\·
The last two inequalities imply the absolute value inequality. Equality holds
if and only if a;/||x|| = ±2//||y||; that is, x = ay for some a G R.
I
INNER PRODUCTS AND NORMS
21
The Euclidean norm of a vector ||x|| has the following properties:
1. Positivity: ||x|| > 0, ||&|| = 0 if and only if x = 0.
2. Homogeneity: ||ra?|| = |r|||ic||, r G R.
3. Triangle inequality: ||ic + 2/|| < ||x|| -f \\y\\.
The triangle inequality can be proved using the Cauchy-Schwarz inequality,
as follows. We have
\\x + y\\2 = \\x\\2 + 2{x,y) + \\y\\2.
By the Cauchy-Schwarz inequality,
\\x + y\\2<\\x\\2
+ 2\\x\\\\y\\ + \\y\\2
= (\\x\\ +
and therefore
\\y\\)2,
H* + 2/11 < 11*11+ llvl|.
Note that if x and y are orthogonal: (x,y) = 0, then
||* + i/||2 = IMI2 + ||y||2,
which is the Pythagorean theorem for R n .
The Euclidean norm is an example of a general vector norm, which is any
function satisfying the three properties of positivity, homogeneity, and triangle
inequality. Other examples of vector norms on R n include the 1-norm, defined
by ||cc||i = \x\\ + · · · + |x n |, and the oo-norm, defined by ||
(where the notation max* represents the largest over all the possible index
values of i). The Euclidean norm is often referred to as the 2-norm, and
denoted ||aj||2· The norms above are special cases of the p-norm, given by
11*11 = ί(Ι^ιΙ ρ + ··· + Ι^Ι ρ ) 1 / ρ
p
1 m a x { | # i | , . . . , \xn\}
ifi<p<oo
iip = oc.
We can use norms to define the notion of a continuous function, as follows.
A function / : R n —» R m is continuous at x if for all ε > 0, there exists
δ > 0 such that \\y - x\\ < δ ^ \\f(y) ~ / ( * ) | | < ε. If the function / is
continuous at every point in R n , we say that it is continuous on R n . Note that
/ = [/i,..., fm]T is continuous if and only if each component /^, i = 1 , . . . , m,
is continuous.
For the complex vector space C n , we define an inner product (x,y) to be
ΣΓ=ι xiVii where the bar denotes complex conjugation. The inner product on
C n is a complex-valued function having the following properties:
1. (cc, a;) > 0, (sc, x) = 0 if and only if x = 0.
22
VECTOR SPACES AND MATRICES
2. (x,y) =
(y,x).
3. (x + y,z) = (x,z) + (y,z).
4. (rx,y)
= r(x,y),
where r G C.
Prom properties 1 to 4, we can deduce other properties, such as
(x, ny + r2z) = ή {x, y) + f 2 (x, *),
where τ*ι,Γ2 G C. For C n , the vector norm can similarly be defined by ||x|| 2 =
(x,x). For more information, consult Gel'fand [47].
EXERCISES
2.1 Let A G R m x n and rank A = m. Show that m < n.
2.2 Prove that the system Ax — 6, A G M m x n , has a unique solution if and
only if rank A = rank [A, b] = n.
2.3 (Adapted from [38].) We know that if k > n + 1, then the vectors αι,α2,...,α& G R n are linearly dependent; that is, there exist scalars
a i , . . . ,c*fc such that at least one α^ φ 0 and Σ ί = ι α * α * = ^- Show that if
k > n + 2, then there exist scalars α ι , . . . , α& such that at least one α* ^ 0,
Σ * = 1 α»ο» = 0, and Σί=ι ai = °·
#ζη£: Introduce the vectors ä; — [1, α ^ ] Τ G M n + 1 , z = 1 , . . . ,fc,and use the
fact that any n + 2 vectors in IR n+1 are linearly dependent.
2.4 Consider a n m x m matrix Ai" that has block form
T» /r
-M- m—k,k
I Mk,k
-Lm—k
Ok,m-kj
where Mk,k is /c x /c, Mm-k,k is (m — k) x k, Im-k is the (m — k) x (m — k)
identity matrix, and Ok,m-k is the k x (m — k) zero matrix.
a. Show that
| d e t M | - |detM f c j f c |.
This result is relevant to the proof of Proposition 19.1.
b . Under certain assumptions, the following stronger result holds:
d e t M = det(-M fc ,fc)
Identify cases where this is true, and show that it is false in general.
EXERCISES
23
2.5 It is well known that for any a, 6, c, d £ C,
a
det
6
ad — be.
Suppose now that A, B, C, and D are real or complex square matrices of
the same size. Give a sufficient condition under which
det
A
C
B
D
AD -
BC.
An interesting discussion on determinants of block matrices is provided in
[121].
2.6 Consider the following system of linear equations:
Xl + X2 + 2^3 + X4 = 1
x\ — 2x2 —
XA
— —2.
Use Theorem 2.1 to check if the system has a solution. Then, use the method
of Theorem 2.2 to find a general solution to the system.
2.7 Prove the seven properties of the absolute value of a real number.
2.8 Consider the function (·, -) 2 : M2 x R 2 -> R, defined by (x, y)2 = 2x\yi +
3^22/1 + 3#i2/2 + 5x22/2, where x = [xi,X2] T and y = [yi,y2]T> Show that
(·, ·)2 satisfies conditions 1 to 4 for inner products.
Note: This is a special case of Exercise 3.21.
2.9 Show that for any two vectors x,y G R n , |||x|| — ||y||| < \\x — y\\.
Hint: Write x = (x — y) + y, and use the triangle inequality. Do the same
for y.
2.10 Use Exercise 2.9 to show that the norm || · || is a uniformly continuous
function] that is, for all ε > 0, there exists δ > 0 such that if \\x — y\\ < (5,
then |||*||-Ill/Ill < ε ·
CHAPTER 3
TRANSFORMATIONS
3.1
Linear Transformations
A function C : R n —» R m is called a linear transformation if:
1. C(ax) = aC(x) for every x G R n and a G R.
2. £ ( x + y) = C(x) + £(y) for every x, y G R n .
If we fix the bases for R n and R m , then the linear transformation C can be
represented by a matrix. Specifically, there exists A G R m x n such that the
following representation holds. Suppose that x G R n is a given vector, and x'
is the representation of x with respect to the given basis for R n . If y = £ ( x ) ,
and y' is the representation of y with respect to the given basis for R m , then
y' = Ax'.
We call A the matrix representation of C with respect to the given bases for
R n and R m . In the special case where we assume the natural bases for R n
and R m , the matrix representation A satisfies
C(x) = Ax.
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
25
26
TRANSFORMATIONS
Let {ei, e 2 , . . . , en} and {e^, e 2 , . . . , e^} be two bases for R n . Define the
matrix
T = [ei, e 2 , . . . , e ^ ] _ 1 [ei, e 2 , . . ·, e n ].
We call T the transformation matrix from {ei, β 2 , . . . , e n } to {e^, e 2 , . . . , e'n}.
It is clear that
[ e i , e 2 , . . . , e n ] = [e'^e^,... ,e^]T;
that is, the ith column of T is the vector of coordinates of e^ with respect to
the basis { e ' ^ e ^ , . . . ,e'n}.
Fix a vector in IRn. Let x be the column of the coordinates of the vector
with respect to { e i , e 2 , . . . , e n } and x' the coordinates of the same vector
with respect to {e^, e 2 , . . . , e'n}. Then, we can show that x' = Tx (see Exercise 3.1).
Consider a linear transformation
C : R n -+ Mn,
and let A be its representation with respect to { e i , e 2 , . . . , e n } and B its
representation with respect to {e^, e 2 , . . . , e'n}. Let y = Ax and y' = Bx'.
Therefore, y' = Ty = TAx - Bx' = BTx, and hence TA = BT, or
A =
TlBT.
Two n x n matrices A and B are similar if there exists a nonsingular
matrix T such that A = T~lBT. In conclusion, similar matrices correspond
to the same linear transformation with respect to different bases.
3.2
Eigenvalues and Eigenvectors
Let A be an n x n real square matrix. A scalar λ (possibly complex) and a
nonzero vector v satisfying the equation Av = Xv are said to be, respectively,
an eigenvalue and an eigenvector of A. For λ to be an eigenvalue it is necessary
and sufficient for the matrix XI — A to be singular; that is, det[AJ — A] = 0 ,
where I is the n x n identity matrix. This leads to an nth-order polynomial
equation
det[AJ - A] = Xn + α η _ ι λ η _ 1 + · · · + axX + a 0 = 0.
We call the polynomial det[A7 — A] the characteristic polynomial of the matrix A, and the equation above the characteristic equation. According to
the fundamental theorem of algebra, the characteristic equation must have
n (possibly nondistinct) roots that are the eigenvalues of A. The following
theorem states that if A has n distinct eigenvalues, then it also has n linearly
independent eigenvectors.
Theorem 3.1 Suppose that the characteristic equation det[AJ — A] = 0 has
n distinct roots λχ, λ 2 ,..., λ η . Then, there exist n linearly independent vectors
V\, V2,..., vn such that
Ανι = XiVi,
i = 1,2, . . . , n .
D
EIGENVALUES AND EIGENVECTORS
27
Proof. Because det[\il — A] = 0, i = l , . . . , n , there exist nonzero Vi,
i = 1 , . . . , n, such that Avi = A ^ , i — 1 , . . . , n. We now prove the linear independence of {vi, v 2 , . . . , vn}. To do this, let c i , . . . , cn be scalars such
that Σ™=ι CiVi — 0. We show that cz- = 0, i = 1 , . . . , n.
Consider the matrix
Z = (X2I - A)(X3I
-A)···
(XnI - A).
We first show that c\ = 0. Note that
Zvn = ( λ 2 Ι - A)(X3I - A) · · · (A n _il - Λ ) ( λ η Ι - A ) u n
= ( λ 2 Ι - Α)(λ 3 7 - A) · · · ( λ η - ΐ / - Α)(ληΤ7η - At>„)
= 0
since Xnvn — A v n = 0.
Repeating the argument above, we get
Zvk = 0, fc = 2 , 3 , . . . , n .
But
Z v i = ( λ 2 Ι - A)(X3I - A) · · · (A n _il - Α ) ( λ η / - Α)νχ
= ( λ 2 / - Α)(λ 3 7 - A) · · · (Ä n _!t;i - Α ν ι ) ( λ η - λχ)
= ( λ 2 / - Α)(Χ3Ι - A)v! · · · ( λ η - ι - λι)(λ η - λι)
= (λ 2 - λι)(λ 3 - λι) · · · (λ η _ι - λι)(λ η - λι)ι>ι.
Using the equation above, we see that
(
n
\
i=l
/
n
i=l
= C\ZV\
= Ci(A 2 -
λι)(λ3 -
λ ι ) · · · ( λ η " λ ι ) « ι = 0.
Because the λζ are distinct, it must follow that C\ = 0.
Using similar arguments, we can show that all c2- must vanish, and therefore
the set of eigenvectors {vi, i> 2 ,..., vn} is linearly independent.
I
Consider a basis formed by a linearly independent set of eigenvectors
{vi, v 2 , . . . , vn}. With respect to this basis, the matrix A is diagonal [i.e., if
dij is the (i, j ) t h element of A, then α^ = 0 for all i φ j]. Indeed, let
T=
[vi,v2,...,vn]
28
TRANSFORMATIONS
Then,
TAT-1=TA[vuv2,...,vn]
= T[Av1,Av2,...,Avn]
= Τ[λινι,λ2ν2,·..,ληυη]
λι
=
T T
θ"
λ2
- i
0
Ι
λη
λι
λ2
L°
because TT1
Xn
= I.
A matrix A is symmetric if A = A T .
Theorem 3.2
J4ZZ
eigenvalues of a real symmetric matrix are real.
Q
Proof Let
where x ^ 0. Taking the inner product of Ax with a? yields
(Αχ,χ)
= (Χχ,χ) =
X(x,x).
On the other hand,
(Aa?,ir} = (a;, A a;) = (χ,Αχ)
= (χ,Χχ) — X{x,x).
The above follows from the definition of the inner product on C n . We note
that (a?, x) is real and (x,x) > 0. Hence,
X(x,x) = X(x,x)
and
Because (x, x) > 0,
Thus, λ is real.
(λ-λ)(ίυ,χ) =0.
λ = λ.
I
Theorem 3.3 Any real symmetric n x n matrix has a set of n eigenvectors
that are mutually orthogonal.
Q
ORTHOGONAL PROJECTIONS
29
Proof. We prove the result for the case when the n eigenvalues are distinct.
For a general proof, see [62, p. 104].
Suppose that Av\ = Ai^i, Av2 = X2v2, where λι φ \2. Then,
(Avi,v2)
= (λινι,ν2) = λι(νι,ν2).
Because A = A ,
(Av1,v2)
= (vuATv2)
= (vuAv2)
=
\2{vi,v2).
Therefore,
Al(Vl,V2> = A 2 ( V 1 , V 2 > .
Because Ai φ \2, it follows that
(vuv2)
= 0.
If A is symmetric, then a set of its eigenvectors forms an orthogonal basis
for R n . If the basis {v\, v2,..., vn} is normalized so that each element has
norm of unity, then defining the matrix
T = [Vi,U2,...,«n],
we have
and hence
TTT
/TIT
= I
rrt—1
A matrix whose transpose is its inverse is said to be an orthogonal matrix.
3.3
Orthogonal Projections
Recall that a subspace V of R n is a subset that is closed under the operations
of vector addition and scalar multiplication. In other words, V is a subspace
of R n if xi,x2
e V => α χ ι + βχ2 e V for all α,/3 G R. Furthermore,
the dimension of a subspace V is equal to the maximum number of linearly
independent vectors in V. If V is a subspace of R n , then the orthogonal
complement of V, denoted V-1", consists of all vectors that are orthogonal to
every vector in V. Thus,
V± = {x: vTx = 0 for all v G V}.
The orthogonal complement of V is also a subspace (see Exercise 3.7). Together, V and V1- span R n in the sense that every vector x G R n can be
represented uniquely as
X = X\ + X 2 ,
30
TRANSFORMATIONS
where X\ G V and x<i G V-1. We call the representation above the orthogonal
decomposition of x (with respect to V). We say that X\ and x<i are orthogonal
projections of x onto the subspaces V and V1-, respectively. We write R n =
V Θ V1- and say that R n is a direct sum of V and V-1. We say that a linear
transformation P is an orthogonal projector onto V if for all x G R n , we have
P x G V and a; - Px G V x .
In the subsequent discussion we use the following notation. Let A G R m x n .
Let the range, or image, of A be denoted
11(A) = {Ax:xe
Rn},
and the nullspace, or kernel, of A be denoted
ΛΓ(Α) = {x G R n : A x = 0}.
Note that 11(A) and N(A)
are subspaces (see Exercise 3.9).
T h e o r e m 3.4 Let A be a given matrix.
λί(Α)±=1Ι(ΑΤ).
Then, IZ(A)1-
= λί(Ατ)
and
D
Proof. Suppose that x G 11(A)1-. Then, yT(ATx)
= (Ay)Tx = 0 for all y,
T
Τ
so that A x = 0. Hence, x G Λ/*(Α ). This implies that 11(A)1- c Λ/*(ΑΤ).
If now x G Λ/*(Α ), then (Ay)Tx
= yT(ATx)
= 0 for all y, so that
τ
x G π ( Α ) \ and consequently, λί(Α ) C ^ ( A ) - 1 . Thus, H(A)^ =
λί(Ατ).
1
The equation Λί(Α) - = 1Z(A ) follows from what we have proved above
and the fact that for any subspace V, we have (V-1)1- = V (see Exercise 3.11).
■
Theorem 3.4 allows us to establish the following necessary and sufficient
condition for orthogonal projectors. For this, note that if P is an orthogonal projector onto V, then Px = x for all x G V, and Ti(P) = V (see
Exercise 3.14).
T h e o r e m 3.5 A matrix P is an orthogonal projector [onto the subspace V =
n{P)] if and only if P2 = P = PT.
D
Proof. =>: Suppose that P is an orthogonal projector onto V = Έ,(Ρ). Then,
11(1 -P)C
H(P)-1. But, H(P)1- = λί(Ρτ)
by Theorem 3.4. Therefore,
11(1 - P) C Af(P').
Hence, PT(I - P)y = 0 for all y, which implies that
P (I — P) = O, where O is the matrix with all entries equal to zero; i.e.,
the zero matrix. Therefore, PT = PTP, and thus P = PT = P2.
<=: Suppose that P2 = P = PT. For any x, we have (Py)T(I - P)x =
T T
y P (I
- P)x - yTP(I - P)x = 0 for all y. Thus, (I - P)x G
ft(P)\
which means that P is an orthogonal projector.
I
QUADRATIC FORMS
3.4
31
Q u a d r a t i c Forms
A quadratic form f : R n
is a function
f(x) = xTQx,
where Q is an n x n real matrix. There is no loss of generality in assuming
Q to be symmetric: Q = QT. For if the matrix Q is not symmetric, we can
always replace it with the symmetric matrix
QO = QO=\(Q
QT)·
+
Note that
xTQx
= xTQ0x
= xT ( -Q + -QT
J x.
A quadratic form xTQx, Q = QT, is said to be positive definite ifxTQx
>
0 for all nonzero vectors x. It is positive semidefinite if xTQx > 0 for all
x. Similarly, we define the quadratic form to be negative definite, or negative
semidefinite, if xTQx < 0 for all nonzero vectors x, or xTQx < 0 for all x,
respectively.
Recall that the minors of a matrix Q are the determinants of the matrices
obtained by successively removing rows and columns from Q. The principal
minors are det Q itself and the determinants of matrices obtained by successively removing an ith row and an ith column. That is, the principal minors
are
det
Qiiii
Qiit2
Qiiip
Qt2ii
Qt2l2
Ql2ip
Qipii
Qipl2
■
, 1 < i\ < - - - < ip < n, p = 1,2,..., n.
*Λ.
The leading principal minors are det Q and the minors obtained by successively removing the last row and the last column. That is, the leading
principal minors are
Δι = g n ,
det
Δ 2 = det
Qll
<7l2
Q21
Q22 Q23
913
Q31
Q32
911
912
921
922
An = detQ.
933
We now prove Sylvester's criterion, which allows us to determine if a
quadratic form xTQx is positive definite using only the leading principal
minors of Q.
32
TRANSFORMATIONS
Theorem 3.6 Sylvester's
Criterion. A quadratic form xTQx, Q = QJ,
is positive definite if and only if the leading principal minors of Q are positive.
D
Proof. The key to the proof of Sylvester's criterion is the fact that a quadratic
form whose leading principal minors are nonzero can be expressed in some
basis as a sum of squares
where x<i are the coordinates of the vector x in the new basis, Δο = 1, and
Δ ι , . . . , Δ η are the leading principal minors of Q.
To this end, consider a quadratic form f(x) = xTQx, where Q = QT. Let
{ei, β2,.. ·, e n } be the natural basis for R n , and let
X = X\e\ + #2^2 + · * ' + Χη&η
be a given vector in R n . Let {vi, t>2> · · · ? vn} be another basis for R n . Then,
the vector x is represented in the new basis as i , where
X — [vi,V2,...,Vn]i = Vx.
Accordingly, the quadratic form can be written as
xTQx
= xTVTQVx
=
xTQx,
where
Qn
Q =
Qln
VTQV
Qnl
Note that q^ = (vi,Qvj).
Our goal is to determine conditions on the new
basis {vi, I>2J . . . , vn} such that q^ = 0 for i φ j .
We seek the new basis in the form
vi = a n e i ,
v2 = α 2 ιβι +α 2 2β2,
vn = θίηχβι + α η 2 β 2 Η
Observe that for j = 1 , . . . , i — 1, if
(vuQej)
=0,
(vuQvj)
=0.
then
l·
anne„.
QUADRATIC FORMS
33
Our goal t h e n is to determine the coefficients an, α ^ , . . . , ^ , i = 1 , . . . , n,
such t h a t t h e vector
Vi = ane\
+ 0^2 H
l·
α ^
satisfies t h e i relations
{vuQej)
= 0,
{euQvi)
= 1.
j = l , . . . , z - 1,
In this case, we get
0
an
0
ttn
For each i = l , . . . , n , the i relations above determine the coefficients
&ii,...,Ciii
in a unique way. Indeed, upon substituting the expression for
Vi into t h e equations above, we obtain t h e set of equations
otnqii + ai2qi2 H
α*ιφ-ι 1 + ^29i-i 2 H
h 0:2291* = 0,
h Oiuqi-i» = 0,
Oil 9a + <*<2φ2 H
h a ^ i = 1.
T h e set of equations above can be expressed in matrix form as
9ll
912
* * * Qli
OL%\
921
922
· · · qi%
OL%2
qn
q%i
—-
&ii
q%\
~°10!
=
1
If t h e leading principal minors of t h e m a t r i x Q do not vanish, t h e n t h e coefficients aij can be obtained using Cramer's rule. In particular,
9n
an = -— det
92-11
qn
Hence,
' 1
Δι
Q
9i 2-1
0
:
0
92-ii-i
92 2-1
0
1
Δι
Δ2
Δη-l
Δη J
34
TRANSFORMATIONS
In the new basis, the quadratic form can be expressed as a sum of squares
xTQx
=
xTQx
Δι
-2 , Δι___
Λ Χ$
ζ + ·
+ —.—χί.
We now show that a necessary and sufficient condition for the quadratic form
to be positive definite is Δ* > 0, i = 1 , . . . , n.
Sufficiency is clear, for if Δ^ > 0, i = 1 , . . . , n, then by the previous argument there is a basis such that
xTQx
= xTQx
> 0
for any x φ 0 (or, equivalently, any x φ 0).
To prove necessity, we first show that for i = 1 , . . . , n, we have Δ* φ 0. To
see this, suppose that Δ& = 0 for some k. Note that Δ& = det Qk,
Qn
Qik
Qki
Qkk
Qk =
fc
Then, there exists a vector v e M , v φ 0, such that vTQk
x e W1 be given by x = [υτ, 0 T ] T . Then,
xTQx
= vTQkv
= 0. Now let
= 0.
But x φ 0, which contradicts the fact that the quadratic form / is positive
definite. Therefore, if xTQx > 0, then Ai φ 0, i — 1 , . . . , n. Then, using our
previous argument, we may write
xTQx
= x Qx = ^~xl~2 + —^Ί
+ ——xi
Δη
where x = [υχ,.. .,νη]χ.
Hence, if the quadratic form is positive definite,
then all leading principal minors must be positive.
I
Note that if Q is not symmetric, Sylvester's criterion cannot be used to
check positive definiteness of the quadratic form x1Qx. To see this, consider
an example where
Γl
Q
°1
-4 1
The leading principal minors of Q are Δι = 1 > 0 and Δ2 = det Q = 1 > 0.
However, if x = [1,1] T , then xTQx = — 2 < 0, and hence the associated
quadratic form is not positive definite. Note that
x1 Qx = x1
1
-4
0
1
1
-4
= x
1
-2
0
1
-2
1
+
X — X
x
L^QX.
MATRIX NORMS
35
The leading principal minors of Q0 are Δι = 1 > 0 and Δ 2 = det Q0 = — 3 <
0, as expected.
A necessary condition for a real quadratic form to be positive semidefinite
is that the leading principal minors be nonnegative. However, this is not
a sufficient condition (see Exercise 3.16). In fact, a real quadratic form is
positive semidefinite if and only if all principal minors are nonnegative (for a
proof of this fact, see [44, p. 307]).
A symmetric matrix Q is said to be positive definite if the quadratic form
xTQx is positive definite. If Q is positive definite, we write Q > 0. Similarly,
we define a symmetric matrix Q to be positive semidefinite (Q > 0), negative
definite (Q < 0), and negative semidefinite (Q < 0) if the corresponding
quadratic forms have the respective properties. The symmetric matrix Q is
indefinite if it is neither positive semidefinite nor negative semidefinite. Note
that the matrix Q is positive definite (semidefinite) if and only if the matrix
—Q is negative definite (semidefinite).
Sylvester's criterion provides a way of checking the definiteness of a
quadratic form, or equivalently, a symmetric matrix. An alternative method
involves checking the eigenvalues of Q, as stated below.
Theorem 3.7 A symmetric matrix Q is positive definite (or positive semidefinite) if and only if all eigenvalues of Q are positive (or nonnegative).
□
Proof For any x, let y = T~lx = TTx, where T is an orthogonal matrix whose columns are eigenvectors of Q. Then, xTQx = yTT QTy =
ΣΓ=ι ^iVi- I^ 0111 this, the result follows.
I
Through diagonalization, we can show that a symmetric positive semidefinite matrix Q has a positive semidefinite (symmetric) square root Q1'2 satisfying Q 1 / 2 Q 1 / / 2 = Q. For this, we use T as above and define
2
A.'
o l
o
\T \
Q1/2 - T
which is easily verified to have the desired properties. Note that the quadratic
form xTQx can be expressed as ||Q1/^2a?||2.
In summary, we have presented two tests for definiteness of quadratic forms
and symmetric matrices. We point out again that nonnegativity of leading principal minors is a necessary but not a sufficient condition for positive
semidefinit eness.
3.5
Matrix Norms
The norm of a matrix may be chosen in a variety of ways. Because the set
of matrices R m X n can be viewed as the real vector space M m n , matrix norms
36
TRANSFORMATIONS
should be no different from regular vector norms. Therefore, we define the
norm of a matrix A, denoted ||A||, to be any function || · || that satisfies the
following conditions:
1. ||A|| > 0 if A φ O, and | | 0 | | = 0, where O is a matrix with all entries
equal to zero.
2. \\cA\\ = |c|||A||, for any c G R.
3. ||Λ + Β | | < | | Α | | + ||Β||.
An example of a matrix norm is the Frobenius norm, defined as
Ki=l
3=1
where A e R m x n . Note that the Frobenius norm is equivalent to the Euclidean norm on R m n .
For our purposes, we consider only matrix norms that satisfy the following
additional condition:
4. ||AB|| < ||Α||||Β||.
It turns out that the Frobenius norm satisfies condition 4 as well.
In many problems, both matrices and vectors appear simultaneously.
Therefore, it is convenient to construct the norm of a matrix in such a way
that it will be related to vector norms. To this end we consider a special class
of matrix norms, called induced norms. Let || · ||(n) and || · ||(m) be vector
norms on R n and R m , respectively. We say that the matrix norm is induced
by, or is compatible with, the given vector norms if for any matrix A e R m x n
and any vector x G l n , the following inequality is satisfied:
||Ax|| ( m ) < ||A||||x|| ( n ) .
We can define an induced matrix norm as
max
IK») = 1
II A x I(m)5
that is, || A|| is the maximum of the norms of the vectors Ax where the vector
x runs over the set of all vectors with unit norm. When there is no ambiguity,
we omit the subscripts (m) and (n) from || · ||(m) and || · ||( n ).
Because of the continuity of a vector norm (see Exercise 2.10), for each
matrix A the maximum
max \\Ax\\
is attainable; that is, a vector x0 exists such that ||xo|| = 1 and || Acc0|| = ||-A||.
This fact follows from the theorem of Weierstrass (see Theorem 4.2).
MATRIX NORMS
37
The induced norm satisfies conditions 1 to 4 and the compatibility condition, as we prove below.
Proof of Condition 1. Let Αφ O. Then, a vector x, ||x|| = 1, can be found
such that Ax φ 0, and thus ||Ax|| Φ 0. Hence, ||A|| = max^i^x ||Aaj|| φ 0.
If, on the other hand, A — O, then ||A|| = max|| x || = i ||Οχ|| = 0 .
I
Proof of Condition 2.
By definition, ||cA|| = max||a.||=i ||cAx||. Obviously, ||cAaj|| = |c|||Acc||, and therefore \\cA\\ = m a x ^ i ^ i |c|||Ax|| =
|c|max|| x || = i \\Ax\\ = |c|||A||.
■
Proof of Compatibility Condition.
Let y φ 0 be any vector. Then, x =
y/\\y\\ satisfies the condition ||x|| = 1. Consequently, \\Ay\\ = ||Α(||?/||χ)|| =
||y||||Aa;|| < ||y||||A||.
■
Proof of Condition 3. For the matrix A + B, we can find a vector XQ such
that \\A + B | | = ||(A + B)a5 0 || and ||z 0 || = 1. Then, we have
\\A + B\\ = \\(A + B)x0\\
= \\Ax0 + Bx0\\
<\\Ax0\\ + \\Bxo\\
<||A||||* 0 || + ||B||||*o||
= ||A|| + ||B||,
which shows that condition 3 holds.
I
Proof of Condition 4- For the matrix AB, we can find a vector xo such that
Hzoll = 1 and ||ΑΒχ 0 || = \\AB\\. Then, we have
||AB|| = \\ABx0\\
= \\A(Bx0)\\
<\\A\\\\Bx0\\
<||Α||||Β||||*ο||
= ||A||||B||,
which shows that condition 4 holds.
I
Theorem 3.8 Let
ΙΝΙ=ίΣΐ^Π
= ν^>·
The matrix norm induced by this vector norm is
\\A\\ = >/ÄT,
38
TRANSFORMATIONS
where λι is the largest eigenvalue of the matrix A A.
Proof. We have
||Ax|| 2 = (Ax, Ax) = (x, AT
G
Ax).
The matrix A A is symmetric and positive semidefinite. Let λι > λ2 >
• · · > λ η > 0 be its eigenvalues and Xi, X2, ·. ·, xn the orthonormal set of the
eigenvectors corresponding to these eigenvalues. Now, we take an arbitrary
vector x with ||x|| = 1 and represent it as a linear combination of Xi, i =
l,...,n:
X = C\X\ + C2X2 +
Note that
l·
CnXn.
+ cl + --- + c2n = 1.
(x,x)=c\
Furthermore,
\\Ax\\2 =
(x,ATAx)
— (c\X\ H
= \ic\ H
l·
cnxn, ciAiXi H
h cnAnxn)
hAn4
<Ai(c? + -.. + 4 )
For the eigenvector aJi of A T A corresponding to the eigenvalue λι, we have
||Axi|| 2 = (®i, ΑΎ Αχλ) = (χι,λχΧι)
and hence
= λι,
max IIAxII — ν λ ι ·
ll*ll=i
This completes the proof.
I
Using arguments similar to the above, we can deduce the following important inequalities.
Rayleigh's Inequalities. If an n x n matrix P is real symmetric positive
definite, then
Xmin(P)\\x\\2
< XTPX
< Amax(P)||x||2,
where A m i n (P) denotes the smallest eigenvalue of P , and A m a x (P) denotes
the largest eigenvalue of P .
Example 3.1 Consider the matrix
A =
2 1
1 2
MATRIX NORMS
39
and let the norm in R2 be given by
2
11*11 = V*?
+ A-
Then,
ATA
5
4
4
5
and det[A/ 2 - AT A] = λ 2 - 10A + 9 = (λ - 1)(λ - 9). Thus, ||A|| = \/9 = 3.
The eigenvector of A A corresponding to λι = 9 is
xi
V2
Note that \\Axi\\ = \\A\\. Indeed,
||AiCi||
=
2
I"
*1 [ill
72 l l 2j
1i
lil
i II 3
il
7i L3j
= 3.
Because A = AT in this example, we also have ||A|| = maxi<i< n |λ$(Α)|,
where λ ι ( Α ) , . . . , λ η (Α) are the eigenvalues of A (possibly repeated).
I
Warning: In general,
maxi<2<n |^i(-^-)| Φ 11-^11 · Instead, we have ||A|| >
maxi<i< n |λί(Α)|, as illustrated in the following example (see also Exercise 5.2).
Example 3.2 Let
0 1
A =
0 0
then
ATA
=
and
d e t [ A I 2 - A T A ] = det
0
0
λ
0
Note that 0 is the only eigenvalue of A.
\Xi(A)\=0.
0
1
0
= λ(λ-1).
λ-1
Thus, for i = 1,2, ||A|| = 1 >
I
40
TRANSFORMATIONS
For a more complete but still basic treatment of topics in linear algebra as
discussed in this and the preceding chapter, see [47], [66], [95], [126]. For a
treatment of matrices, we refer the reader to [44], [62]. Numerical aspects of
matrix computations are discussed in [41], [53].
EXERCISES
3.1 Fix a vector in R n . Let x be the column of the coordinates of the vector
with respect to the basis {ei, β 2 , . . . , e n } and x' the coordinates of the same
vector with respect to the basis {e^, e 2 , . . . , e'n}. Show that x' = Tx, where
T is the transformation matrix from {ei, β 2 , . . . , e n } to {e[,e2l...,
e'n}.
3.2 For each of the following cases, find the transformation matrix T from
{ e i , e 2 , e 3 } to {ei,e 7 2 ,e£}:
a. e[ = ei + 3e 2 - 4e 3 , e2 = 2ei - e 2 + 5e 3 , e 3 = 4ei + 5e 2 + 3e 3 .
b . ei = e[ + e'2 + 3e'3, e 2 = 2e[ - e2 + 4e 3 , e 3 = Se^ + 5e 3 .
3.3 Consider two bases of R 3 , {ei,e2,e 3 } and {e[,e2,e3},
where e\ =
2e[ + e2 — e 3 , e 2 = 2ei — e 2 H- 2β3, and e 3 = 3ei + e'3. Suppose that a linear
transformation has a matrix representation in {ei, e2, e 3 } of the form
2 - 1 0
0 1
-1
0 0
1
Find the matrix representation of this linear transformation in the basis
\ e l > e 2> e 3 J ·
3.4 Consider two bases of R 4 , {ei,e2,e 3 ,e4} and {e[,e2,e,3,e,4},
where
e[ = ei, e2 = e\+e2, e'3 = e i + e 2 + e 3 , and e'4 = e\ -he 2 + e 3 + e 4 . Suppose
that a linear transformation has a matrix representation in {ei, e2, e 3 , e±) of
the form
2 0 1 0
-3 2 0 1
0 1 -1 2
1 0 0 3
Find the matrix representation of this linear transformation in the basis
l e l ? e 2> e 3> e 4 J ·
EXERCISES
41
3.5 Consider a linear transformation given by the matrix
- 1 0 0 0
1 1 0
0
2 5 2 1
-1 1 0 3
Find a basis for R 4 with respect to which the matrix representation for the
linear transformation above is diagonal.
3.6 Let λ ι , . . . , λ η be the eigenvalues of the matrix A G R n x n . Show that
the eigenvalues of the matrix In — A are 1 — λ χ , . . . , 1 — λ η .
3.7 Let V be a subspace. Show that V1- is also a subspace.
3.8 Find the nullspace of
^4-2
2 1
2 -3
0 "
-1
1
3.9 Let A G R m x n be a matrix. Show that 11(A) is a subspace of R m and
λί(Α) is a subspace of R n .
3.10 Prove that if A and B are two matrices with m rows, and λί(Ατ)
C
λί(Βτ),
then 11(B) c 11(A).
Hint: Use the fact that for any matrix M with m rows, we have dim7£(M") +
dim Af(MT) = m [this is one of the fundamental theorems of linear algebra
(see [126, p. 75])].
3.11 Let V be a subspace. Show that (V^1Hint: Use Exercise 3.10.
= V.
3.12 Let V and W be subspaces. Show that if V C W, then W±
CV±.
3.13 Let V be a subspace of R n . Show that there exist matrices V and U
such that V = 1Z(V) = Af(U).
3.14 Let P be an orthogonal projector onto a subspace V. Show that
a. Px = x for all x G V.
b . 1Z(P) = V.
42
TRANSFORMATIONS
3.15 Is the quadratic form
x
1
1
-fi
x
1
positive definite, positive semidefinite, negative definite, negative semidefinite,
or indefinite?
3.16 Let
A =
2
2
2
2
2
2
2
2
0
Show that although all leading principal minors of A are nonnegative, A is
not positive semidefinite.
3.17 Consider the matrix
"o
Q
1 f
1 0 1
1 1 0
a. Is this matrix positive definite, negative definite, or indefinite?
b . Is this matrix positive definite, negative definite, or indefinite on the
subspace
M = {x : x\ + X2 + X3 = 0} ?
3.18 For each of the following quadratic forms, determine if it is positive
definite, negative definite, positive semidefinite, negative semidefinite, or indefinite.
a. f(xi,X2,X3)
b.
=%2
f(xi,X2,X3)=Xi'\'2xl-XiXs
C. f(xi,X2,X3)
= X1+X3
+ ΊΧ\Χ2 + 2χιΧ3 + 2X2^3
3.19 Find a transformation that brings the following quadratic form into the
diagonal form:
f{x\,X2,xz)
= kx\ +x\ + 9^3 - 4xi#2 - 6x 2 #3 + 12xix 3 .
Hint: Read the proof of Theorem 3.6.
EXERCISES
43
3.20 Consider the quadratic form
f(xi,X2,%3)
= x\ + x\ + $xl + 2ξχιχ 2 - 2χιχ 3 + 4χ 2 ^3·
Find the values of the parameter £ for which this quadratic form is positive
definite.
3.21 Consider the function (-,-) Q : R n x R n -► R, defined by (x,y)Q
=
&TQy, where x,y G R n and Q G R n x n is a symmetric positive definite
matrix. Show that (·, -)Q satisfies conditions 1 to 4 for inner products (see
Section 2.4).
3.22 Consider the vector norm || · ||οο on R n given by |j21?|]oo — max^ \xi\,
where x — [a?i,..., xn]T. Define the norm || - ||oo o n ^ m similarly. Show that
the matrix norm induced by these vector norms is given by
n
\\A\loo =maxY]|aifc|,
k=l
where a^· is the (i,j)th
element of A G R m x n .
3.23 Consider the vector norm || · ||i on R n given by ||ic||i = ΣΓ=ι \χί\·> where
x = [ x i , . . . , x n ] T . Define the norm || · ||i on R m similarly. Show that the
matrix norm induced by these vector norms is given by
771
||A||i =
max^2\aik\,
i=l
where a^· is the (i, j)th element of A G W1
CHAPTER 4
CONCEPTS FROM GEOMETRY
4.1
Line Segments
In the following analysis we concern ourselves only with R n . The elements of
this space are the n-component vectors x = [xi, X2,..., £ η ] Τ ·
The line segment between two points x and y in R n is the set of points on
the straight line joining points x and y (see Figure 4.1). Note that if z lies
on the line segment between x and y, then
z-y
=
a(x-y),
where a is a real number from the interval [0,1]. The equation above can be
rewritten as z = ax + (1 — a)y. Hence, the line segment between x and y
can be represented as
{ax + (l-a)y\ae
[0,1]}.
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
45
46
CONCEPTS FROM GEOMETRY
Figure 4.1
4.2
Line segment.
Hyperplanes and Linear Varieties
Let ui,U2,...,un,v
G R, where at least one of the ui is nonzero. The set of
all points x = [x\, X2, · . . , xn}T that satisfy the linear equation
UiXi + U2X2 H
h ^n^n = V
is called a hyperplane of the space R n . We may describe the hyperplane by
{ x e R n : uTx = v},
where
U= [wi,U2,...,Wn] T .
A hyperplane is not necessarily a subspace of R n since, in general, it does
not contain the origin. For n = 2, the equation of the hyperplane has the
form u\Xi + U2X2 = v, which is the equation of a straight line. Thus, straight
lines are hyperplanes in R 2 . In R 3 (three-dimensional space), hyperplanes are
ordinary planes. By translating a hyperplane so that it contains the origin of
R n , it becomes a subspace of R n (see Figure 4.2). Because the dimension of
this subspace is n — 1, we say that the hyperplane has dimension n — 1.
The hyperplane H = {x : U\X\ H
h unxn = v} divides R n into two halfspaces. One of these half-spaces consists of the points satisfying the inequality
U\Xi + U2X2 H
+ unxn > v, denoted
H+ = {x e R n : uTx
> v},
where, as before,
U=
[ui,U2,...,Un]T.
The other half-space consists of the points satisfying the inequality UiXi +
U2X2 H
h unxn < v, denoted
H- = {x e R n : uTx
< v}.
The half-space H+ is called the positive half-space, and the half-space if_ is
called the negative half-space.
HYPERPLANES AND LINEAR VARIETIES
Figure 4.2
47
Translation of a hyperplane.
Let a = [ai, a2,..., an]T be an arbitrary point of the hyperplane H. Thus,
u a — v = 0. We can write
T
uTx
— v = uTx — v — (uTa — v)
= uT(x
— a)
= wi(»i - «l) + u2(x2 - a 2 ) H
l·
un(xn - an) — 0.
The numbers (xi — α^), i = 1 , . . . , n, are the components of the vector x — a.
Therefore, the hyperplane H consists of the points x for which (u, x — a) = 0.
In other words, the hyperplane H consists of the points x for which the
vectors u and x — a are orthogonal (see Figure 4.3). We call the vector u the
normal to the hyperplane H. The set H+ consists of those points x for which
(u, x — a) > 0, and H- consists of those points x for which (u,x — a) < 0.
A linear variety is a set of the form
{x e R n : Ax = 6}
for some matrix A G R m x n and vector b G M m . If dimAf(A) = r, we say
that the linear variety has dimension r. A linear variety is a subspace if and
only if b = 0. If A = O, the linear variety is R n . If the dimension of the
linear variety is less than n, then it is the intersection of a finite number of
hyperplanes.
48
CONCEPTS FROM GEOMETRY
Figure 4.3
4.3
The hyperplane H = {x G Rn : uT(x - a) = 0}.
Convex Sets
Recall that the line segment between two points u,v G R n is the set {w G
R n : w = au + (1 — a)v,a G [0,1]}. A point w = au + (1 — a)v (where
a G [0,1]) is called a convex combination of the points n and v.
A set Θ C W1 is convex if for all iz, t; G Θ, the line segment between u and
v is in Θ. Figure 4.4 gives examples of convex sets, whereas Figure 4.5 gives
examples of sets that are not convex. Note that Θ is convex if and only if
au + (1 — a)v G Θ for all u, v G Θ and a G (0,1).
Examples of convex sets include the following:
■ The empty set
■ A set consisting of a single point
■ A line or a line segment
Θ
Figure 4.4
Convex sets.
CONVEX SETS
Figure 4.5
49
Sets that are not convex.
A subspace
A hyperplane
A linear variety
A half-space
Theorem 4.1 Convex subsets ofW1 have the following properties:
a. If Θ is a convex set and ß is a real number, then the set
βθ = {χ:χ
=
βν,ν£θ}
is also convex.
b. If ©i and 02 are convex sets, then the set
θ ι + Θ2 = {x : x = Vi +1>2, Vi e θ ι , v2 G 62}
is also convex.
c. The intersection of any collection of convex sets is convex (see Figure 4.6
for an illustration of this result for two sets).
□
Proof.
a. Let βν\,βν<ι G /?©, where Vi,V2 G Θ. Because Θ is convex, we have
OLV\ + (1 — a)v2 G Θ for any a G (0,1). Hence,
αβυι + (1 - a)ßv2 = ß{av1 + (1 - α)ν 2 ) G /?θ,
and thus βθ is convex.
50
CONCEPTS FROM GEOMETRY
Figure 4.6
Intersection of two convex sets.
b . Let V\,v2 G ©i + 02- Then, v± = v[ + v'{, and v2 = v2 -f v2, where
vi,V2 € θ ι , and v",v2 G Θ2. Because θ ι and ©2 are convex, for all
a €(0,1),
051 = av[
and
4- (1 — OL)V2 G θ ι
x2 = av" + (1 - α)«2 £ θ 2 .
By definition of ©i + ©2, X\ + ^2 G ©i + ©2- Now,
a v i + (1 - a;)v2
= «(vi
+ v'/) + (1 - OL)(V'2 -f «2)
= 051 +CC2 G ©1 + ©2-
Hence, ©i + ©2 is convex.
c. Let C be a collection of convex sets. Let #1,052 G f l e e c ® (where
n © e c ® represents the intersection of all elements in C). Then, 05i, 052 G
© for each Θ e C. Because each © G C is convex, α χ ι + (1 — α)θ52 G ©
for all a G (0,1) and each © G C. Thus, αχχ + (1 — OJ)O52 G f l e e c ®* I
A point 05 in a convex set © is said to be an extreme point of © if there are
no two distinct points u and v in © such that 05 = au + (1 — a)v for some
a G (0,1). For example, in Figure 4.4, any point on the boundary of the disk
is an extreme point, the vertex (corner) of the set on the right is an extreme
point, and the endpoint of the half-line is also an extreme point.
4.4
Neighborhoods
A neighborhood of a point 05 G R n is the set
{yeRn:||y-*||<e},
where ε is some positive number. The neighborhood is also called a ball with
radius ε and center 05.
In the plane R 2 , a neighborhood of x = [#i, x2]T consists of all the points
inside a disk centered at 05. In R 3 , a neighborhood of 05 = [x\,X2, #3] T consists
of all the points inside a sphere centered at 05 (see Figure 4.7).
NEIGHBORHOODS
disk
Figure 4.7
51
sphere
Examples of neighborhoods of a point in R2 and R3.
A point x G S is said to be an interior point of the set S if the set S contains
some neighborhood of x; that is, if all points within some neighborhood of x
are also in S (see Figure 4.8). The set of all the interior points of S is called
the interior of S.
A point x is said to be a boundary point of the set S if every neighborhood
of x contains a point in S and a point not in S (see Figure 4.8). Note that
a boundary point of S may or may not be an element of S. The set of all
boundary points of S is called the boundary of S.
A set S is said to be open if it contains a neighborhood of each of its points;
that is, if each of its points is an interior point, or equivalently, if S contains
no boundary points.
A set S is said to be closed if it contains its boundary (see Figure 4.9). We
can show that a set is closed if and only if its complement is open.
A set that is contained in a ball of finite radius is said to be bounded. A
set is compact if it is both closed and bounded. Compact sets are important
in optimization problems for the following reason.
Theorem 4.2 Theorem of Weierstrass.
Let f : Ω —► R be a continuous
function, where Ω C R n is a compact set Then, there exists a point XQ G Ω
Figure 4.8
x is an interior point; y is a boundary point.
52
CONCEPTS FROM GEOMETRY
Si = {[x 1 ,X 2 ] T :1<Xi<2,1<X2<2}
S-j is open
A*2
3+
2
I
1+
0
S2 = {[X1,X 2 ] T -3^X1^4,1<X 2 ^2}
TsV!
H
2
S 2 is closed
1
3
1
1
4
Figure 4.9
5
^
*1
Open and closed sets.
such that f(xo) < f(x) for allx G Ω. In other words, f achieves its minimum
on Ω.
ü
Proof. See [112, p. 89] or [2, p. 154].
4.5
I
Polytopes and Polyhedra
Let Θ be a convex set, and suppose that y is a boundary point of Θ. A
hyperplane passing through y is called a hyperplane of support (or supporting
hyperplane) of the set Θ if the entire set Θ lies completely in one of the two
half-spaces into which this hyperplane divides the space Rn.
Recall that by Theorem 4.1, the intersection of any number of convex sets
is convex. In what follows we are concerned with the intersection of a finite
number of half-spaces. Because every half-space H+ or H- is convex in R n ,
the intersection of any number of half-spaces is a convex set.
A set that can be expressed as the intersection of a finite number of halfspaces is called a convex polytope (see Figure 4.10).
A nonempty bounded polytope is called a polyhedron (see Figure 4.11).
For every convex polyhedron Θ C Mn, there exists a nonnegative integer
k < n such that Θ is contained in a linear variety of dimension k, but is not
Figure 4.10
Polytopes.
EXERCISES
Figure 4.11
53
One-dimensional polyhedron.
entirely contained in any (k — 1)-dimensional linear variety of R n . Furthermore, there exists only one fc-dimensional linear variety containing Θ, called
the carrier of the polyhedron Θ, and k is called the dimension of Θ. For example, a zero-dimensional polyhedron is a point of R n , and its carrier is itself.
A one-dimensional polyhedron is a segment, and its carrier is the straight
line on which it lies. The boundary of any fc-dimensional polyhedron, k > 0,
consists of a finite number of (A: — 1)-dimensional polyhedra. For example,
the boundary of a one-dimensional polyhedron consists of two points that are
the endpoints of the segment.
The (k—l)-dimensional polyhedra forming the boundary of a fc-dimensional
polyhedron are called the faces of the polyhedron. Each of these faces has,
in turn, (k — 2)-dimensional faces. We also consider each of these (k — 2)dimensional faces to be faces of the original fc-dimensional polyhedron. Thus,
every fc-dimensional polyhedron has faces of dimensions k — l,fc — 2 , . . . , 1 , 0 . A
zero-dimensional face of a polyhedron is called a vertex, and a one-dimensional
face is called an edge.
EXERCISES
4.1 Show that a set S C R n is a linear variety if and only if for all x,y G S
and a G R, we have ax + (1 — a)y G S.
4.2 Show that the set {x G R n : ||cc|| < r} is convex, where r > 0 is a given
real number and ||x|| = VxTx is the Euclidean norm of x G R n .
4.3 Show that for any matrix A G MrnXn and vector b G M m , the set (linear
variety) {x G R n : Ax = b} is convex.
4.4 Show that the set {x G R n : x > 0} is convex (where x > 0 means that
every component of x is nonnegative).
CHAPTER 5
ELEMENTS OF CALCULUS
5.1
Sequences and Limits
A sequence of real numbers is a function whose domain is the set of natural
numbers 1,2,..., fc,... and whose range is contained in R. Thus, a sequence
of real numbers can be viewed as a set of numbers {x\, #2, . . . , £ & , . . . } , which
is often also denoted as {xk} (or sometimes as {xk}kLi, to indicate explicitly
the range of values that k can take).
A sequence {xk} is increasing if x\ < X2 < · · · < Xk · · *; that is, Xk < Xk+i
for all k. If Xk < ^fc+i, then we say that the sequence is nondecreasing. Similarly, we can define decreasing and nonincreasing sequences. Nonincreasing
or nondecreasing sequences are called monotone sequences.
A number x* G R is called the limit of the sequence {xk} if for any positive
ε there is a number K (which may depend on e) such that for all k > K,
\xk — x* | < ε; that is, Xk lies between x* — ε and x* + ε for all k > K. In this
case we write
x* = lim Xk
or
fc—»oo
Xk —► x * .
j4n Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
55
56
ELEMENTS OF CALCULUS
A sequence that has a limit is called a convergent sequence.
The notion of a sequence can be extended to sequences with elements in
R n . Specifically, a sequence in R n is a function whose domain is the set
of natural numbers 1,2,...,A:,... and whose range is contained in R n . We
use the notation {χ(χ\χ(2\
...} or {x^} for sequences in R n . For limits of
n
sequences in R , we need to replace absolute values with vector norms. In
other words, x* is the limit of {x^} if for any positive ε there is a number K
(which may depend on e) such that for all k > K, \\x^ —x*\\ < ε. As before,
if a sequence {x^} is convergent, we write x* = lim^oo x^ or x^ —> x*.
Theorem 5.1 A convergent sequence has only one limit.
Q
Proof. We prove this result by contradiction. Suppose that a sequence {x^}
has two different limits, say X\ and x2. Then, we have ||xi — a^21| > 0. Let
e = -||a;i-a:2||.
From the definition of a limit, there exist K\ and K2 such that for k > K\
we have \x^ — x\\\ < ε, and for k > K2 we have \\χ^ — x2\\ < ε. Let K =
max{K1,K2}.
Then, if k > K, we have \\x^ -X\\\ <e and \\x^ -x2\\ < e.
Adding \\x^ - xi|| < ε and \\x^ - x2\\ < ε yields
\\x^
-
Xl\\
+ \\x^
- x2\\
<2ε.
Applying the triangle inequality gives
|| — asi 4- »21| = \\x{k) ~ xi ~ x(k) + 0521|
=
< \\χ^
Therefore,
\\(xW-Xl)-(xW-X2)\\
-
Xl\\
+ \\x<<k) - x2\\.
|| -£Ci 4-X2II = \\xi -«2II < 2 ε .
However, this contradicts the assumption that ||xi — »2II — 2ε, which completes the proof.
I
A sequence {x^} in R n is bounded if there exists a number B > 0 such
that ||a;(fc) || < B for all k = 1,2,....
Theorem 5.2 Every convergent sequence is bounded.
G
Proof. Let {x^} be a convergent sequence with limit x*. Choose ε = 1.
Then, by definition of the limit, there exists a natural number K such that
for all k> K,
\\x^k) - a:*!! < 1 .
SEQUENCES AND LIMITS
57
By the result of Exercise 2.9, we get
\\x{k)\\ - ||**H < \\x{k) - s * | | < 1 for all k > K.
Therefore,
\\x{k)\\ < ||a5*|| + l
for all jfe > K.
Letting
B = max {llas^H, ||*< 2 >||,..., ||*<*>||, | | * · | | + l } ,
we have
B> \\x{k)\\
which means that the sequence {x^}
for all/c,
is bounded.
I
For a sequence {xk} in R, a number B is called an upper bound if Xk < B
for all k = 1,2,
In this case, we say that {xk} is bounded above. Similarly,
B is called a lower bound if x^ > B for all fc = 1,2,
In this case, we
say that {α:^} is bounded below. Clearly, a sequence is bounded if it is both
bounded above and bounded below.
Any sequence {xk} in R that has an upper bound has a least upper bound
(also called the supremum), which is the smallest number B that is an upper
bound of {xk}- Similarly, any sequence {xk} in R that has a lower bound has
a greatest lower bound (also called the infimum). If B is the least upper bound
of the sequence {x^}, then Xk < B for all fc, and for any ε > 0, there exists
a number K such that XK > B — ε. An analogous statement applies to the
greatest lower bound: If B is the greatest lower bound of {x^}? then Xk > B
for all fc, and for any ε > 0, there exists a number K such that XK < B + ε.
Theorem 5.3 Every monotone bounded sequence in R is convergent
□
Proof. We prove the theorem for nondecreasing sequences. The proof for
nonincreasing sequences is analogous.
Let {xk} be a bounded nondecreasing sequence in R and x* the least upper
bound. Fix a number ε > 0. Then, there exists a number K such that
XK > x* — ε. Because {xk} is nondecreasing, for any k > K,
Xk >
XK
> x* — ε.
Also, because x* is an upper bound of {xk}, we have
Xk < X* < X* + £·
Therefore, for any k > K,
\xk - x*\ < e,
which means that #& —* x*.
I
58
ELEMENTS OF CALCULUS
Suppose that we are given a sequence {x^}
of natural numbers {ra^}. The sequence
and an increasing sequence
{a5<mfc>} = { * < m i \ x < m a \ . . . }
is called a subsequence of the sequence {x^}.
A subsequence of a given
sequence can thus be obtained by neglecting some elements of the given sequence.
Theorem 5.4 Consider a convergent sequence {x^}
any subsequence of {x^} also converges to x*.
with limit x*.
Then,
□
Proof. Let {x^™^} be a subsequence of {x^}, where {rrik} is an increasing
sequence of natural numbers. Observe that rrik > k for all k = 1,2,
To
show this, first note that m\>\ because πΐ\ is a natural number. Next, we
proceed by induction by assuming that mk > k. Then, we have rafc+i > rrik >
fc, which implies that rrik+i > k + 1. Therefore, we have shown that rrik > k
for all k = 1,2,....
Let ε > 0 be given. Then, by definition of the limit, there exists K such that
||x(fc)— x*|| < ε for any k > K. Because rrik > A:, we also have ||x^mfc^— x*|| < ε
for any k > K. This means that
lim x (mfc) =x*.
k-+oc
It turns out that any bounded sequence contains a convergent subsequence.
This result is called the Bolzano-Weierstrass theorem (see [2, p. 70]).
Consider a function / : W1 —> Rm and a point Xo € l n . Suppose that
there exists / * such that for any convergent sequence {x^} with limit Xo,
we have
lim /(«<*>) = / * .
k—+oo
Then, we use the notation
lim
X—>Xo
f(x)
to represent the limit / * .
It turns out that / is continuous at Xo if and only if for any convergent
sequence {x^} with limit x0> we have
lim /(*<*>) = / ( lim x&A =
fc—>oo
yfc—>oo
J
f(x0)
(see [2, p. 137]). Therefore, using the notation introduced above, the function
/ is continuous at XQ if and only if
lim f(x) =
X—*XQ
f(x0).
SEQUENCES AND LIMITS
59
We end this section with some results involving sequences and limits of
matrices. These results are useful in the analysis of algorithms (e.g., Newton's
algorithm in Chapter 9).
We say that a sequence {A^} o f m x n matrices converges to the m x n
matrix A if
lim | | A - Ak\\ = 0 .
/c—»oo
L e m m a 5.1 Let A e
values of A satisfy \\i(A)\
l
. Then, Hindoo A
< 1, i — 1 , . . . , n.
= O if and only if the eigenD
Proof. To prove this theorem, we use the Jordan form (see, e.g., [47]). Specifically, it is well known that any square matrix is similar to the Jordan form:
There exists a nonsingular T such that
TAT1
= diag [ J m i ( λ ι ) , . . . , J m s (λι), J n i (λ 2 ), ...,Jtu (λ ς )] = J ,
where J r (A) is the r x r matrix:
"λ
1
0
λ
Jr(A) 0
The λ ι , . . . , Xq above are distinct eigenvalues of A, the multiplicity of λι is
mi H
+ m s , and so on.
We may rewrite the above as A = T~XJT. To complete the proof, observe
that
xk
Jfe-lJ
λ*-ι
\k
(jr(\))k
Xk
0
where
k\
i\(k-i)\
Furthermore,
Ak =
T~lJkT.
Hence,
lim Ak = T l
/c—»oo
( lim Jk ) T = O
k—+oo
60
ELEMENTS OF CALCULUS
if and only if |λ;| < 1, i — 1 , . . . , n.
I
Lemma 5.2 The series of n x n matrices
In + A + A 2 + · · · + Ak + · · ·
converges if and only if Hm^oo A
equals (In — A ) - 1 .
= O. In this case the sum of the series
D
Proof. The necessity of the condition is obvious.
To prove the sufficiency, suppose that lim^oo A = O. By Lemma 5.1 we
deduce that |λ;(Α)| < 1, i = 1 , . . . , n. This implies that det(i" n — A) φ 0, and
hence ( J n — A ) - 1 exists. Consider now the following relation:
(In + A + A 2 + · · · + Ak)(In
- A) = In - A* + 1 .
Postmultiplying the equation above by ( J n — A ) - 1 yields
In + A + A 2 + . . . + Ak = ( I n - A ) " 1 - A f c + 1 ( / n - A ) " 1 .
Hence,
k
lim Γ Α ' ^ / . - Α ) - 1 ,
J=0
because lim^oo A
+1
= O. Thus,
oo
ΣΑΐ = (Ιη-Α)-\
3=0
which completes the proof.
if
A matrix-valued function A : R r —► R n x n is continuous at a point £ 0 G
lim
||Α(£)-Α(ξο)||=0.
Lemma 5.3 Let A : R r —> R n X n be an n x n matrix-valued function that
is continuous at ξ0. J / A ( £ 0 ) _ 1 exists, then A ( £ ) _ 1 exists for ξ sufficiently
close to £ 0 and A ( · ) - 1 is continuous at £ 0 .
□
Proof. We follow [114]. We first prove the existence of A ( £ ) _ 1 for all ξ sufficiently close to £o· We have
•A(€) = Mio) - Mio) + Mi) = ^(€o)(Jn - *(€)),
where
Κ{ζ) =
Α{ζ0)-ΗΑ{ζο)-Α{ζ)).
SEQUENCES AND LIMITS
Thus,
ΙΙ^(€)ΙΙ<Ι|Α(€ο)-ΊΐΙΙ^«ο)-Α(€)||
and
lim
Because A is continuous at £0> f° r
\\Κ(ξ)\\ = 0.
au
£ close enough to £ 0 , we have
where Θ € (0,1). Then,
||*(€)||<0<ι
and
(In-K«-))-1
exists. But then
Α ( ξ ) - 1 = (Α(ξ0)(Ιη - A · « ) ) ) " 1 = (/„ Κ(ζ))-ιΑ{ξο)-\
-1
which means that A ( £ ) exists for £ sufficiently close to £ 0 .
To prove the continuity of A ( · ) - 1 note that
ΙΙΑ(ξο)-1 - Α(ξ)-ι\\ = WAV-)"1 - Aß,)"1»
= ||((7η-ΑΓ(ί))-1-Ιη)Α(€0)-1||.
However, since ||ϋΓ(ξ)|| < 1, it follows from Lemma 5.2 that
( i n - i f ( ξ ) ) - 1 - / „ = κ(ξ)
+ κ2(ζ)
+ · · · = «■({)(/„ + Ä - ( 0 + · · · ) ·
Hence,
| | ( I n - A - « ) ) " 1 - 1 „ | | < ||Jr(i)||(l + ||Ä-(€)|| + IIÄC(Oll2 + · · ·)
l|Ä-(i)ll
I-||Ä-(OH'
when \\Κ(ζ)\\ < 1. Therefore,
HA«)-1 - Αίίο)-1!! < rqj^Hiill^o)-1!!·
Because
lim
ΙΙ*-*οΙΗο
we obtain
lim
l l s - «oil - > Ό
which completes the proof.
||ÄT(€)||=0,
JA^-A^^O,
61
62
ELEMENTS OF CALCULUS
5.2
Differentiability
Differential calculus is based on the idea of approximating an arbitrary function by an affine function. A function A : R n —► R m is affine if there exists a
linear function £ : R n —► R m and a vector y G R m such that
A{x) = C(x) + y
for every x G R n . Consider a function / : R n -► R m and a point x0 G R n .
We wish to find an affine function A that approximates / near the point XoFirst, it is natural to impose the condition
A{x0) = /(a? 0 ).
Because *A(sc) = £(x) + y, we obtain 1/ = f(xo) — £(#0)· By the linearity of
£,
£(sc) + 1 / = £(x) - £(aj 0 ) H- f(xo) = £ ( « - «0) + /(«o)·
Hence, we may write
A(x) = C(x - x0) + /(»o)·
Next, we require that A(x) approaches f(x)
that is,
iim
x—».χο,ίΕ^Ω
faster than x approaches Xo;
ll/(»)-^)ll=0,
\\x ~
Xo\\
The conditions above on A ensure that A approximates / near Xo in the sense
that the error in the approximation at a given point is "small" compared with
the distance of the point from x$.
In summary, a function / : Ω —► R m , Ω C R n , is said to be differentiable
at Xo £ Ω if there is an affine function that approximates / near XQ; that is,
there exists a linear function £ : R n —> R m such that
lim
x—>χο,χ£Ω
\\f(x)-(C(x-x0)
+ f(x0))\\
= 0
\\X — Xo\\
The linear function £ above is determined uniquely by / and XQ and is called
the derivative of / at x$. The function / is said to be differentiable on Ω if
/ is differentiable at every point of its domain Ω.
In R, an affine function has the form ax + 6, with a, b G R. Hence, a realvalued function f{x) of a real variable x that is differentiable at Xo can be
approximated near x0 by a function
A(x) = ax -\-b.
Because f(xo) — A{x§) = axo + 6, we obtain
A{x) = ax-\-b = a(x - x0) + /(ffo)·
THE DERIVATIVE MATRIX
0 '
63
x0
Figure 5.1
Illustration of the notion of the derivative.
The linear part of A(x), denoted earlier by C(x), is in this case just ax. The
norm of a real number is its absolute value, so by the definition of differentiability we have
\f(x)-(a(x-xo)
lim
+ f(xo))\
= 0
\x -xo\
X—>XQ
which is equivalent to
IimMlM =e .
x—>χο
x — Xo
The number a is commonly denoted f'(xo) and is called the derivative of /
at xo· The affine function A is therefore given by
A(x) = f(x0) + f'(xo)(x
~ xo)-
This affine function is tangent to / at XQ (see Figure 5.1).
5.3
The Derivative Matrix
Any linear transformation from R n to R m , and in particular the derivative C
of / : R n —► R m , can be represented by an m x n matrix. To find the matrix
representation L of the derivative £ of a differentiate function / : R n —► R m ,
we use the natural basis { e i , . . . , en} for R n . Consider the vectors
j = 1, . . . , Π.
Xj — XQ + t€j,
By the definition of the derivative, we have
Hm
t^o
fjxA-jtLej
+ fjxo))
t
= 0
64
forj
ELEMENTS OF CALCULUS
1 , . . . , n. This means that
lim
f{Xj) t
f(x0)
Le^
for j = 1 , . . . , n. But Lej is the jth. column of the matrix L. On the other
hand, the vector Xj differs from XQ only in the jth coordinate, and in that
coordinate the difference is just the number t. Therefore, the left side of the
preceding equation is the partial derivative
3/
dxj
(®o).
Because vector limits are computed by taking the limit of each coordinate
function, it follows that if
fi(x)
/(*)
5
fm(x)_
then
df_
dxj
[Mj(^o)'
\ _
(®o)
;—
5
ίΐ^(*ο)_
and the matrix L has the form
af_ *o)
dxi
[fe(«o) ··· f£(*o)
dx„ (*o)
m^
Ό)
··· !fc(*o)
The matrix L is called the Jacobian matrix, or derivative matrix, of / at Xo>
and is denoted Df(xo).
For convenience, we often refer to Df(x0) simply
as the derivative of / at XQ. We summarize the foregoing discussion in the
following theorem.
Theorem 5.5 If a function f : R n —► R m is differentiable at Xo, then the
derivative of f at XQ is determined uniquely and is represented by the m x n
derivative matrix Df(xo). The best affine approximation to f near XQ is then
given by
A{x) = f(xo) + Df(x0)(x - x 0 ),
in the sense that
f(x)
= A(x) + r(x)
THE DERIVATIVE MATRIX
65
and Ηηΐφ-^ο ||r(x)||/||x — Xo|| — 0· The columns of the derivative matrix
Df(xo) are vector partial derivatives. The vector
is a tangent vector at XQ to the curve f obtained by varying only the jth
coordinate ofx.
D
If / : R n
is differentiate, then the function V / defined by
£(«)'
V/(x)
Df(x)T
&(*>.
is called the gradient of / . The gradient is a function from R n to R n , and
can be pictured as a vector field, by drawing the arrow representing V/(cc) so
that its tail starts at x.
Given / : R n —> R, if V / is differentiate, we say that / is twice differentiate, and we write the derivative of V / as
dx\dx2
d2f
dx2dx\
d2f
dxj
d2f
_ dx\dxn
d2f
dx2dxn
Γ
D2f =
^
d2
l
2
a /
d2f
dxndxi
d2f
dxndx2
.
■
^
~ 1
r e n r p s e n t s t a kinff t h e n a r t i a l d e r i v a t
dxidxj
to Xj first, then with respect to X{.) The matrix D2f(x) is called the Hessian
matrix of / at x, and is often also denoted F(x).
A function / : Ω —* R m , Ω C R n , is said to be continuously differentiate
on Ω if it is differentiate (on Ω), and Df : Ω —> R m x n is continuous; that
is, the components of / have continuous partial derivatives. In this case, we
write / G C1. If the components of / have continuous partial derivatives of
order p, then we write f eCp.
Note that the Hessian matrix of a function / : R n —► R at x is symmetric
if / is twice continuously differentiate at x. This is a well-known result
from calculus called ClairauVs theorem or Schwarz's theorem. However, if the
second partial derivatives of / are not continuous, then there is no guarantee
that the Hessian is symmetric, as shown in the following well-known example.
E x a m p l e 5.1 Consider the function
f{x)
=
{xlx2{x2l-xl)/{x\JtX2)
\0
iftf^O
nx = 0.
66
ELEMENTS OF CALCULUS
Let us compute its Hessian at the point 0 = [0,0] T . We have
F =
d2f
dx\
d2f
dx\X2
d2f
dx2X\
a2/
dx\ J
We now proceed with computing the components of the Hessian and evaluating them at the point [0,0] T one by one. We start with
&£ = _d_ (df\
dx\ dx\ \dx\J '
where
df_(x)
dx\
+ ±χ\χ22)/(χ\
\χ2{χ\-χ\
10
+ χΙ)2
ήχφθ
if x = 0.
Note that
Hence,
Also,
^([0)χ2Γ) = - χ ,
Hence, the mixed partial is
a2/
We next compute
&l
dx\
=
(0) = - 1 .
_d_(df_
dx2 \dx2J
'
where
df ,
v
dx-2{Xl'X2)
0
Note that
if x = 0.
g W ) = o.
DIFFERENTIATION RULES
67
Hence,
§<·>-
Also,
|£([*i,0] T )=*i.
Hence, the mixed partial is
d2f
(0) = 1.
Therefore, the Hessian evaluated at the point 0 is
F(0) =
0
1
-1
0
which is not symmetric.
5.4
Differentiation Rules
We now introduce the chain rule for differentiating the composition
of a function / : R -► R n and a function g : R n -* R.
g(f(t)),
Theorem 5.6 Lei # : V —► R &e differentiable on an open set D c R n , and
Ze£ / : (a, 6) —> P 6e differentiable on (a, 6). Tften, £/ie composite function
h : (a, 6) —> R given ft?/ ft(t) = g(f(t)) is differentiable on (a, 6), and
Λ'(ί) = Dg(f(t))Df(t)
= V5(/(i))T
Proof. By definition,
v y
s—ί
S—t
s-+t
S - t
if the limit exists. By Theorem 5.5 we write
9(f(s))
- g(f(t))
= Dg(f(t))(f(s)
- /(*)) + r(s),
where lims_>t r(s)/(s — t) = 0. Therefore,
s-t
s-t
s-t
68
ELEMENTS OF CALCULUS
Letting s —»t yields
h\t) = YimDg{f{t))f{s)-f{t)
s—>t
+^ -
S —t
S — t
=
Dg(f(t))Df(t).
I
Next, we present the product rule. Let / : R n -► R m and g : R n -> R m
be two differentiable functions. Define the function h : R n —► R by h(x) =
f(x)Tg(x).
Then, /i is also differentiable and
f(x)TDg(x)+g(x)TDf(x).
Dh(x) =
We end this section with a list of some useful formulas from multivariable
calculus. In each case, we compute the derivative with respect to x. Let
A G R m x n be a given matrix and y G R m a given vector. Then,
D(yT Ax) = yT A
D(xTAx)
= xT(A
+ AT)
if m = n.
It follows from the first formula above that if y G R n , then
D(yTx)=yT.
It follows from the second formula above that if Q is a symmetric matrix,
then
D(xTQx) = 2xTQ.
In particular,
5.5
D(xTx)
= 2xT.
Level Sets and Gradients
The level set of a function / : R n —> R at level c is the set of points
S = {x: f(x) = c}.
For / : R 2 —» R, we are usually interested in 5 when it is a curve. For
/ : R 3 —> R, the sets S most often considered are surfaces.
Example 5.2 Consider the following real-valued function on R 2 :
f{x) = 100(x2 - x\f
+ (1 - * i ) 2 ,
x = [xuX2]T·
The function above is called Rosenbrock's function. A plot of the function /
is shown in Figure 5.2. The level sets of / at levels 0.7, 7, 70, 200, and 700 are
depicted in Figure 5.3. These level sets have a particular shape resembling
LEVEL SETS AND GRADIENTS
-1
Figure 5.2
-2
-1.5
Figure 5.3
-1
-2
Graph of Rosenbrock's function.
-0.5
Level sets of Rosenbrock's (banana) function.
69
70
ELEMENTS OF CALCULUS
f(x1}x2)=c
Figure 5.4
Orthogonality of the gradient to the level set.
bananas. For this reason, Rosenbrock's function is also called the banana
function.
I
To say that a point XQ is on the level set S at level c means that f(xo) =
c. Now suppose that there is a curve 7 lying in S and parameterized by a
continuously differentiable function g : R —► R n . Suppose also that g(to) = XQ
and Dg(to) = υ φ 0, so that v is a tangent vector to 7 at Xo (see Figure 5.4).
Applying the chain rule to the function h(t) = f(g(i)) at to gives
ti(t0) = Df{g{t0))Dg{t0)
=
Df(x0)v.
But since 7 lies on 5, we have
h(t) = f(g(t))
= c;
that is, h is constant. Thus, h'(to) — 0 and
Df(x0)v
= Wf(x0)Tv
= 0.
Hence, we have proved, assuming / continuously differentiable, the following
theorem (see Figure 5.4).
Theorem 5.7 The vector Vf(xo) is orthogonal to the tangent vector to an
arbitrary smooth curve passing through XQ on the level set determined by
f(x) = f(x0).
□
LEVEL SETS AND GRADIENTS
71
Af(Xl.X2)
Figure 5.5
Illustration of a path of steepest ascent.
It is natural to say that V/(iCo) is orthogonal or normal to the level set S
corresponding to XQ, and it is also natural to take as the tangent plane (or
line) to S at xo the set of all points x satisfying
Vf(x0)T(x
- xo) = 0
if
V / ( x 0 ) Φ 0.
As we shall see later, Vf(xo) is the direction of maximum rate of increase of
/ at Xo. Because Vf(xo) is orthogonal to the level set through XQ determined
by f(x) = f(xo), we deduce the following fact: The direction of maximum
rate of increase of a real-valued differentiable function at a point is orthogonal
to the level set of the function through that point.
Figure 5.5 illustrates the discussion above for the case / : R 2 —> R. The
curve on the shaded surface in Figure 5.5 running from bottom to top has
the property that its projection onto the (x 1,^2)-plane is always orthogonal
to the level curves and is called a path of steepest ascent because it always
heads in the direction of maximum rate of increase for / .
The graph of / : R n -+ R is the set {[xT,f(x)]T
: x e R n } c R n + 1 . The
notion of the gradient of a function has an alternative useful interpretation
72
ELEMENTS OF CALCULUS
in terms of the tangent hyperplane to its graph. To proceed, let XQ G R n
and ZQ = f(xo)- The point [a?o~,zo]T € R n + 1 is a point on the graph of
/ . If / is differentiable at £, then the graph admits a nonvertical tangent
hyperplane at ξ = [χζ, ZQ) T . The hyperplane through ξ is the set of all points
[ x i , . . . , xn, z]T G R n + 1 satisfying the equation
ui(xi - xoi) H
h un(xn - x0n) + v(z - ZQ) = 0,
where the vector [u\,... ,un,v]T G R n + 1 is normal to the hyperplane. Assuming that this hyperplane is nonvertical (that is, v ^ O ) , let
Thus, we can rewrite the hyperplane equation above as
z = di(xi - x0i) + . . . + dn(xn - xon) + ZQ.
We can think of the right side of the above equation as a function z : R n —> R.
Observe that for the hyperplane to be tangent to the graph of / , the functions
/ and z must have the same partial derivatives at the point XQ. Hence, if /
is differentiable at a?o, its tangent hyperplane can be written in terms of its
gradient, as given by the equation
z - z0 = Df(x0)(x
5.6
x0)TVf(x0).
- x0) = (x-
Taylor Series
The basis for many numerical methods and models for optimization is Taylor's
formula, which is given by Taylor's theorem.
Theorem 5.8 Taylor's Theorem. Assume that a function f : R —► R is m
times continuously differentiable (i.e., f G Cm) on an interval [a, 6]. Denote
h = b — a. Then,
/(*>) = /(«) + £/ ( 1 ) («) + | / ( 2 ) ( a ) + ■ · ■ + J^TJy^^(a)
+ Rm,
(called Taylor's formula) where / W is the ith derivative of f, and
umfi
*" =
withe,e'e (0,1).
n\m—l
(m-l)!
/(TO)(Q +
h) =
°
urn
^! / ( m ) ( a + Θ'Η)>
□
Proof. We have
i C = f(b) - f(a) - £/<*>(„) - */<»>(<,)
^L/(m-i)(„).
TAYLOR SERIES
73
Denote by g<m(x) an auxiliary function obtained from i? m by replacing a by
x. Hence,
9m(x)
b
= f(b) - /(*) -
-^f^(x)
(m-1)!
;
- ^^/(2>(Z)
[ h
Differentiating gm(x) yields
g£Hx) = -f{1)(x) + / ( 1 ) ( x ) - ^1!/ ( 2 ) ( x )
/ ( 3 ) (x) + ■
(6-x)m-2
> ( m _ i)i J
m
L
+ H
__{b-xT^
~
(m-1)!
(m)
7
l j
W
(m
_ 1} , /
W
'
Observe that g m (6) = 0 and (?m(a) = i? m . Applying the mean-value theorem
yields
flro(ft) - f l m ( o ) _ ( l )
= 9£>(α + ΘΙι),
b— a
where Θ £ (0,1). The equation above is equivalent to
Rm
h
(b-a- ΘΚ)m—l
(m-1)!
f{m\a + eh) = -
hm-\l-9) m—l ( m )
- / ( a + 0ft).
(m - 1)!
Hence,
ro(1
flm = / t(m
" ? r 1 / ( m ) ( o + gft).
- 1)!
To derive the formula
i?m
see, e.g., [81] or [83].
=
iL/W(a
+
^ )
!
I
An important property of Taylor's theorem arises from the form of the remainder i2 m . To discuss this property further, we introduce the order symbols,
O and o.
Let g be a real-valued function defined in some neighborhood of 0 G Mn,
with g(x) φ 0 if x φ 0. Let / : Ω -+ R m be defined in a domain Ω C W1 that
includes 0. Then, we write
74
ELEMENTS OF CALCULUS
1. f(x) — 0(g(x)) to mean that the quotient ||/(a:)||/|^(ic)| is bounded near
0; that is, there exist numbers K > 0 and δ > 0 such that if \\x\\ < J,
xen,then\\f(x)\\/\g(x)\<K.
2. f(x)
= o(g(x)) to mean that
lim
χ-+0,χ€Ω
MM
\g(&)\
0.
The symbol 0(g(x)) [read "big-oh of g{x)"] is used to represent a function
that is bounded by a scaled version of g in a neighborhood of 0. Examples of
such a function are:
x = O(x).
x3
=
2x + 3a:4
2
0(x2).
■ cosx = O(l).
■ sin a; =
0(x).
On the other hand, o(g(x)) [read "little-oh of g(x)"] represents
a function that goes to zero "faster" than g(x) in the sense that
1ίπιχ_>ο ll0(fi,(2C))ll/l^(iC)l — 0· Examples of such functions are:
■ x2 = o(x).
x3
2x2 + 3x 4
■ X3 —
m
o(x).
o(x2).
x = o(l).
Note that if f(x) = o(g(x)), then f(x) = 0(g(x)) (but the converse is not
necessarily true). Also, if f(x) = 0(||cc|| p ), then f(x) — ο(\\χ\\ρ~ε) for any
ε>0.
Suppose that / G C m . Recall that the remainder term in Taylor's theorem
has the form
urn
Rm = —rf(m)(a + eh),
ml
where Θ £ (0,1). Substituting this into Taylor's formula, we get
f(b) = f(a)+±fW(a)+^fW(a)+.
· . + ^^/<">-ΐ)(«)+^/(«0( α +βΑ).
TAYLOR SERIES
75
By the continuity of /< m ), we have / ( m ) ( a + 0ft) -> / ( m ) ( a ) as ft ^ 0; that is,
/(™)(a + 6>ft) = /< m )(a) + o(l). Therefore,
um
um
- r / ( m ) ( a + 0ft) - ^ r / ( m ) ( a ) + o(ft m ),
m!
m!
since ftmo(l) = o(ft m ). We may then write Taylor's formula as
/(&) - /(β) + £ / ( 1 ) ( α ) + | - / ( 2 ) ( α ) + · · · + ^ , / ( m ) ( a ) + o(hm)If, in addition, we assume that / G C m + 1 , we may replace the term o(ftm)
above by 0 ( f t m + 1 ) . To see this, we first write Taylor's formula with i? m +i:
f(b) = /(a) + £/(1)(") + |-/ (2) (a) + · · · + ^/ ( m ) (a) + V i >
where
Um+l
with 6»' G (0,1). Because /("»+1) is bounded on [a, 6] (by Theorem 4.2),
i?m+1=0(fcm+1).
Therefore, if / G C m + 1 , we may write Taylor's formula as
h2
h
f(b) = f(a) + £ / ( 1 ) ( a ) + ^/2\a)
hm
+ ■■■ + ^f(m)(a)
+ 0(/*™ +1 ).
We now turn to the Taylor series expansion of a real-valued function / :
R n —> R about the point xo G R n . Suppose that / G C2. Let x and a?0 be
points in R n , and let z(a) = sc0 + <*(# ~~ x o ) / | | # — #o||· Define φ : R —► R by
0(α) = / ( ζ ( α ) ) = f(x0 + a(aj - xo)/\\x ~ »oil).
Using the chain rule, we obtain
<A'(a) = g ( a )
Df(z(a))^~Xo)
\\χ-χο\\
T
T
(x-x0) Df(z(a)) /\\x-x0\\
= Z?/(z(a))ZM«) =
=
76
ELEMENTS OF CALCULUS
and
d
da v£) {a)
;|x-»ol
;(x-x0)TD2f(z(a))T(x-x0)
i|a5-aJo||
(x - x0)' D2f(z(a))(x
- x0),
where we recall that
~dx\
d2f
dx\dx2
d2f
dx2dx\
d2f
~dx\
d2f
_dx\dx n
d2f
dx2dxn
Γ
D2f =
d2
f
a2/
dxndxi
d2f
dxndx2
d2f
dxt
Observe that
/{χ)=φ(\\χ-χο\\)
- Λ(η\ ι I'35 " x*W*'(cΛ
1!
2
I I * -■*oll <kf,((\\ _L
0"(O) + O (||x-xo|| 2 )·
2!
Hence,
f(x) = f(x0) + γ|£>/(χο)(® - xo)
+ - ( » - x0)TD2f(x0)(x
- x0) + o(||a? - x 0 || 2 ).
If we assume that / G C3, we may use the formula for the remainder term A3
to conclude that
f(x) = f(x0) + —Df(x0)(x
- xo)
1
+ 2[(* ~ *o) D2f(x0)(x
- x0) + 0 ( | | * - *ο|Γ).
We end with a statement of the mean value theorem, which is closely related
to Taylor's theorem.
EXERCISES
77
Theorem 5.9 If a function f : R n —► R m is differentiable on an open set
Ω C R n , then for any pair of points x,y G Ω, there exists a matrix M such
that
f(x)-f(y)
=
M(x-y).
D
The mean value theorem follows from Taylor's theorem (for the case where
m = 1) applied to each component of / . It is easy to see that M is a matrix
whose rows are the rows of Df evaluated at points that lie on the line segment
joining x and y (these points may differ from row to row).
For further reading in calculus, consult [13], [81], [83], [115], [120], [134].
A basic treatment of real analysis can be found in [2], [112], whereas a more
advanced treatment is provided in [89], [111]. For stimulating reading on the
"big-oh" notation, see [77, pp. 104-108].
EXERCISES
5.1 Show that a sufficient condition for limfc_>oo Ak = O is \\A\\ < 1.
5.2 Show that for any matrix A G R n X n ,
||A|| > max |λ,(Α)|.
1<2<η
Hint: Use Exercise 5.1.
5.3 Consider the function
fix)
=
(aTx)(bTx),
where a, 6, and x are n-dimensional vectors.
a. Find
Vf(x).
b . Find the Hessian
F(x).
5.4 Define the functions / : R 2 -> R and g : R -> R 2 by f(x) = xf/6 + x | / 4 ,
g(t) = [3t + 5,2i - 6] T . Let F : R -► R be given by F(t) = f(g(t)). Evaluate
^■(t) using the chain rule.
5.5 Consider f{x) = XiX 2 /2, g(s,t) = [4s+3t,2s+t]T.
and §if(g(s,t)) using the chain rule.
Evaluate
5.6 Let x(t) = [el + t3,t2,t + 1] T , t G R, and f(x) = x\x2xl
x = [^i,x 2 ,^3] T G R 3 . Find -^f(x(t)) in terms of t.
-j^f(g(s,t))
+ x\X2 + #3,
78
ELEMENTS OF CALCULUS
5.7 Suppose that f(x) = o(g(x)). Show that for any given ε > 0, there
exists δ > 0 such that if \\x\\ < 5, then ||/(#)|| < ε|^(χ)|.
5.8 Use Exercise 5.7 to show that if functions / : R n -> R and g : R n -► R
satisfy f(x) = —g(x) + o(g(x)) and g{x) > 0 for all x φ 0, then for all £C ^ 0
sufficiently small, we have f(x) < 0.
5.9 Let
fl(xi,X2)
=Xi
-x\,
/ 2 ( x i , x 2 ) = 2xix 2 Sketch the level sets associated with f\{x\,X2) = 12 and / 2 ( # i , x 2 ) = 16 on
the same diagram. Indicate on the diagram the values of x = [xi,# 2 ] T for
which f(x) = [ / i ( x i , x 2 ) , / 2 ( x i , x 2 ) ] T = [12,16] T .
5.10 Write down the Taylor series expansion of the following functions about
the given points Xo. Neglect terms of order three or higher.
a. f(x) = Xle-X*
+x2 + l,x0
= [1,0] T .
b . f(x) =xj + 2x\x\ + x\, x0 = [1,1] T .
c. f(x) = e*1"*2 + e Xl+:E2 +x1+x2
+ l,x0
= [1,0] T .
PART II
UNCONSTRAINED
OPTIMIZATION
CHAPTER 6
BASICS OF SET-CONSTRAINED AND
UNCONSTRAINED OPTIMIZATION
6.1
Introduction
In this chapter we consider the optimization problem
minimize
subject to
f(x)
x G Ω.
The function / : R n —► R that we wish to minimize is a real-valued function
called the objective function or cost function. The vector x is an n-vector of
independent variables: x = [xi, #2, · · ·, #n] T £ R n · The variables X i , . . . , xn
are often referred to as decision variables. The set Ω is a subset of R n called
the constraint set or feasible set.
The optimization problem above can be viewed as a decision problem that
involves finding the "best" vector x of the decision variables over all possible
vectors in Ω. By the "best" vector we mean the one that results in the-smallest
value of the objective function. This vector is called the minimizer of / over
Ω. It is possible that there may be many minimizers. In this case, finding any
of the minimizers will suffice.
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
81
82
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
There are also optimization problems that require maximization of the
objective function, in which case we seek maximizers. Minimizers and maximizers are also called extremizers. Maximization problems, however, can be
represented equivalently in the minimization form above because maximizing
/ is equivalent to minimizing —/. Therefore, we can confine our attention to
minimization problems without loss of generality.
The problem above is a general form of a constrained optimization problem, because the decision variables are constrained to be in the constraint
set Ω. If Ω = R n , then we refer to the problem as an unconstrained optimization problem. In this chapter we discuss basic properties of the general
optimization problem above, which includes the unconstrained case. In the
remaining chapters of this part, we deal with iterative algorithms for solving
unconstrained optimization problems.
The constraint "x G Ω" is called a set constraint Often, the constraint
set Ω takes the form Ω = {x : h(x) = 0, g(x) < 0}, where h and g are
given functions. We refer to such constraints as functional constraints. The
remainder of this chapter deals with general set constraints, including the
special case where Ω = R n . The case where Ω = R n is called the unconstrained
case. In Parts III and IV we consider constrained optimization problems with
functional constraints.
In considering the general optimization problem above, we distinguish between two kinds of minimizers, as specified by the following definitions.
Definition 6.1 Suppose that / : R n —► R is a real-valued function defined
on some set Ω C R n . A point x* G Ω is a local minimizer of / over Ω if there
exists ε > 0 such that f(x) > f(x*) for all x G Ω \ {x*} and \\x — x*\\ < ε.
A point sc* G Ω is a global minimizer of / over Ω if f(x) > f(x*) for all
icefi\{ai*}.
■
If in the definitions above we replace ">" with ">," then we have a strict
local minimizer and a strict global minimizer, respectively. In Figure 6.1, we
illustrate the definitions for n = 1.
If x* is a global minimizer of / over Ω, we write f(x*) = π ύ η ^ Ω / ( # ) and
x* = argmin xG Q f(x). If the minimization is unconstrained, we simply write
x* = argminjp f(x) or x* = arg min/(cc). In other words, given a real-valued
function / , the notation arg min f(x) denotes the argument that minimizes the
function / (a point in the domain of / ) , assuming that such a point is unique
(if there is more than one such point, we pick one arbitrarily). For example, if
/ : R —> R is given by f(x) = (x + l ) 2 + 3, then argmin/(x) = —1. If we write
a r g m i n ^ ^ , then we treat ux G Ω" to be a constraint for the minimization.
For example, for the function / above, argmin a . >0 f(x) = 0.
Strictly speaking, an optimization problem is solved only when a global
minimizer is found. However, global minimizers are, in general, difficult to
find. Therefore, in practice, we often have to be satisfied with finding local
minimizers.
CONDITIONS FOR LOCAL MINIMIZERS
83
Figure 6.1 Examples of minimizers: X\: strict global minimizer; X2'. strict local
minimizer; X3: local (not strict) minimizer.
6.2
Conditions for Local Minimizers
In this section we derive conditions for a point x* to be a local minimizer. We
use derivatives of a function / : R n —► R. Recall that the first-order derivative
of / , denoted Df, is
Df
dj_ df_
df_
dxi' dx2'
' dxn
Note that the gradient V / is just the transpose of £>/; that is, V / = (Df)T.
The second derivative of / : R n —► R (also called the Hessian of / ) is
r
d2f
dx„dx\
£f(*)
F{x) = £>'/(*) =
Example 6.1 Let f(xi,x2)
Df(x) = (Vf(x))T
Sw
a 2 / (x)
dx\dx7
L
(x)
= 5#i + 8x2 + ^1^2 — x\ — 2^2· Then,
df ,
^
( X )
Λ
df . ■
'^
( X )
[5 + X2 — 2xi, 8 + x\ - 4x2]
and
F(x) = D2f(x)
=
«2
1
dX!dx2(X'
ax2öxi(x)
Έχ\(Χ>
-2
1
1
-4
Given an optimization problem with constraint set Ω, a minimizer may lie
either in the interior or on the boundary of Ω. To study the case where it lies
on the boundary, we need the notion of feasible directions.
84
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
ocdi
Figure 6.2
Two-dimensional illustration of feasible directions; d\ is a feasible
direction, d2 is not a feasible direction.
Definition 6.2 A vector d G R n , d ^ 0, is a feasible direction at x G Ω if
there exists ctQ > 0 such that x + ad G Ω for all a G [0, ao].
I
Figure 6.2 illustrates the notion of feasible directions.
Let / : R n —► R be a real-valued function and let d be a feasible direction
at x G Ω. The directional derivative of f in the direction d, denoted df/dd,
is the real-valued function defined by
Άχ)
od
= a->o
lim / ( * +
ad)
a
-/(a;).
If ||d|| = 1, then df/dd is the rate of increase of / at x in the direction d.
To compute the directional derivative above, suppose that x and d are given.
Then, f(x + ad) is a function of a, and
a=0
Applying the chain rule yields
g(.) _!-/(, +a*
a=0
Vf{xYd = <V/(s),<i) = rfTV/(x).
In summary, if d is a unit vector (||d|| = 1), then ( V / ( x ) , d) is the rate of
increase of / at the point x in the direction d.
Example 6.2 Define / :
by f(x) = #i#2#3> and let
T
d =
L2'2'72j
The directional derivative of / in the direction d is
— (x) = V/(a?) T d =
[x2x3,xiX3,XiX2]
1/2
1/2
1/V2
X2^3 + Ζι:τ3 + \/2a;iX2
CONDITIONS FOR LOCAL MINIMIZERS
85
Note that because ||d|| = 1, the above is also the rate of increase of / at x in
the direction d.
I
We are now ready to state and prove the following theorem.
Theorem 6.1 First-Order Necessary Condition (FONC). Let Ω be a
subset ofW1 and f G C1 a real-valued function on Ω. Ifx* is a local minimizer
of f over Ω, then for any feasible direction d at x*, we have
d T V / ( x * ) > 0.
D
Proof. Define
x(a) = x* + ad G Ω.
Note that a?(0) = x*. Define the composite function
φ(α) = f(x(a)).
Then, by Taylor's theorem,
f(x* + ad) - f(x*) = φ{α) - 0(0) = φ'{0)α + o(a) = adTVf(x(0))
+ o(a),
where a > 0 [recall the definition of o(a) ("little-oh of a") in Part I]. Thus,
if φ(α) > 0(0), that is, f(x* + ad) > f(x*) for sufficiently small values of
a > 0 (a?* is a local minimizer), then we have to have d Vf(x*) > 0 (see
Exercise 5.8).
I
Theorem 6.1 is illustrated in Figure 6.3.
An alternative way to express the FONC is
for all feasible directions d. In other words, if x* is a local minimizer, then
the rate of increase of / at x* in any feasible direction d in Ω is nonnegative.
Using directional derivatives, an alternative proof of Theorem 6.1 is as follows.
Suppose that x* is a local minimizer. Then, for any feasible direction d, there
exists ä > 0 such that for all a G (0, ä),
/(«*)</(«*+ad)·
Hence, for all a G (0, ä), we have
/(**+ad)-/(**)
a
Taking the limit as a —> 0, we conclude that
g(x-)>o.
>0.
86
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
Figure 6.3 Illustration of the FONC for a constrained case; X\ does not satisfy the
FONC, whereas x2 satisfies the FONC.
A special case of interest is when x* is an interior point of Ω (see Section 4.4). In this case, any direction is feasible, and we have the following
result.
Corollary 6.1 Interior Case. Let Ω be a subset o / R n and f G C1 a realvalued function on Ω. If x* is a local minimizer of f over Ω and if x* is an
interior point of Ω, then
V/(**) = 0.
D
Proof. Suppose that / has a local minimizer as* that is an interior point of
Ω. Because x* is an interior point of Ω, the set of feasible directions at x* is
the whole of Rn. Thus, for any d G R n , d T V/(cc*) > 0 and - d T V / ( x * ) > 0.
Hence, d T V/(a;*) - 0 for all d G R n , which implies that V/(«*) = 0.
I
Example 6.3 Consider the problem
minimize
subject to
x\ + 0.5x2 + 3#2 + 4.5
£i,#2 > 0.
a. Is the first-order necessary condition (FONC) for a local minimizer satisfied at x = [1,3] T ?
b . Is the FONC for a local minimizer satisfied at x = [0,3] T ?
c. Is the FONC for a local minimizer satisfied at x = [1,0] T ?
CONDITIONS FOR LOCAL MINIMIZERS
87
4
3
X
CM
O
^
1
0
0
1
2
3
X
Figure 6.4
4
1
Level sets of the function in Example 6.3.
d. Is the FONC for a local minimizer satisfied at x = [0,0] T ?
Solution: First, let / : R 2 -► R be defined by f(x) = x\ + 0.5x§ + 3x 2 + 4.5,
where x — \x\, x2]T. A plot of the level sets of / is shown in Figure 6.4.
a. At x = [1,3] T , we have Vf(x) = [2xux2 + 3] T = [2,6] T . The point
x = [1,3] T is an interior point of Ω = {x : x\ > 0,x2 > 0}. Hence, the
FONC requires that Vf(x) = 0. The point x = [1,3] T does not satisfy
the FONC for a local minimizer.
b . At x = [0,3] T , we have V/(a?) = [0,6] T , and hence dTVf(x)
= 6d 2 ,
where d = [di,d2] T . For d to be feasible at as, we need di > 0, and d2
can take an arbitrary value in R. The point x = [0,3] T does not satisfy
the FONC for a minimizer because d2 is allowed to be less than zero. For
example, d = [1, — 1] T is a feasible direction, but d T V / ( x ) = — 6 < 0.
c. At x = [1,0] T , we have V / ( x ) = [2,3] T , and hence dTVf(x)
= 2d1+3d2.
For d to be feasible, we need d2 > 0, and d\ can take an arbitrary
value in R. For example, d = [—5,1]T is a feasible direction. But
dTVf(x)
= -7 < 0. Thus, x = [1,0] T does not satisfy the FONC
for a local minimizer.
d. At x = [0,0] T , we have V / ( x ) = [0,3] T , and hence dTVf{x)
= 3d2. For
d to be feasible, we need d2 > 0 and d\ > 0. Hence, x — [0,0] T satisfies
the FONC for a local minimizer.
|
Example 6.4 Figure 6.5 shows a simplified model of a cellular wireless system (the distances shown have been scaled down to make the calculations
88
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
Primary
Base Station
2
H
H
Neighboring
Base Station
Interference
|
*-| Mobile
x
Simplified cellular wireless system in Example 6.4.
Figure 6.5
simpler). A mobile user (also called a mobile) is located at position x (see
Figure 6.5).
There are two base station antennas, one for the primary base station
and another for the neighboring base station. Both antennas are transmitting
signals to the mobile user, at equal power. However, the power of the received
signal as measured by the mobile is the reciprocal of the squared distance
from the associated antenna (primary or neighboring base station). We are
interested in finding the position of the mobile that maximizes the signal-tointerference ratio, which is the ratio of the signal power received from the
primary base station to the signal power received from the neighboring base
station.
We use the FONC to solve this problem. The squared distance from the
mobile to the primary antenna is 1 + x2, while the squared distance from the
mobile to the neighboring antenna is 1 + (2 — x)2. Therefore, the signal-tointerference ratio is
fix) I[X)
1+
( 2 -*> 2
1+x2
'
We have
_ -2(2-x)(l
J[
+ x2)-2x(l
+
(2-x)2)
~
(1 + * 2 ) 2
_ 4(x2 - 2x - 1)
(1 + x 2 ) 2 '
]
By the FONC, at the optimal position x* we have /'(#*) = 0. Hence, either
x* — 1 — y/2 or x* = 1 + y/2. Evaluating the objective function at these two
candidate points, it easy to see that x* = 1 — y/2 is the optimal position. I
The next example illustrates that in some problems the FONC is not helpful
for eliminating candidate local minimizers. However, in such cases, there may
be a recasting of the problem into an equivalent form that makes the FONC
useful.
CONDITIONS FOR LOCAL MINIMIZERS
89
Example 6.5 Consider the set-constrained problem
minimize
subject to
f(x)
x G Ω,
where Ω = {[xi,#2] T · x\ + %\ = 1}·
a. Consider a point x* G Ω. Specify all feasible directions at x*.
b . Which points in Ω satisfy the FONC for this set-constrained problem?
c. Based on part b, is the FONC for this set-constrained problem useful for
eliminating local-minimizer candidates?
d. Suppose that we use polar coordinates to parameterize points x G Ω in
terms of a single parameter Θ:
Xi=cos0
#2 = sin0.
Now use the FONC for unconstrained problems (with respect to Θ) to
derive a necessary condition of this sort: If x* G Ω is a local minimizer,
then d T V / ( x * ) = 0 for all d satisfying a "certain condition." Specify
what this certain condition is.
Solution:
a. There are no feasible directions at any x*.
b . Because of part a, all points in Ω satisfy the FONC for this setconstrained problem.
c. No, the FONC for this set-constrained problem is not useful for eliminating local-minimizer candidates.
d. Write h{ß) = /(#(#)), where g : R —► R 2 is given by the equations relating
Θ to x = [χι,Χ2] Τ · Note that Dg{9) = [— sin0,cos0] T . Hence, by the
chain rule,
h\ff) = Df{g{e))Dg{9)
=
Dg(e)TVf(g(e)).
Notice that Dg{6) is tangent to Ω at x = g(0). Alternatively, we could
say that Dg(9) is orthogonal to x = g(0).
Suppose that x* G Ω is a local minimizer. Write x* = g{0*). Then
Θ* is an unconstrained minimizer of h. By the FONC for unconstrained
problems, h'(6*) = 0, which implies that d T V / ( x * ) = 0 for all d tangent
to Ω at x* (or, alternatively, for all d orthogonal to x*).
|
We now derive a second-order necessary condition that is satisfied by a
local minimizer.
90
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
Theorem 6.2 Second-Order Necessary Condition (SONC). Let Ω c
R n , f G C2 a function on Ω, x* a local minimizer of f over Ω, and d a feasible
direction at x*. If dTWf(x*) = 0, then
dTF(x*)d
> 0,
Q
where F is the Hessian of f.
Proof We prove the result by contradiction. Suppose that there is a feasible
direction d at x* such that dTVf(x*)
= 0 and dTF(x*)d < 0. Let x{a) =
x* + ad and define the composite function φ(α) = f(x* + ad) = f(x(a)).
Then, by Taylor's theorem,
φ(α) = 0(0) + ^ " ( 0 ) ^ + ο ( α 2 ) ,
where by assumption, <//(0) = d T V / ( x * ) = 0 and φ"{ϋ) = dTF(x*)d
For sufficiently small a,
φ(α)-φ(0)
that is,
=
< 0.
φ"(0)^+ο(α2)<0,
/(x* + a d ) < / ( x * ) ,
which contradicts the assumption that x* is a local minimizer. Thus,
φ"(0) = dTF(x*)d
> 0.
■
Corollary 6.2 Interior Case. Let x* be an interior point o / ! l c l " .
x* is a local minimizer of f : Ω —> ]R, / G C 2 , i/ien
//
V/(**) = 0,
and F(x*) is positive semidefinite (F{x*) > 0); that is, for all d G W1,
dTF(x*)d
> 0.
G
Proof If x* is an interior point, then all directions are feasible. The result
then follows from Corollary 6.1 and Theorem 6.2.
I
In the examples below, we show that the necessary conditions are not
sufficient.
E x a m p l e 6.6 Consider a function of one variable f(x) = x 3 , / : R —► R.
Because /'(0) = 0, and /"(0) = 0, the point x = 0 satisfies both the FONC
and SONC. However, x = 0 is not a minimizer (see Figure 6.6).
I
CONDITIONS FOR LOCAL MINIMIZERS
AW
Figure 6.6
91
' f(x)=x3
The point 0 satisfies the FONC and SONC but is not a minimizer.
Example 6.7 Consider a function / : R 2 —► R, where f(x) = x\ - x\. The
FONC requires that Vf{x) = [2x1,-2x2]T
= 0. Thus, x = [0,0] T satisfies
the FONC. The Hessian matrix of / is
F(x)
2
0
0
-2
The Hessian matrix is indefinite; that is, for some d\ G R 2 we have dx Fd\ > 0
(e.g., di = [1,0] T ) and for some d 2 we have d j F d 2 < 0 (e.g., d 2 = [0,1] T ).
Thus, x = [0,0] T does not satisfy the SONC, and hence it is not a minimizer.
The graph of f(x) — x\
x"o is shown in Figure 6.7.
I
Figure 6.7
Graph of f(x) xl
SONC; this point is not a minimizer.
xl The point 0 satisfies the FONC but not
92
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
We now derive sufficient conditions that imply that x* is a local minimizer.
Theorem 6.3 Second-Order
Sufficient Condition (SOSC),
Interior
Case. Let f E C2 be defined on a region in which x* is an interior point.
Suppose that
1. V/(x*) = 0.
2. F(x*) > 0.
G
Then, x* is a strict local minimizer of f.
Proof. Because / G C 2 , we have F(x*) = FT(as*). Using assumption 2 and
Rayleigh's inequality it follows that if d φ 0, then 0 < A m i n (F(ic*))||d|| 2 <
d F(x*)d. By Taylor's theorem and assumption 1,
/ ( * · + d) - /(**) = \dTF(x*)d
+ o(\\df)
>
Λ
" " ° ^ ( 8 * ) ) μ | | 2 + 0 (!|rf|| 2 ).
Hence, for all d such that ||d|| is sufficiently small,
f{x* + d)>
f(x*),
which completes the proof.
I
Example 6.8 Let f{x) = x\ + x\. We have Vf(x)
only if x = [0,0] T . For all x G R 2 , we have
F(x) =
2
0
0
2
= [2xl,2x2)T
= 0 if and
>0.
The point x = [0,0] T satisfies the FONC, SONC, and SOSC. It is a strict
local minimizer. Actually, x = [0,0] T is a strict global minimizer. Figure 6.8
shows the graph of f(x) = x\ + x\.
I
In this chapter we presented a theoretical basis for the solution of nonlinear unconstrained problems. In the following chapters we are concerned
with iterative methods of solving such problems. Such methods are of great
importance in practice. Indeed, suppose that one is confronted with a highly
nonlinear function of 20 variables. Then, the FONC requires the solution of
20 nonlinear simultaneous equations for 20 variables. These equations, being
nonlinear, will normally have multiple solutions. In addition, we would have
to compute 210 second derivatives (provided that / G C2) to use the SONC
or SOSC. We begin our discussion of iterative methods in the next chapter
with search methods for functions of one variable.
EXERCISES
Figure 6.8
93
Graph of f(x) = x\ + x\.
EXERCISES
6.1 Consider the problem
minimize
subject to
/(x)
x G Ω,
where / G C2. For each of the following specifications for Ω, x*, and / , determine if the given point x* is: (i) definitely a local minimizer; (ii) definitely
not a local minimizer; or (iii) possibly a local minimizer.
a. / : R 2 -» R, Ω = {x = [xi,x 2 ] T : x\ > 1}, x* = [1,2] T , and gradient
V/(x*) = [ l , l ] T .
b . / : R 2 -> R, Ω = {x = [a?i,x 2 ] T : x\ > 1,^2 > 2}, x* = [1,2] T , and
gradient V/(x*) = [l,0] T .
c. / : R 2 -+ R, Ω = {x = [xi,x 2 ] T : »l > 0,x 2 > 0}, x* = [1,2] T , gradient
V/(x*) = [0,0] T , and Hessian F(x*) = I (identity matrix).
d. / : R 2 -► R, Ω = {x = [xi,x 2 ] T : X\ > l,x2 > 2}, x* = [1,2] T , gradient
V/(x*) = [1,0] T , and Hessian
F(x*
1
0
0
-1
6.2 Find minimizers and maximizers of the function
/ ( x i , x 2 ) = -x\ - 4 x i + -x\ - 16x2.
94
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
6.3 Show that if x* is a global minimizer of / over Ω, and #* G Ω' C Ω, then
x* is a global minimizer of / over Ω'.
6.4 Suppose that x* is a local minimizer of / over Ω, and i l c f f . Show
that if x* is an interior point of Ω, then x* is a local minimizer of / over Ω'.
Show that the same conclusion cannot be made if a?* is not an interior point
of Ω.
6.5 Consider the problem of minimizing / : R —> R, / G C3, over the
constraint set Ω. Suppose that 0 is an interior point of Ω.
a. Suppose that 0 is a local minimizer. By the FONC we know that /'(0) =
0 (where / ' is the first derivative of / ) . By the SONC we know that
/"(0) > 0 (where / " is the second derivative of / ) . State and prove a
third-order necessary condition (TONC) involving the third derivative at
o, r(o).
b . Give an example of / such that the FONC, SONC, and TONC (in part
a) hold at the interior point 0, but 0 is not a local minimizer of / over
Ω. (Show that your example is correct.)
c. Suppose that / is a third-order polynomial. If 0 satisfies the FONC,
SONC, and TONC (in part a), then is this sufficient for 0 to be a local
minimizer?
6.6 Consider the problem of minimizing / : R —> R, / G C3, over the
constraint set Ω = [0,1]. Suppose that x* — 0 is a local minimizer.
a. By the FONC we know that /'(O) > 0 (where / ' is the first derivative
of / ) . By the SONC we know that if /'(0) = 0, then /"(0) > 0 (where
/ " is the second derivative of / ) . State and prove a third-order necessary
condition involving the third derivative at 0, /'"(O).
b . Give an example of / such that the FONC, SONC, and TONC (in part
a) hold at the point 0, but 0 is not a local minimizer of / over Ω = [0,1].
6.7 Let / : R n -> R, x0 G R n , and Ω c R n . Show that
x0 + arg m i n / ( x ) = arg min / ( y ) ,
χβΩ
yeQ'
where Ω' = {y : y — XQ G Ω}.
6.8 Consider the following function / : R 2 —> R:
"l 2~
"3"
x + xT
4 7
5
EXERCISES
95
a. Find the gradient and Hessian of / at the point [1,1] T .
b . Find the directional derivative of / a t [1,1] T with respect to a unit vector
in the direction of maximal rate of increase.
c. Find a point that satisfies the FONC (interior case) for / . Does this
point satisfy the SONC (for a minimizer)?
6.9 Consider the following function:
f(x\,X2)
= x\x2 + # 2 χ 1 ·
a. In what direction does the function / decrease most rapidly at the point
χ(°) = [2,1] τ ?
b . What is the rate of increase of / at the point x^
maximum decrease of / ?
c. Find the rate of increase of / at the point x^
in the direction of
in the direction d — [3,4] T .
6.10 Consider the following function / : R 2 -+ R:
"2
-1
5
x + xT
1
3
4
a. Find the directional derivative of / at [0,1] T in the direction [1,0] T .
b . Find all points that satisfy the first-order necessary condition for / .
Does / have a minimizer? If it does, then find all minimizer(s); otherwise,
explain why it does not.
6.11 Consider the problem
minimize
subject to
— x\
|#21 < x\
x\ > 0 ,
where £i,#2 £ ^ ·
a. Does the point [#i,£2] T = 0 satisfy the first-order necessary condition
for a minimizer? That is, if / is the objective function, is it true that
d T V / ( 0 ) > 0 for all feasible directions d at 0?
b . Is the point [#i,£2] T = 0 a local minimizer, a strict local minimizer, a
local maximizer, a strict local maximizer, or none of the above?
96
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
6.12 Consider the problem
minimize
subject to
f(x)
x G Ω,
where / : R 2 —> R is given by f(x) = 5^2 with x = [xi,x 2 ] T ? and Ω = {x =
[xi,x 2 ] T : x\ + X2 > 1}.
a. Does the point x* = [0,1] T satisfy the first-order necessary condition?
b . Does the point x* = [0,1] T satisfy the second-order necessary condition?
c. Is the point x* = [0,1] T a local minimizer?
6.13 Consider the problem
minimize
subject to
f(x)
xGi],
where / : R 2 —> R is given by f(x) = —3x\ with x = [xi, X2]T? a n d Ω = {x =
[xi,X2] T · x\ + x\ < 2}. Answer each of the following questions, showing
complete justification.
a. Does the point x* = [2,0] T satisfy the first-order necessary condition?
b . Does the point x* = [2,0] T satisfy the second-order necessary condition?
c. Is the point x* = [2,0] T a local minimizer?
6.14 Consider the problem
minimize
subject to
f(x)
x G Ω,
where Ω = {x G R 2 : x\ + x\ > 1} and f(x) = x2.
a. Find all point (s) satisfying the FONC.
b . Which of the point(s) in part a satisfy the SONC?
c. Which of the point(s) in part a are local minimizers?
6.15 Consider the problem
minimize
subject to
f(x)
x GΩ
EXERCISES
97
where / : R 2 —> R is given by f(x) = 3x\ with x = [xi,X2]T, and Ω = {x =
[xi,X2] T · X\ + x\ > 2}. Answer each of the following questions, showing
complete justification.
a. Does the point x* = [2,0] T satisfy the first-order necessary condition?
b . Does the point x* = [2,0] T satisfy the second-order necessary condition?
c. Is the point x* = [2,0] T a local minimizer?
Hint: Draw a picture with the constraint set and level sets of / .
6.16 Consider the problem
minimize
subject to
f(x)
x G Ω,
where x = [£ι,£2] Τ , / : R 2 —> R is given by f(x) = 4x 2 — x\, and Ω = {x :
x\ + 2#i - x2 > 0, x\ > 0, x2 > 0}.
a. Does the point x* = 0 = [0,0] T satisfy the first-order necessary condition?
b . Does the point x* = 0 satisfy the second-order necessary condition?
c. Is the point x * = 0 a local minimizer of the given problem?
6.17 Consider the problem
maximize
subject to
f(x)
x G Ω,
where Ω c {x G R 2 : x\ > 0,^2 > 0} and / : Ω —► R is given by
f(x) = log(xi) + log(#2) with x = [xi,x 2 ] T , where "log" represents natural logarithm. Suppose that x* is an optimal solution. Answer each of the
following questions, showing complete justification.
a. Is it possible that x* is an interior point of Ω?
b . At what point(s) (if any) is the second-order necessary condition satisfied?
6.18 Suppose that we are given n real numbers, # i , . . . , xn. Find the number
x G R such that the sum of the squared difference between x and the numbers
above is minimized (assuming that the solution x exists).
6.19 An art collector stands at a distance of x feet from the wall, where a
piece of art (picture) of height a feet is hung, b feet above his eyes, as shown in
98
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
Picture
a
Eye Λ:
Figure 6.9
Art collector's eye position in Exercise 6.19.
ϋϋϋΒέφ
tiil^iiiiii!;!;!!
: · : · : ·
:;:;:;2!:;i;i;:;i!:ii;iii;!;i!i;Mi^fffitfSi;
::::::::: : : : :::::::::::::::::?? [:: t ji^iäU':£::
: · : · * ■ : · : ■ : · : ■
H Sensor
Figure 6.10
Simplified fetal heart monitoring system for Exercise 6.20.
Figure 6.9. Find the distance from the wall for which the angle 0 subtended
by the eye to the picture is maximized.
Hint: (1) Maximizing 0 is equivalent to maximizing tan(0).
(2) If 0 = 02 - 0i, then tan(0) = (tan(0 2 ) - tan(0i))/(l + tan(0 2 ) tan(0i)).
6.20 Figure 6.10 shows a simplified model of a fetal heart monitoring system
(the distances shown have been scaled down to make the calculations simpler).
A heartbeat sensor is located at position x (see Figure 6.10).
The energy of the heartbeat signal measured by the sensor is the reciprocal
of the squared distance from the source (baby's heart or mother's heart).
Find the position of the sensor that maximizes the signal-to-interference ratio,
which is the ratio of the signal energy from the baby's heart to the signal
energy from the mother's heart.
6.21 An amphibian vehicle needs to travel from point A (on land) to point
B (in water), as illustrated in Figure 6.11. The speeds at which the vehicle
travels on land and water are v\ and t>2, respectively.
EXERCISES
Figure 6.11
99
Path of amphibian vehicle in Exercise 6.21.
a. Suppose that the vehicle traverses a path that minimizes the total time
taken to travel from A to B. Use the first-order necessary condition to
show that for the optimal path above, the angles θ\ and θ2 in Figure 6.11
satisfy Snell's law:
sin θι
vi
sin 02
v2'
b . Does the minimizer for the problem in part a satisfy the second-order
sufficient condition?
6.22 Suppose that you have a piece of land to sell and you have two buyers.
If the first buyer receives a fraction x\ of the piece of land, the buyer will pay
you Ό\(χ\) dollars. Similarly, the second buyer will pay you U2{x2) dollars
for a fraction of x2 of the land. Your goal is to sell parts of your land to the
two buyers so that you maximize the total dollars you receive. (Other than
the constraint that you can only sell whatever land you own, there are no
restrictions on how much land you can sell to each buyer.)
a. Formulate the problem as an optimization problem of the kind
maximize
subject to
f(x)
x £Ω
by specifying / and Ω. Draw a picture of the constraint set.
100
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
b . Suppose that Ui(xi) = a ^ , i = 1,2, where a\ and a 2 are given positive
constants such that a\ > a 2 . Find all feasible points that satisfy the
first-order necessary condition, giving full justification.
c. Among those points in the answer of part b, find all that also satisfy the
second-order necessary condition.
6.23 Let / : R 2 -► R be defined by
f(x) = {xi - x2)4 + x\ - x\ - 2xi + 2x2 + 1,
where x = [xi,X2]T. Suppose that we wish to minimize / over R 2 . Find all
points satisfying the FONC. Do these points satisfy the SONC?
6.24 Show that if d is a feasible direction at a point x G Ω, then for all
ß > 0, the vector ßd is also a feasible direction at x.
6.25 Let Ω = {x G R n : Ax = b}. Show that d G R n is a feasible direction
at x G Ω if and only if Ad = 0.
6.26 Let / : R 2 -> R. Consider the problem
minimize
subject to
f(x)
x\,X2 > 0,
where x = [χι,α^]"1". Suppose that V / ( 0 ) Φ 0, and
£<o)so,
-g(o)<o.
Show that 0 cannot be a minimizer for this problem.
6.27 Let c G R n , c φ 0, and consider the problem of minimizing the function
f(x) = cTx over a constraint set Ω C R n . Show that we cannot have a
solution lying in the interior of Ω.
6.28 Consider the problem
maximize
C\X\ + C2X2
subject to
x\ + X2 < 1
x i , x 2 > 0,
where c\ and c2 are constants such that c\ > c2 > 0. This is a linear programming problem (see Part III). Assuming that the problem has an optimal feasible solution, use the first-order necessary condition to show that the unique
optimal feasible solution x* is [1,0] T .
EXERCISES
101
Hint: First show that x* cannot lie in the interior of the constraint set. Then,
show that x* cannot lie on the line segments L\ = {x : x\ = 0,0 < x2 < 1},
L2 = {x : 0 < x\ < 1, x 2 = 0}, L 3 = {x : 0 < X\ < 1, x2 = 1 - xi}.
6.29 Line Fitting. Let [#i,2/i] T ,..., [x n? 2/n] T 5 n > 2, be points on the R 2
plane (each Xi,yi G R). We wish to find the straight line of "best fit" through
these points ("best" in the sense that the average squared error is minimized);
that is, we wish to find a, b G R to minimize
1 n
/ ( a , b) = - ^2 (axi + b - yi)2 .
2=1
a. Let
—
1
n
X = - Vxi,
n f-f
2=1
1
n
2=1
1
n
2=1
1
n
2=1
I
n
χ
XY = n ~Σ
*-^ Μ'
2=1
Show that f(a,b) can be written in the form zTQz — 2 c T z + d, where
z = [a, 6] T , Q = Q T G R 2 ^ x 2 L cGR 2 and d G R, and find expressions for
Q, c, and d in terms of X, Ϋ, X 2 , Y 2 , and 1 7 .
b . Assume that the x z , z = 1 , . . . , n, are not all equal. Find the parameters
a* and b* for the line of best fit in terms of X, Y, X 2 , Y 2 , and XY.
Show that the point [α*, δ*] τ is the only local minimizer of / .
Hint:JÖ-{Xf =
^Yri^i-X?·
c. Show that if a* and 6* are the parameters of the line of best fit, then
Y = a*X + b* (and hence once we have computed a*, we can compute
6* using the formula b* = Y — a*X).
6.30 Suppose that we are given a set of vectors {x^\ . . . , x ^ } , a:W G R n ,
2 = 1 , . . . ,p. Find the vector x G R n such that the average squared distance
(norm) between x and x^\ . . . , χ(ρ\
1
P
PUi
ωιι 2
102
BASICS OF SET-CONSTRAINED AND UNCONSTRAINED OPTIMIZATION
is minimized. Use the SOSC to prove that the vector x found above is a strict
local minimizer. How is x related to the centroid (or center of gravity) of the
given set of points { x ^ \ . . . , x^}?
6.31 Consider a function / : Ω —► R, where Ω C R n is a convex set and
/ eC1. Given x* G Ω, suppose that there exists c > 0 such that d T V / ( x * ) >
c||d|| for all feasible directions d at x*. Show that x* is a strict local minimizer
of / over Ω.
6.32 Prove the following generalization of the second-order sufficient condition:
Theorem: Let Ω be a convex subset of R n , / G C2 a real-valued function on
Ω, and x* a point in Ω. Suppose that there exists c G R, c > 0, such that
for all feasible directions d at x* (d φ 0), the following hold:
1. d T V / ( x * ) > 0.
2. d T F(x*)d > c||d|| 2 .
Then, x* is a strict local minimizer of / .
6.33 Consider the quadratic function / : R n —> R given by
- xT6,
/ ( x ) = -xTQx
where Q = QT > 0. Show that x* minimizes / if and only if x* satisfies the
FONC.
6.34 Consider the linear system Xk+i = Q<Xk + biik+i, k > 0, where X{ G R,
ui G R, and the initial condition is xo = 0. Find the values of the control
inputs u\,..., un to minimize
n
-qxn +
r^uh
2=1
where </, r > 0 are given constants. This can be interpreted as desiring to
make xn as large as possible but at the same time desiring to make the total
input energy Σ™=1 u2 as small as possible. The constants q and r reflect the
relative weights of these two objectives.
CHAPTER 7
ONE-DIMENSIONAL SEARCH METHODS
7.1
Introduction
In this chapter, we are interested in the problem of minimizing an objective function / : K —» R (i.e., a one-dimensional problem). The approach is
to use an iterative search algorithm, also called a line-search method. Onedimensional search methods are of interest for the following reasons. First,
they are special cases of search methods used in multivariable problems. Second, they are used as part of general multivariable algorithms (as described
later in Section 7.8).
In an iterative algorithm, we start with an initial candidate solution x^
and generate a sequence of iterates x^l\x^2\
For each iteration k =
0 , 1 , 2 , . . . , the next point χ^+^ depends on x^ and the objective function
/ . The algorithm may use only the value of / at specific points, or perhaps
its first derivative / ' , or even its second derivative / " . In this chapter, we
study several algorithms:
■ Golden section method (uses only / )
■ Fibonacci method (uses only / )
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
103
104
ONE-DIMENSIONAL SEARCH METHODS
AfM
H
'
a0
'
b0
Figure 7.1
^v
x
Unimodal function.
■ Bisection method (uses only / ' )
■ Secant method (uses only / ' )
■ Newton's method (uses f and / " )
The exposition here is based on [27].
7.2
Golden Section Search
The search methods we discuss in this and the next two sections allow us to
determine the minimizer of an objective function / : R —► R over a closed
interval, say [αο,&ο]· The only property that we assume of the objective
function / is that it is unimodal, which means that / has only one local
minimizer. An example of such a function is depicted in Figure 7.1.
The methods we discuss are based on evaluating the objective function
at different points in the interval [αο,&ο]· We choose these points in such a
way that an approximation to the minimizer of / may be achieved in as few
evaluations as possible. Our goal is to narrow the range progressively until
the minimizer is "boxed in" with sufficient accuracy.
Consider a unimodal function / of one variable and the interval [αο,&ο]·
If we evaluate / at only one intermediate point of the interval, we cannot
narrow the range within which we know the minimizer is located. We have
to evaluate / at two intermediate points, as illustrated in Figure 7.2. We
choose the intermediate points in such a way that the reduction in the range
is symmetric, in the sense that
ai - a0 = b0 -bi
where
= p(b0 - a 0 ),
1
2We then evaluate / at the intermediate points. If f(a\) < /(&i), then the
minimizer must lie in the range [αο,&ι]. If, on the other hand, f(a{) > /(£>i),
then the minimizer is located in the range [01,60] (see Figure 7.3).
P<
GOLDEN SECTION SEARCH
ara0
+
a0
Figure 7.2
a-,
0" b 1
+
b-|
+
b0
Evaluating the objective function at two intermediate points.
a0
Figure 7.3
b
105
x* a^
bA
b0
The case where /(αι) < /(6i); the minimizer x* G [ao,&i].
Starting with the reduced range of uncertainty, we can repeat the process
and similarly find two new points, say Ü2 and 62, using the same value of
p < \ as before. However, we would like to minimize the number of objective function evaluations while reducing the width of the uncertainty interval.
Suppose, for example, that f{a\) < /(6i), as in Figure 7.3. Then, we know
that x* G [αο,&ι]. Because a\ is already in the uncertainty interval and f(a\)
is already known, we can make a\ coincide with 62· Thus, only one new evaluation of / at 02 would be necessary. To find the value of p that results in only
one new evaluation of / , see Figure 7.4. Without loss of generality, imagine
that the original range [ao, bo] is of unit length. Then, to have only one new
evaluation of / it is enough to choose p so that
p(fei - a 0 ) = 6 1 - 6 2 .
Because 61 — ao = 1 — p and 61 — 62 = 1 — 2p, we have
p(l-p)
= l - 2p.
We write the quadratic equation above as
p2 - 3p + 1 = 0.
The solutions are
Pi =
3 + ^5
92
3-\/5
106
ONE-DIMENSIONAL SEARCH METHODS
1-p
!
a0
-
>-.
P
1-2p i
a2
a1=b2
b1
b0
<
> ►
b0-a0=1
Figure 7.4
Finding value of p resulting in only one new evaluation of / .
Because we require that p < ^, we take
p
Observe that
and
that is,
=^ Λ
„ 0.382.
>/5-l
! - P = —ö—
>/5
x/5-1
\/5-l
2
P
1-p
1-p
1
1
Thus, dividing a range in the ratio of p to 1 — p has the effect that the ratio of
the shorter segment to the longer equals the ratio of the longer to the sum of
the two. This rule was referred to by ancient Greek geometers as the golden
section.
Using the golden section rule means that at every stage of the uncertainty
range reduction (except the first), the objective function / need only be
evaluated at one new point. The uncertainty range is reduced by the ratio 1 — p « 0.61803 at every stage. Hence, N steps of reduction using the
golden section method reduces the range by the factor
(1 - p)N « (0.61803) N
Example 7.1 Suppose that we wish to use the golden section search method
to find the value of x that minimizes
f{x) =xA-
14x3 + 60z 2 - 70x
in the interval [0,2] (this function comes from an example in [21]). We wish
to locate this value of x to within a range of 0.3.
GOLDEN SECTION SEARCH
107
After N stages the range [0,2] is reduced by (0.61803)^. So, we choose N
so that
(0.61803)^ < 0.3/2.
Four stages of reduction will do; that is, N = 4.
Iteration 1. We evaluate / at two intermediate points a\ and b\. We have
di = ao H- p(b0 — a 0 ) = 0.7639,
6i = a 0 + (1 - p){bo - a0) = 1.236,
where p = (3 —
Λ/5)/2.
We compute
/(αχ) = -24.36,
f{h) = -18.96.
Thus, / ( a i ) < /(&i), so the uncertainty interval is reduced to
[oo,6i] = [0,1.236].
Iteration 2. We choose 62 to coincide with ai, and so / need only be
evaluated at one new point,
a2 = a0 + p(bx - a0) = 0.4721.
We have
f(a2) = -21.10,
f(b2) = / ( a i ) = -24.36.
Now, /(i>2) < /(02), so the uncertainty interval is reduced to
[α2,6ι] = [0.4721,1.236].
Iteration 3. We set a3 = b2 and compute 63:
63 = a2 + (1 - p)(6i - a 2 ) = 0.9443.
We have
/ ( a 3 ) = /(&2) = "24.36,
/(6 3 ) = -23.59.
So f(bs) > f(as).
Hence, the uncertainty interval is further reduced to
[a2M
= [0.4721,0.9443].
Iteration 4- We set 64 = as and
a4 = a2 + p(bs — a2) = 0.6525.
108
ONE-DIMENSIONAL SEARCH METHODS
We have
/ ( a 4 ) = -23.84,
/ ( M = / ( a 3 ) = -24.36.
Hence, f(a±) > /(fo*). Thus, the value of x that minimizes / is located in the
interval
[04,63] = [0.6525,0.9443].
Note that b3 - a4 = 0.292 < 0.3.
7.3
I
Fibonacci Method
Recall that the golden section method uses the same value of p throughout.
Suppose now that we are allowed to vary the value p from stage to stage, so
that at the fcth stage in the reduction process we use a value ρ&, at the next
stage we use a value pfc+i, and so on.
As in the golden section search, our goal is to select successive values of
Pfc> 0 < pk < 1/2, such that only one new function evaluation is required at
each stage. To derive the strategy for selecting evaluation points, consider
Figure 7.5. From this figure we see that it is sufficient to choose the pk such
that
p fc +i(l - pk) =
l-2pk.
After some manipulations, we obtain
p fc+ i = 1 -
—.
1 - Pk
There are many sequences pi, p2,... that satisfy the law of formation above
and the condition that 0 < pk < 1/2. For example, the sequence pi = p2 =
ps = · · · = (3 — Λ / 5 ) / 2 satisfies the conditions above and gives rise to the
golden section method.
Suppose that we are given a sequence p i , p 2 , . . . satisfying the conditions
above and we use this sequence in our search algorithm. Then, after N iterations of the algorithm, the uncertainty range is reduced by a factor of
(l-ft)(l-/*)···(!-PAT).
Depending on the sequence p i , p 2 , . . . , we get a different reduction factor.
The natural question is as follows: What sequence p i , p 2 , . . . minimizes the
reduction factor above? This problem is a constrained optimization problem
that can be stated formally as
minimize
subject to
(1 - pi)(l - p 2 ) · · · (1 - PN)
pfc+i = 1
0<pk<\,
1 ~ Pk
fc
, k = 1,..., N — 1
= l,...,W.
FIBONACCI METHOD
i
Pk
1-2p w
■
109
Pk
Iteration k
* i
Iteration k+1
J
*k+1
1
Pk+i(1-Pk)
-I
k+1
-►I
"Pk
Selecting evaluation points.
Figure 7.5
Before we give the solution to the optimization problem above, we need to
introduce the Fibonacci sequence Fi, F2, F3,
This sequence is defined as
follows. First, let F_i = 0 and Fo = 1 by convention. Then, for k > 0,
-Ffc+i = Fk + Fk-i.
Some values of elements in the Fibonacci sequence are:
Fi
1
F2
2
F3
3
F4
5
F5
8
F6
13
F7
21
F8
34
It turns out that the solution to the optimization problem above is
FN
FN+I
92 = 1 -
Pk
PN
FN-I
?N
FjV-fc+1
F\
F2'
where the F^ are the elements of the Fibonacci sequence. The resulting algorithm is called the Fibonacci search method. We present a proof for the
optimality of the Fibonacci search method later in this section.
In the Fibonacci search method, the uncertainty range is reduced by the
factor
{1-nXl-&)...(!-pN)
FN+I
FN
F\
F2
Fi
1
FN+I
FN+I
110
ONE-DIMENSIONAL SEARCH METHODS
Because the Fibonacci method uses the optimal values of pi, P 2 , . . . , the reduction factor above is less than that of the golden section method. In other
words, the Fibonacci method is better than the golden section method in that
it gives a smaller final uncertainty range.
We point out that there is an anomaly in the final iteration of the Fibonacci
search method, because
Recall that we need two intermediate points at each stage, one that comes
from a previous iteration and another that is a new evaluation point. However,
with PN = 1/2, the two intermediate points coincide in the middle of the
uncertainty interval, and therefore we cannot further reduce the uncertainty
range. To get around this problem, we perform the new evaluation for the
last iteration using ρχ = 1/2 — ε, where ε is a small number. In other words,
the new evaluation point is just to the left or right of the midpoint of the
uncertainty interval. This modification to the Fibonacci method is, of course,
of no significant practical consequence.
As a result of the modification above, the reduction in the uncertainty
range at the last iteration may be either
or
1 - (pN - ε) = - + ε =
—^—,
depending on which of the two points has the smaller objective function value.
Therefore, in the worst case, the reduction factor in the uncertainty range for
the Fibonacci method is
l + 2g
FN+I
Example 7.2 Consider the function
f(x) = x4- Ux3 + 60x2 - 70x.
Suppose that we wish to use the Fibonacci search method to find the value of
x that minimizes / over the range [0,2], and locate this value of x to within
the range 0.3.
After N steps the range is reduced by (1 + 2g)/F/v+1 in the worst case. We
need to choose N such that
Thus, we need
1 + 2ε final range
0.3
—
< . . . ,
= —z- = 0.15.
.Fjv+i
initial range
2
FN+1
- ΈΪ5"·
If we choose ε < 0.1, then N = 4 will do.
Iteration 1. We start with
F4
5
We then compute
αι= a0+ pi(b0 - a0) = -,
5
h = a0 + (1 - pi)(6o - «o) = τ ,
/ ( d ) = -24.34,
/(fc) = -18.65,
/ ( G l ) < /(6i).
The range is reduced to
[a0M
0,
Iteration 2. We have
3
F3
ß2 = <k> + P2(h - a 0 ) = - ,
,
3
t>2 = O l
=
T,
/ ( a 2 ) = -21.69,
/(&2) = / ( a i ) = -24.34,
/ ( a 2 ) > /(fc),
so the range is reduced to
[ö2,6l] =
1 5
2'4
Iteration 3. We compute
Pi = 2
F3 " 3 '
3
«3 = b2
4'
&3 = «2 + (1 - P3)(&1 - «2) = 1,
f(as) = f{b2) = -24.34,
1-P3
f(h)
= -23,
/ ( a 3 ) < /(fts).
112
ONE-DIMENSIONAL SEARCH METHODS
The range is reduced to
[02,63] =
2'1
Iteration 4- We choose ε = 0.05. We have
F i
1
l
04 = a 2 + {pi - s)(b3 - a 2 ) = 0.725,
,
3
04 = a 3 =
-,
/(04) = -24.27,
/(M = f(a3) = -24.34,
/(a 4 ) > f(b4).
The range is reduced to
[a 4 ,6 3 ] = [0.725,1].
Note that b3 - a4 = 0.275 < 0.3.
I
We now turn to a proof of the optimality of the Fibonacci search method.
Skipping the rest of this section does not affect the continuity of the presentation.
To begin, recall that we wish to prove that the values of pi,P2, · · · ,PN
used in the Fibonacci method, where pk = 1 — F/v-fc+i/F/v-fc+2, solve the
optimization problem
minimize
subject to
(1 - pi)(l - p 2 ) * * · (1 - PN)
pk+i = 1 —
0<pk<\,
fc
Pk
1 - Pk
, k — 1 , . . . , TV — 1
= l,...,JV.
It is easy to check that the values of p\, p2,... above for the Fibonacci search
method satisfy the feasibility conditions in the optimization problem above
(see Exercise 7.4). Recall that the Fibonacci method has an overall reduction
factor of (1 — pi) · ■ · (1 — PN) = I/.FW+1. To prove that the Fibonacci search
method is optimal, we show that for any feasible values of p i , . . . , p^? we have
(1-ΡΙ)···(1-ΡΛΓ)>1/^+Ι.
It is more convenient to work with r^ = 1 — pk rather than p&. The
optimization problem stated in terms of r^ is
minimize
subject to
7*1 · · · r/v
7*fc+i =
Tk
1, fc = 1 , . . . , iV — 1
\ < r f c < l , fc = l , . . . , W .
FIBONACCI METHOD
113
Note that if Τ Ί , Γ 2 , . . . satisfy r^+i — ^— 1, then rk > 1/2 if and only if
rk+i < 1· Also, rk > 1/2 if and only if Tk-i < 2/3 < 1. Therefore, in the
constraints above, we may remove the constraint r^ < 1, because it is implied
implicitly by rk > 1/2 and the other constraints. Therefore, the constraints
above reduce to
nfe+i =
l, fe = i , . . . , J V - i ,
rk
rk > ^ k = l,...,N.
To proceed, we need the following technical lemmas. In the statements of
the lemmas, we assume that 7*1, Γ2,... is a sequence that satisfies
rk+i =
L e m m a 7.1 For
rk
1,
rk > 2>
fc
= l,2,.
k>2,
rk = ~
Fk-2 -
Fk-in
Fk-3 -
Fk_2ri'
D
Proof. We proceed by induction. For A: = 2 we have
1 - n _
ri
r\
F0-
Fin
F_i - F0ri
and hence the lemma holds for k = 2. Suppose now that the lemma holds for
k > 2. We show that it also holds for k + 1. We have
rk+i =
=
1
rk
-F f c -3 + Fk-2ri _ Ffc-2 - F fc _iri
Fk-2 - Fk-in
Fk-2 ~ Fk-in
Ffc-2 + Fk-s ~ (Fk-i + F fc _ 2 )ri
Fk-2 - Fk-in
i V x - Fkn
Fk-2 -
Fk-iri
where we used the formation law for the Fibonacci sequence.
Lemma 7.2 For
k>2,
(-l)fc(Ffc_2-Ffc_1r1)>0.
D
114
ONE-DIMENSIONAL SEARCH METHODS
Proof. We proceed by induction. For k = 2, we have
(-l)2(F0-F1ri) = l - n .
But ri = 1/(1 + r2) < 2/3, and hence 1 — r\ > 0. Therefore, the result holds
for k = 2. Suppose now that the lemma holds for k > 2. We show that it also
holds for k -f 1. We have
( - l ) * + 1 ( ^ - i - Fkn) = (-l)*+V f c + i — ( F f c - i - F f c n).
By Lemma 7.1,
rk+i ■■
Fk-i - Fkn
Ffc_2 - Ffc-in
Substituting for l/r^+i, we obtain
(-l) f c + 1 (F f c _! - F f c n) = r f c + 1 (-l) f c (F f c _ 2 - F ^ n ) > 0,
which completes the proof.
Lemma 7.3 For
k>2,
i\fc+i Fk
( - l ) \&+ι«.
* + 1 r i ^> /(-1)*
Fk+i
D
Proof. Because rk+\ = ^r— 1 and /> > | , we have r^+i < 1. Substituting
for 7>+i from Lemma 7.1, we get
Ffc-i-Ffcn
Ffc_2 - Ffc-in
<]L
Multiplying the numerator and denominator by (—l)k yields
(-l)k+1(Fk-i-Fkn)
(-l)k(Fk.2-Fk.iri)
< 1.
By Lemma 7.2, (—l)k(Fk-2 — ^fc-i^i) > 0, and therefore we can multiply
both sides of the inequality above by (—l)k(Fk-2 — -Ffc-i^i) to obtain
( - ΐ ) * + 1 ( Ρ * _ ! - Fkn)
< (-i)*(F f c _ 2 - Ffc-xn).
Rearranging yields
( - 1 ) * + 1 ( ^ _ χ + Fk)n
> (-l) fe+1 (F fc _2 + F fc _i).
Using the law of formation of the Fibonacci sequence, we get
( - l ) f e + 1 F f c + 1 n > (-l) f e + 1 F f c ,
FIBONACCI METHOD
which upon dividing by Ffc+i on both sides gives the desired result.
115
I
We are now ready to prove the optimality of the Fibonacci search method
and the uniqueness of this optimal solution.
Theorem 7.1 Let Γχ,... ,ΓΝ, N > 2, satisfy the constraints
r-fc+i =
1, k = 1 , . . . , 7 V - 1 ,
g, k =
rk>
l,...,N.
Then,
ri--rN
>
1
FN+I
Furthermore,
1
ri--rN
z/ and only ifrk — Fjsf-k+i/FN-k+2, k = 1 , . . . , N. In other words, the values
of r i , . . . , ΓΑΓ ^sed m £Ae Fibonacci search method form a unique solution to
the optimization problem.
D
Proof. By substituting expressions for η , . . . , r # from Lemma 7.1 and performing the appropriate cancellations, we obtain
ri · · · rN = (-l)N(FN-2
= (-l)NFN-2
- FN-iri)
+
FN^-I^+W
Using Lemma 7.3 yields
ri · · · rN > (-l)NFN-2
— (_1)
FN^(-1)N^^-
+
(^V-2^V+i - FN-iFN)—
.
By Exercise 7.5, it is readily checked that the following identity holds:
{-1)N(FN.2FN+1
- FN^FN)
= 1. Hence,
T\ --rN
From the above we see that
ri--rN
if and only if
.
> —
1
FN+I
FN
FN+I
This is simply the value of r\ for the Fibonacci search method. Note that
fixing ri determines r2,..., r^ uniquely.
I
For further discussion on the Fibonacci search method and its variants, see
[133].
116
7.4
ONE-DIMENSIONAL SEARCH METHODS
Bisection Method
Again we consider finding the minimizer of an objective function / : R —> R
over an interval [αο>&ο]· As before, we assume that the objective function /
is unimodal. Further, suppose that / is continuously differentiate and that
we can use values of the derivative / ' as a basis for reducing the uncertainty
interval.
The bisection method is a simple algorithm for successively reducing the
uncertainty interval based on evaluations of the derivative. To begin, let
χ(°) = (α0 + 6o)/2 be the midpoint of the initial uncertainty interval. Next,
evaluate f'(x^).
If f'(x^)
> 0, then we deduce that the minimizer lies to
the left of χ(°\ In other words, we reduce the uncertainty interval to [ao, x^].
On the other hand, if f'(x^)
< 0, then we deduce that the minimizer lies to
the right of χ(°\ In this case, we reduce the uncertainty interval to [x^°\6o]·
Finally, if f'(x^)
= 0, then we declare x^ to be the minimizer and terminate
our search.
With the new uncertainty interval computed, we repeat the process iteratively. At each iteration k, we compute the midpoint of the uncertainty
interval. Call this point x^k\ Depending on the sign of f'{x^)
(assuming
that it is nonzero), we reduce the uncertainty interval to the left or right of
x^k\ If at any iteration k we find that f'{x^)
= 0, then we declare x^ to
be the minimizer and terminate our search.
Two salient features distinguish the bisection method from the golden section and Fibonacci methods. First, instead of using values of / , the bisection
methods uses values of / ' . Second, at each iteration, the length of the uncertainty interval is reduced by a factor of 1/2. Hence, after N steps, the range
is reduced by a factor of (1/2) N . This factor is smaller than in the golden
section and Fibonacci methods.
Example 7.3 Recall Example 7.1 where we wish to find the minimizer of
f(x) = x4-
Ux3 + 60x2 - 70x
in the interval [0,2] to within a range of 0.3. The golden section method
requires at least four stages of reduction. If, instead, we use the bisection
method, we would choose N so that
(0.5)" < 0.3/2.
In this case, only three stages of reduction are needed.
7.5
I
Newton's Method
Suppose again that we are confronted with the problem of minimizing a function / of a single real variable x. We assume now that at each measurement
NEWTON'S METHOD
117
point x^ we can determine / ( x ^ ) , / ' ( x ^ ) , and f"(x^k>)).
We can fit a
quadratic function through x^ that matches its first and second derivatives
with that of the function / . This quadratic has the form
q{x) = /(x ( / c ) ) + f'{x{k))(x
- x{k)) + y,f(x{k))(x
- x (fc) ) 2 .
Note that q(xW) = /(x<fc>), q'(x^)
= /'(x ( f c ) ), and q"{x^) = /"(χ(*>).
Then, instead of minimizing / , we minimize its approximation q. The firstorder necessary condition for a minimizer of q yields
0 = q\x) = /'(*<*>) + f"(xW)(x
Setting x = x^k+1\
- *<*>).
we obtain
f"{xwy
Example 7.4 Using Newton's method, we will find the minimizer of
f(x) = -x2
-sinx.
Suppose that the initial value is x^ = 0 . 5 , and that the required accuracy is
e = 10~ 5 , in the sense that we stop when |x(fc+1) — x^\ < e.
We compute
f'(x)
— x — cosx,
f"{x) — 1 + sinx.
Hence,
(Λ\ ~ ~ 0.5 — cos0.5
xW = 0.5 ——
1 + s i n 0.5
= ° · 5 - -0.3775
1.479
= 0.7552.
Proceeding in a similar manner, we obtain
*<">= *<« - f"(xW)
& Ά =X"-°-^ 1.685 =0.7391,
Ί-5
118
ONE-DIMENSIONAL SEARCH METHODS
x(k)
X(k+1)
Newton's algorithm with f"{x) > 0.
Figure 7.6
Note that \x^ - x^\ < e = Η Γ 5 . Furthermore, / ' ( x ( 4 ) ) = - 8 . 6 x 10" 6 « 0.
Observe that f"(x^)
= 1.673 > 0, so we can assume that x* « x^ is a strict
minimizer.
I
Newton's method works well if f"(x) > 0 everywhere (see Figure 7.6).
However, if f"(x) < 0 for some x, Newton's method may fail to converge to
the minimizer (see Figure 7.7).
Newton's method can also be viewed as a way to drive the first derivative
of / to zero. Indeed, if we set g(x) = / ; ( x ) , then we obtain a formula for
iterative solution of the equation g(x) = 0:
g(x{k))
x(fc+1) = x (*0 _
g'{x(k))'
In other words, we can use Newton's method for zero finding.
X(k+1)
Figure 7.7
x(k)
x*
Newton's algorithm with f"(x) < 0.
NEWTON'S METHOD
Figure 7.8
119
Newton's method of tangents.
Example 7.5 We apply Newton's method to improve a first approximation,
χ(°) = 12, to the root of the equation
g(x) = x3 - 12.2x2 + lAbx + 42 = 0.
We have g'{x) = 3x2 - 24Ax + 7.45.
Performing two iterations yields
102.6
11.33,
146.65
14.73
11.21.
= 11.33
116.11
cW = 12
,(2)
Newton's method for solving equations of the form g(x) = 0 is also referred
to as Newton's method of tangents. This name is easily justified if we look at
a geometric interpretation of the method when applied to the solution of the
equation g(x) = 0 (see Figure 7.8).
If we draw a tangent to g(x) at the given point x^k\ then the tangent line
intersects the x-axis at the point x^k^l\ which we expect to be closer to the
root x* of g(x) = 0. Note that the slope of g(x) at x^ is
9 <(x<'>)=
X*"'»
~(fc+i)
X (k) _
Hence,
g(x{k))
g'(x(k))'
Newton's method of tangents may fail if the first approximation to the root
is such that the ratio g(x^)/g'(x^)
is not small enough (see Figure 7.9).
Thus, an initial approximation to the root is very important.
,(*+!)
r(*0
120
ONE-DIMENSIONAL SEARCH METHODS
Figure 7.9 Example where Newton's method of tangents fails to converge to the
root x* of g(x) = 0.
7.6
Secant Method
Newton's method for minimizing / uses second derivatives of / :
x{k+i)
=
x(k)
/"(#))'
If the second derivative is not available, we may attempt to approximate it
using first derivative information. In particular, we may approximate
fff(x^)
above with
/'(χ(*))-/'(χ(*-ΐ))
x(k) _
x(k-i)
Using the foregoing approximation of the second derivative, we obtain the
algorithm
x (fe+i) =
XW
(k)
x~(k)
_
„(k-l)
x(k-l)
//(α:(*))_//(χ(*-ΐ))·
called the secant method. Note that the algorithm requires two initial points
to start it, which we denote x^~^ and x^°\ The secant algorithm can be
represented in the following equivalent form:
(fc+1)
X
_
f( X (fc)) X (fc-l)_^( x (fc-l)) x (fc)
~
/,(x(fc))-/,(x(fe-1))
Observe that, like Newton's method, the secant method does not directly
involve values of f(x^).
Instead, it tries to drive the derivative / ' to zero.
In fact, as we did for Newton's method, we can interpret the secant method
as an algorithm for solving equations of the form g(x) = 0. Specifically, the
SECANT METHOD
x (k+2)
Figure 7.10
x (k+1)
x (k)
121
x (k-1)
Secant method for root finding.
secant algorithm for finding a root of the equation g(x) = 0 takes the form
x(k+i)
_
(fc) _
x„(fc)
x~(fc-i)
9{X
g(xW) - g(xV°-»)
g(x(k))
h
or, equivalently,
X
(fc+i)
=
9(χΜ)χ«-ν - g(x(k-V)xW
g(XW) - gixV*-»)
The secant method for root finding is illustrated in Figure 7.10 (compare
this with Figure 7.8). Unlike Newton's method, which uses the slope of g to
determine the next point, the secant method uses the "secant" between the
(k — l)th and kth points to determine the (k + l)th point.
Example 7.6 We apply the secant method to find the root of the equation
g(x) = x3 - 12.2x2 + 7.45x + 42 = 0.
We perform two iterations, with starting points χ(~^ = 13 and x^
We obtain
= 12.
χΜ = 11.40,
x& = 11.25.
Example 7.7 Suppose that the voltage across a resistor in a circuit decays
according to the model V(i) = e~Rt, where V(i) is the voltage at time t and
R is the resistance value.
122
ONE-DIMENSIONAL SEARCH METHODS
Given measurements V i , . . . , Vn of the voltage at times t i , . . . , tn> respectively, we wish to find the best estimate of R. By the best estimate we mean
the value of R that minimizes the total squared error between the measured
voltages and the voltages predicted by the model.
We derive an algorithm to find the best estimate of R using the secant
method. The objective function is
/(Ä) = f>-e-«*) a .
Hence, we have
/,(Ä)=2^(V--e-Ät*)e-Ät*i<.
2=1
The secant algorithm for the problem is
Rk — Rk-i
Σ Γ = ι ( ^ - e-Ä**<)e~Äfcti*i - (Vi ~
-Rfc+l = Rk
n
x
e-Kx-^e-Kx-iHi
Y^(Vi-e-RkU)e-Rktiti.
i=l
For further reading on the secant method, see [32]. Newton's method
and the secant method are instances of quadratic fit methods. In Newton's
method, x(fc+1) is the stationary point of a quadratic function that fits / ' and
/ " at x^k\ In the secant method, x(fc+1) is the stationary point of a quadratic
function that fits / ' at x^ and x^k_1\
The secant method uses only / ' (and
not / " ) but needs values from two previous points. We leave it to the reader
to verify that if we set χ^+^ to be the stationary point of a quadratic function that fits / at x^k\ χ^~λ\ and x^k~2\ we obtain a quadratic fit method
that uses only values of / :
(fc+i)
Xv
=
W ( * ( f c ) ) + a 2 0 /(x( f c - 1 )) + σ 01 /(χ(*- 2 >)
2(ίΐ2/(*<*>) + <W(z ( f c - 1 } ) + W ( z ( f c - 2 ) ) )
where σ^ = (x ( / c - i ) ) 2 - (x (fc ~ j) ) 2 and <% = x^k~^ - x^k~^ (see Exercise 7.9)
This method does not use / ' or / " , but needs values of / from three previous
points. Three points are needed to initialize the iterations. The method is
also sometimes called inverse parabolic interpolation.
An approach similar to fitting (or interpolation) based on higher-order
polynomials is possible. For example, we could set x^k+1^ to be a stationary
point of a cubic function that fits / ' at x^k\ x^k~x\ and x^k~2\
It is often practically advantageous to combine multiple methods, to overcome the limitations in any one method. For example, the golden section
method is more robust but slower than inverse parabolic interpolation. Brent's
method combines the two [17], resulting in a method that is faster than the
golden section method but still retains its robustness properties.
BRACKETING
Xo
Figure 7.11
7.7
Xi
X2
X3
123
► X
An illustration of the process of bracketing a minimizer.
Bracketing
Many of the methods we have described rely on an initial interval in which the
minimizer is known to lie. This interval is also called a bracket, and procedures
for finding such a bracket are called bracketing methods.
To find a bracket [a, b] containing the minimizer, assuming unimodality, it
suffices to find three points a < c < b such that /(c) < / ( a ) and /(c) < f(b). A
simple bracketing procedure is as follows. First, we pick three arbitrary points
xo < xi < #2- If / ( # i ) < /(#o) and f(x\) < / ( ^ ) , then we are done—the
desired bracket is [#0^2]· If not, say f(xo) > f(xi) > ffa), then we pick a
point xs > X2 and check if /(#2) < /(#3)· If it holds, then again we are done—
the desired bracket is [χι,Χβ]. Otherwise, we continue with this process until
the function increases. Typically, each new point chosen involves an expansion
in distance between successive test points. For example, we could double the
distance between successive points, as illustrated in Figure 7.11. An analogous
process applies if the initial three points are such that f(xo) < / ( # i ) < /(#2)·
In the procedure described above, when the bracketing process terminates,
we have three points Xfc-2, #fc-i, and Xk such that f(xk-i) < f{xk-2) and
f(xk-i) < f(xk)· The desired bracket is then [xfc_2,Xfc], which we can then
use to initialize any of a number of search methods, including the golden
section, Fibonacci, and bisection methods. Note that at this point, we have
already evaluated /(χ^_2), f(xk-i), and f(xk)· If function evaluations are
expensive to obtain, it would help if the point Xk-i inside the bracket also
124
ONE-DIMENSIONAL SEARCH METHODS
coincides with one of the points used in the search method. For example,
if we intend to use the golden section method, then it would help if Xk-ι ~
Xk-2 — p{%k — Xk-2), where p = (3 — \/5)/2. In this case, Xk-i would be
one of the two points within the initial interval used in the golden section
method. This is achieved if each successive point Xk is chosen such that
Xk = Xk-i + (2 — p)(xk-i — Xk-2)- In this case, the expansion in the distance
between successive points is a factor 2 — p « 1.618, which is less than double.
7.8
Line Search in Multidimensional Optimization
One-dimensional search methods play an important role in multidimensional
optimization problems. In particular, iterative algorithms for solving such
optimization problems (to be discussed in the following chapters) typically
involve a line search at every iteration. To be specific, let / : W1 —► R
be a function that we wish to minimize. Iterative algorithms for finding a
minimizer of / are of the form
k
xV<+»=xW+akdfi
where x^
\
is a given initial point and a^ > 0 is chosen to minimize
0 fc (a) = /(*<*>+ad ( f c ) ).
The vector er ' is called the search direction and α& is called the step size.
Figure 7.12 illustrates a line search within a multidimensional setting. Note
that choice of ctk involves a one-dimensional minimization. This choice ensures
that under appropriate conditions,
/(*( f c + i )) < /(»<*>).
Any of the one-dimensional methods discussed in this chapter (including
bracketing) can be used to minimize </>&. We may, for example, use the secant
method to find α&. In this case we need the derivative of (j)k, which is
0'fc(a) = d<*>T V/(a><fc) + ad ( f c ) ).
This is obtained using the chain rule. Therefore, applying the secant method
for the line search requires the gradient V / , the initial line-search point
x^k\ and the search direction d> ' (see Exercise 7.11). Of course, other onedimensional search methods may be used for line search (see, e.g., [43] and
[88]).
Line-search algorithms used in practice involve considerations that we have
not yet discussed thus far. First, determining the value of α& that exactly
minimizes 4>k may be computationally demanding; even worse, the minimizer
of φκ may not even exist. Second, practical experience suggests that it is
better to allocate more computational time on iterating the multidimensional
LINE SEARCH IN MULTIDIMENSIONAL OPTIMIZATION
Figure 7.12
125
Line search in multidimensional optimization.
optimization algorithm rather than performing exact line searches. These
considerations led to the development of conditions for terminating line-search
algorithms that would result in low-accuracy line searches while still securing
a sufficient decrease in the value of the / from one iteration to the next. The
basic idea is that we have to ensure that the step size ctk is not too small or
too large.
Some commonly used termination conditions are as follows. First, let ε G
(0,1), 7 > 1, and η G (ε, 1) be given constants. The Armijo condition ensures
that Qfc is not too large by requiring that
0fc(a f c )<0fc(O)+ea f c ^(O).
Further, it ensures that a& is not too small by requiring that
0*(7<*fc) > 0*(O) + e7a*0!b(O).
The Goldstein condition differs from Armijo in the second inequality:
<£*(<**) > ^ ( Ο ) + η α * 0 * ( Ο ) .
The first Armijo inequality together with the Goldstein condition are often
jointly called the Armijo-Goldstein condition. The Wolfe condition differs
from Goldstein in that it involves only (fr'k:
4>'k(ak) > ηφ'^0).
126
ONE-DIMENSIONAL SEARCH METHODS
A stronger variation of this is the strong Wolfe condition:
Wk{ak)\<nWkmA simple practical (inexact) line-search method is the Armijo backtracking
algorithm, described as follows. We start with some candidate value for the
step size α&. If this candidate value satisfies a prespecified termination condition (usually the first Armijo inequality), then we stop and use it as the step
size. Otherwise, we iteratively decrease the value by multiplying it by some
constant factor r G (0,1) (typically r = 0.5) and re-check the termination
condition. If a^0^ is the initial candidate value, then after m iterations the
value obtained is α& = τ^α^.
The algorithm backtracks from the initial
value until the termination condition holds. In other words, the algorithm
produces a value for the step size of the form α^ = rma^
with m being the
smallest value in {0,1,2,...} for which α^ satisfies the termination condition.
For more information on practical line-search methods, we refer the reader
to [43, pp. 26-40], [96, Sec. 10.5], [11, App. C], [49], and [50].x
EXERCISES
7.1 Suppose that we have a unimodal function over the interval [5,8]. Give
an example of a desired final uncertainty range where the golden section
method requires at least four iterations, whereas the Fibonacci method requires only three. You may choose an arbitrarily small value of ε for the
Fibonacci method.
7.2 Let f(x) = x2 + 4cosx, x G i We wish to find the minimizer x* of /
over the interval [1,2]. (Calculator users: Note that in cosx, the argument x
is in radians.)
a. Plot f(x) versus x over the interval [1,2].
b . Use the golden section method to locate x* to within an uncertainty of
0.2. Display all intermediate steps using a table:
Iteration k
1
2
CLk
bk
?
?
?
?
/K)
/(**)
?
?
?
?
New uncertainty interval
[?,?]
[?,?]
c. Repeat part b using the Fibonacci method, with ε = 0.05. Display all
intermediate steps using a table:
1
We thank Dennis M. Goodman for furnishing us with references [49] and [50].
EXERCISES
Iteration k
1
2
Pk
?
Q>k
&fc
?
Hak)
?
?
?
?
?
?
/(M
?
?
127
New uncertainty interval
[?,?]
[?,?]
d. Apply Newton's method, using the same number of iterations as in part
b, with χ(°) = 1.
7.3 Let / ( # ) = 8e 1 - a : + 71og(x), where "log" represents the natural logarithm
function.
a. Use MATLAB to plot f(x) versus x over the interval [1,2], and verify
that / is unimodal over [1,2].
b . Write a simple MATLAB program to implement the golden section
method that locates the minimizer of / over [1,2] to within an uncertainty
of 0.23. Display all intermediate steps using a table as in Exercise 7.2.
c. Repeat part b using the Fibonacci method, with ε = 0.05. Display all
intermediate steps using a table as in Exercise 7.2.
7.4 Suppose that p i , . . . , p ; v are the values used in the Fibonacci search
method. Show that for each k = 1 , . . . , AT, 0 < pk < 1/2, and for each
fc = l , . . . , 7 V - l ,
7.5 Show that if F 0 , F i , . . . is the Fibonacci sequence, then for each k =
2,3,...,
^fc-2^fc+i - Fk-\Fk = (-1) .
7.6 Show that the Fibonacci sequence can be calculated using the formula
7.7 Suppose that we have an efficient way of calculating exponentials. Based
on this, use Newton's method to devise a method to approximate log(2) [where
"log" is the natural logarithm function]. Use an initial point of x^ = 1, and
perform two iterations.
7.8 Consider the problem of finding the zero of g(x) = (ex — l)/{ex + 1),
x G R, where ex is the exponential of x. (Note that 0 is the unique zero of g.)
128
ONE-DIMENSIONAL SEARCH METHODS
a. Write down the algorithm for Newton's method of tangents applied to
this problem. Simplify using the identity sinha: = (ex — e~x)/2.
b . Find an initial condition x^ such that the algorithm cycles [i.e., x^ =
x{2) _ χ (4) _ . . . j Y O U n e e ( j n o ^ explicitly calculate the initial condition;
it suffices to provide an equation that the initial condition must satisfy.
Hint: Draw a graph of g.
c. For what values of the initial condition does the algorithm converge?
7.9 Derive a one-dimensional search (minimization) algorithm based on
quadratic fit with only objective function values. Specifically, derive an algorithm that computes x^fc+1) based on x^k\ χ^~λ\ x^k~2\ f(x^),
/(x^-1^),
k
and
f{x( -V).
Hint: To simplify, use the notation σ^ = (x^k~^)2 — (x^k~^)2 and Sij =
Y O U might a l S o find it useful to experiment with your algox{k-%) _ x(k-j)^
rithm by writing a MATLAB program. Note that three points are needed to
initialize the algorithm.
7.10 The objective of this exercise is to implement the secant method using
MATLAB.
a. Write a simple MATLAB program to implement the secant method to
locate the root of the equation g(x) = 0. For the stopping criterion, use
the condition |x^ + 1 ^ — x^\ < \χ(^\ε, where ε > 0 is a given constant.
b . Let g(x) = (2x - l ) 2 + 4(4 - 1024x) 4 . Find the root of g(x) = 0 using the
secant method with χ(~^ = 0, χ^ = 1, and ε = 10~ 5 . Also determine
the value of g at the solution obtained.
7.11 Write a MATLAB function that implements a line search algorithm
using the secant method. The arguments to this function are the name of
the M-file for the gradient, the current point, and the search direction. For
example, the function may be called linesearch_secant and be used by the
function call alpha=linesearch_secant( , grad , , x , d ) , where grad.m is the
M-file containing the gradient, x is the starting line search point, d is the
search direction, and alpha is the value returned by the function [which we
use in the following chapters as the step size for iterative algorithms (see, e.g.,
Exercises 8.25 and 10.11)].
Note: In the solutions manual, we used the stopping criterion \d Vf(x +
ad)| < ε\ά V / ( x ) | , where ε > 0 is a prespecified number, V / is the gradient,
x is the starting line search point, and d is the search direction. The rationale
for the stopping criterion above is that we want to reduce the directional
derivative of / in the direction d by the specified fraction ε. We used a value
of ε = 1 0 - 4 and initial conditions of 0 and 0.001.
EXERCISES
129
7.12 Consider using a gradient algorithm to minimize the function
/(«)=i* r
with the initial guess x^
2
ll
= [0.8, -0.25] T .
a. To initialize the line search, apply the bracketing procedure in Figure 7.11
along the line starting at x^ in the direction of the negative gradient.
Use ε = 0.075.
b . Apply the golden section method to reduce the width of the uncertainty
region to 0.01. Organize the results of your computation in a table format
similar to that of Exercise 7.2.
c. Repeat the above using the Fibonacci method.
CHAPTER 8
GRADIENT METHODS
8.1
Introduction
In this chapter we consider a class of search methods for real-valued functions
on R n . These methods use the gradient of the given function. In our discussion
we use such terms as level sets, normal vectors, and tangent vectors. These
notions were discussed in some detail in Part I.
Recall that a level set of a function / : R n —> R is the set of points x
satisfying f(x) = c for some constant c. Thus, a point XQ G R n is on the level
set corresponding to level c if f(xo) = c. In the case of functions of two real
variables, / : R 2 —> R, the notion of the level set is illustrated in Figure 8.1.
The gradient of / at x$, denoted Vf(xo), if it is not a zero vector, is
orthogonal to the tangent vector to an arbitrary smooth curve passing through
Xo on the level set f(x) = c. Thus, the direction of maximum rate of increase
of a real-valued differentiable function at a point is orthogonal to the level
set of the function through that point. In other words, the gradient acts in
such a direction that for a given small displacement, the function / increases
more in the direction of the gradient than in any other direction. To prove
this statement, recall that (V/(sc),d), ||d|| = 1, is the rate of increase of / in
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
131
132
GRADIENT METHODS
Z=f(Xi,X 2 )
Figure 8.1
Constructing a level set corresponding to level c for / .
the direction d at the point x. By the Cauchy-Schwarz inequality,
(V/(x),d)<||V/(x)||
because ||d|| = 1. But if d = V/(aj)/||V/(x)||, then
(v/w-Ä>-|V/(·»·
Thus, the direction in which Vf(x) points is the direction of maximum rate
of increase of / at x. The direction in which — V/(a?) points is the direction of
maximum rate of decrease of / at x. Hence, the direction of negative gradient
is a good direction to search if we want to find a function minimizer.
We proceed as follows. Let x^ be a starting point, and consider the point
χ(°) — a V / ( a ; ^ ) . Then, by Taylor's theorem, we obtain
/(x<°> - aV/(*<°>)) - / ( x ( 0 ) ) - α||ν/(χ(°))|| 2 + o{a).
Thus, if V/(aj(°)) φ 0, then for sufficiently small a > 0, we have
/(χ(°)-αν/(^))</(χ(0)).
This means that the point x^ ~ Q V / ( ^ 0 ' ) is an improvement over the point
χ(°) if W e are searching for a minimizer.
To formulate an algorithm that implements this idea, suppose that we are
given a point x^k\ To find the next point x^k+l\ we start at x^ and move
by an amount —afcV/(x^fc^), where α^ is a positive scalar called the step size.
This procedure leads to the following iterative algorithm:
x( f c + 1 )= a j ( f c )-a f c V/(x( f c )).
THE METHOD OF STEEPEST DESCENT
133
We refer to this as a gradient descent algorithm (or simply a gradient algorithm). The gradient varies as the search proceeds, tending to zero as we
approach the minimizer. We have the option of either taking very small steps
and reevaluating the gradient at every step, or we can take large steps each
time. The first approach results in a laborious method of reaching the minimizer, whereas the second approach may result in a more zigzag path to the
minimizer. The advantage of the second approach is possibly fewer gradient evaluations. Among many different methods that use this philosophy the
most popular is the method of steepest descent, which we discuss next.
Gradient methods are simple to implement and often perform well. For
this reason, they are used widely in practical applications. For a discussion
of applications of the steepest descent method to the computation of optimal controllers, we recommend [85, pp. 481-515]. In Chapter 13 we apply a
gradient method to the training of a class of neural networks.
8.2
The Method of Steepest Descent
The method of steepest descent is a gradient algorithm where the step size
a*: is chosen to achieve the maximum amount of decrease of the objective function at each individual step. Specifically, α^ is chosen to minimize
0fc(a) = f{x^ — aVf(x^)).
In other words,
ak = argmin/(x ( f c ) - aV/(aj (fe) )).
a>0
To summarize, the steepest descent algorithm proceeds as follows: At each
step, starting from the point x^k\ we conduct a line search in the direction
—Vf(x^)
until a minimizer, χ^+1\
is found. A typical sequence resulting
from the method of steepest descent is depicted in Figure 8.2.
Observe that the method of steepest descent moves in orthogonal steps, as
stated in the following proposition.
Proposition 8.1 If {x^}kLo is a steepest descent sequence for a given function f : Rn —► R, then for each k the vector x( fc+1 ) — x^ is orthogonal to the
vector x^^
- x^k+l\
D
Proof. From the iterative formula of the method of steepest descent it follows
that
(x(fe+1> - a.(fe),x(fc+2) - a!<fc+1>) = afcafc+i<V/(aj<*>), V/(*( fc+1 >)>.
To complete the proof it is enough to show that
134
GRADIENT METHODS
x(°)^
Figure 8.2
CQ>CI>C2>C3
Typical sequence resulting from the method of steepest descent.
To this end, observe that α^ is a nonnegative scalar that minimizes φΐζ{θί) =
f(x^
— a\7f(x^)).
Hence, using the FONC and the chain rule gives us
= V/(*<*> -
akVf(x^))T(-Vf(x^))
= -(V/(^t+1)),V/(x»)},
which completes the proof.
I
The proposition above implies that Vf(x^)
is parallel to the tangent plane
to the level set {f(x) = /(a^ fc+1 ^)} at χ^+1\
Note that as each new point is
generated by the steepest descent algorithm, the corresponding value of the
function / decreases in value, as stated below.
Proposition 8.2 If{x^}^=0
is the steepest descent sequence for f : R n —> R
1
and i / V / ( » W ) φ 0, then /{x^ ^
< f{x{k)).
□
Proof. First recall that
x(fc+i) = a .(fc)_ a f c V /(x( f c )),
where a^ > 0 is the minimizer of
0 fc (a) = /(*<*>-aV/(*<*>))
over all a: > 0. Thus, for a > 0, we have
THE METHOD OF STEEPEST DESCENT
135
By the chain rule,
44(0) = ^ ( 0 ) = - ( V / ( * W - 0V/(*( f e )))) T V/(*W) = - | | V / ( x W ) | | 2 < 0
because V / ( a j ^ ) φ 0 by assumption. Thus, </4(0) < 0 and this implies that
there is an ä > 0 such that (j>k(0) > 0&(α) for all a G (0, ä]. Hence,
/ ( α ^ + 1 > ) = 0fc(afc) < <t>k{a) < <j>k(0) = /(x<*>),
which completes the proof.
I
In Proposition 8.2, we proved that the algorithm possesses the descent
property: /(x( f c + 1 )) < f(x^)
if V / ( x ^ ) Φ 0. If for some fc, we have
V/(x ( f c ) ) = 0, then the point x^ satisfies the FONC. In this case, a?(fc+1> =
χ(*0. We can use the above as the basis for a stopping (termination) criterion
for the algorithm.
The condition V/(ic^ + 1 ^) = 0, however, is not directly suitable as a practical stopping criterion, because the numerical computation of the gradient will
rarely be identically equal to zero. A practical stopping criterion is to check
if the norm | | V / ( x ^ ) | | of the gradient is less than a prespecified threshold,
in which case we stop. Alternatively, we may compute the absolute difference
|/(;r( fc+1 )) — f(x^)\
between objective function values for every two successive iterations, and if the difference is less than some prespecified threshold,
then we stop; that is, we stop when
|/( X (*+D) - /(χ(*))| < ε,
where ε > 0 is a prespecified threshold. Yet another alternative is to compute
the norm ||x^fc+1^ — x^\\ of the difference between two successive iterates,
and we stop if the norm is less than a prespecified threshold:
\\χν°+ν-χΜ\\<ε.
Alternatively, we may check "relative" values of the quantities above; for
example,
|/(«(fc+1))-/(g(fc))|
^
\f(xW)\
or
|| x (fc+i)_a.(*0||
P^l
<£
The two (relative) stopping criteria above are preferable to the previous (absolute) criteria because the relative criteria are "scale-independent." For example, scaling the objective function does not change the satisfaction of the criterion |/(a?( fc+1 ))-/(;c( fc ))|/|/(x( fc ))| < e. Similarly, scaling the decision variable
does not change the satisfaction of the criterion ||£c^+1^ — aj(fe)||/||ic(fc))|| < e.
136
GRADIENT METHODS
To avoid dividing by very small numbers, we can modify these stopping criteria as follows:
|/(g<fc+1>)-/(*<*>)!
max{l, |/(*<*>)|}
or
||aj(*+i)_a.(fc)||
fc
< ε.
max{l, ||x( )||}
Note that the stopping criteria above are relevant to all the iterative algorithms we discuss in this part.
Example 8.1 We use the method of steepest descent to find the minimizer
of
f(xi,X2,xs)
= (xi ~ 4) 4 + (x2 - 3) 2 + 4(x 3 + 5) 4 .
The initial point is x^ = [4,2, — 1] T . We perform three iterations.
We find that
V / ( * ) = [4(xi - 4) 3 ,2(x 2 - 3), 16(x3 + 5) 3 ] T .
Hence,
V / ( x ( 0 ) ) = [0,-2,1024] T .
To compute x^\
we need
a 0 = argmin/(x ( °) - a V / ( x ( 0 ) ) )
= argmin(0 + (2 + 2a - 3) 2 + 4 ( - l - 1024a + 5) 4 )
a>0
= argmin</>o(a).
a>0
Using the secant method from Section 7.6, we obtain
a 0 = 3.967 x 1 0 - 3 .
For illustrative purpose, we show a plot of φο(α) versus a in Figure 8.3,
obtained using MATLAB. Thus,
x{l) = x{0) - a 0 V / ( x ( 0 ) ) = [4.000,2.008, -5.062] T .
To find x^2\ we first determine
V / ( « ( 1 ) ) = [0.000,-1.984,-0.003875] T .
Next, we find a i , where
a i = argmin(0 + (2.008 + 1.984a - 3) 2 + 4(-5.062 + 0.003875a + 5) 4 )
a>0
= arg min 0i (a).
a>0
THE METHOD OF STEEPEST DESCENT
Φο(α)
7000
0.002
0.004
a
0.006
0.008
Figure 8.3
Plot of φο(α) versus a.
Figure 8.4
Plot of φι(α) versus a.
Φΐ(α)
0.01
137
138
GRADIENT METHODS
φ2(α)
1.6
1.2
0.8
0.4
"10
12
Figure 8.5
14
α
16
18
20
Plot of φι (a) versus a.
Using the secant method again, we obtain OL\ = 0.5000. Figure 8.4 depicts a
plot of φι(α) versus a. Thus,
XW
=a.(i)
_ α ι ν / ( χ ( 1 ) ) = [4.000,3.000,-5.060] T .
To find χ(3\ we first determine
V/(;z ( 2 ) ) = [0.000,0.000,-0.003525] T
and
a2 = argmin(0.000 + 0.000 + 4(-5.060 + 0.003525a + 5) 4 )
a>0
= arg min 02(a).
α>0
We proceed as in the previous iterations to obtain a2 = 16.29. A plot of φ2(θί)
versus a is shown in Figure 8.5.
The value of x^ is
x ( 3 ) = [4.000,3.000,-5.002] T .
Note that the minimizer of / is [ 4 , 3 , - 5 ] T , and hence it appears that we
have arrived at the minimizer in only three iterations. The reader should be
cautioned not to draw any conclusions from this example about the number
of iterations required to arrive at a solution in general.
It goes without saying that numerical computations, such as those in this
example, are performed in practice using a computer (rather than by hand).
THE METHOD OF STEEPEST DESCENT
139
The calculations above were written out explicitly, step by step, for the purpose of illustrating the operations involved in the steepest descent algorithm.
The computations themselves were, in fact, carried out using a MATLAB
program (see Exercise 8.25).
I
Let us now see what the method of steepest descent does with a quadratic
function of the form
f(x) = -xTQx
- bTx,
where Q G R n X n is a symmetric positive definite matrix, 6 G R n , and x G M.n.
The unique minimizer of / can be found by setting the gradient of / to zero,
where
V / ( x ) = Qx - 6,
because D (xTQx) = xT(Q + QT) = 2xTQ, and D(bTx) = bT. There is
no loss of generality in assuming Q to be a symmetric matrix. For if we are
given a quadratic form xT Ax and Αφ A , then because the transposition
of a scalar equals itself, we obtain
(xT Ax)T
= xT ATx
— xT
Ax.
Hence,
χγΑχ=1-χγΑχ+1-χτΑΎχ
= ^xT(A
=
Note that
+
AT)x
2χΤ®χ·
(A + A T ) T = Q T = A + AT = Q.
The Hessian of / is F(x) = Q = Q > 0. To simplify the notation we
write gW = Vf(x^).
Then, the steepest descent algorithm for the quadratic
function can be represented as
x<*+i> =a .(*)-a fcfl < fc >,
where
ak = argmin/(ic (A:) = arg min (\{x{k)
ag^)
- ag^)TQ(x^
- ag™) - (x{k) - ag^)Tb)
.
In the quadratic case, we can find an explicit formula for ak- We proceed
as follows. Assume that g^ φ 0, for if gW = 0, then x^ = x* and the
140
GRADIENT METHODS
g (or
Figure 8.6
Steepest descent method applied to /(xi, X2) = x\ + %\>
algorithm stops. Because α^ > 0 is a minimizer of 4>k(oi) = f(x^
we apply the FONC to 4>k(&) to obtain
—
ag^),
φ',(α) = ( * « - a f l W ) T Q ( - f l W ) - b T (-g ( f e ) )·
Therefore, </>'fc(a) = 0 if agWTQgW
= (x^TQ
- bT)g(-k\
But
x^TQ-bT=g^T.
Hence,
«fc = gWTQg(k)'
In summary, the method of steepest descent for the quadratic takes the
form
_ g{k)Tg{k)
β (*+ΐ) - xw
(k)
where
gW = Vf(x{k))
= Qx{k)
-b.
Example 8.2 Let
f(xi,X2)
=Xi+xl-
Then, starting from an arbitrary initial point x^ G M2, we arrive at the
solution x* = 0 G M2 in only one step. See Figure 8.6.
However, if
x2
f{xux2) = y + x\,
then the method of steepest descent shuffles ineffectively back and forth when
searching for the minimizer in a narrow valley (see Figure 8.7). This example illustrates a major drawback in the steepest descent method. More
ANALYSIS OF GRADIENT METHODS
Figure 8.7
141
Steepest descent method in search for minimizer in a narrow valley.
sophisticated methods that alleviate this problem are discussed in subsequent
chapters.
I
To understand better the method of steepest descent, we examine its convergence properties in the next section.
8.3
Analysis of Gradient Methods
Convergence
The method of steepest descent is an example of an iterative algorithm. This
means that the algorithm generates a sequence of points, each calculated on
the basis of the points preceding it. The method is a descent method because
as each new point is generated by the algorithm, the corresponding value of
the objective function decreases in value (i.e., the algorithm possesses the
descent property).
We say that an iterative algorithm is globally convergent if for any arbitrary
starting point the algorithm is guaranteed to generate a sequence of points
converging to a point that satisfies the FONC for a minimizer. When the
algorithm is not globally convergent, it may still generate a sequence that
converges to a point satisfying the FONC, provided that the initial point is
sufficiently close to the point. In this case we say that the algorithm is locally
convergent How close to a solution point we need to start for the algorithm
to converge depends on the local convergence properties of the algorithm. A
related issue of interest pertaining to a given locally or globally convergent
algorithm is the rate of convergence; that is, how fast the algorithm converges
to a solution point.
In this section we analyze the convergence properties of descent gradient
methods, including the method of steepest descent and gradient methods
with fixed step size. We can investigate important convergence characteristics
of a gradient method by applying the method to quadratic problems. The
convergence analysis is more convenient if instead of working with / we deal
142
GRADIENT METHODS
with
V(x) = f{x) + \x*TQx* = \{χ - x*)TQ(x - x*),
where Q = QT > 0. The solution point x* is obtained by solving Qx =
6; that is, x* = Q~lb. The function V differs from / only by a constant
^x*TQx*. We begin our analysis with the following useful lemma that applies
to a general gradient algorithm.
Lemma 8.1 The iterative algorithm
X(k+l)
with g^
= Qx^
X(k)
=
_
^gik)
— b satisfies
y(*(fc+1)) = (i-7*m* ( f c ) ),
where if gW = 0, then η^ = 1, and if g^
gWTQgW
7fc _
φ 0, then
N
^gWTg(k)
ak
Z
gWTQ-lg{k)
<*") '
\ g(k)TQg(k)
D
Proof. The proof is by direct computation. Note that if g^> = 0, then the
desired result holds trivially. In the remainder of the proof, assume that
g(k) φ 0. We first evaluate the expression
K(gW)-V(g<*+1))
V(xW)
To facilitate computations, let yW
\y(k)rQy{k).
Hence,
= x^
- x*.
Then, Vfo^)
ν(χ(*+!)) = i(x( fc+1 > - a;')TQ(ar<fc+1> - x*)
= \{x^
akgW)TQ(xW
-x*-
= \y{k)TQy^
- akg^QyW
- x* - afcfl<*>)
+
\α\9^Qg^.
Therefore,
V(xW) - V(x(k+V)
V{Xik))
Because
gW = QXW -
_ 2ak9WrQyW
- a2kgWTQgW
~
y(k)TQy(k)
b =
Qx{k) _ QX*
=
Qy(k)^
=
ANALYSIS OF GRADIENT METHODS
143
we have
y{k)TQy^=9^TQ-l9{k\
g(k)TQyW=gWTgW.
Therefore, substituting the above, we get
y(gW)-y(a(fc+i)) _
V(x(k))
~
gWTQgW
g(k)TQ-^g(k)
ak
(
g^Tg^
{^gWTQgik)
= Tfc-
■
Note that j k < 1 for all fc, because j k = 1 - V[x^k+^)/V(x^)
and V is
a nonnegative function. If 7/- = 1 for some fc, then V(ic(fe+1)) = 0, which is
equivalent to x^k+1>} = x*. In this case we also have that for alH > k + 1,
χ{ι) _ x* a n ( j ^ — 1. It turns out that 7^ = 1 if and only if either gW = 0
or gW is an eigenvector of Q (see Lemma 8.3).
We are now ready to state and prove our key convergence theorem for
gradient methods. The theorem gives a necessary and sufficient condition for
the sequence {x^} generated by a gradient method to converge to x*] that
is, x^ —> x* or limfc^oo x^ = x*.
Theorem 8.1 Let {x^} be the sequence resulting from a gradient algorithm
xik+i) — x(k) — afcgf(/c). Let 7fc be as defined in Lemma 8.1, and suppose that
7fc > 0 for all k. Then, {x^}
converges to x* for any initial condition x^
if and only if
00
J^7fc = 00.
k=0
D
Proof From Lemma 8.1 we have V(x(k+^)
we obtain
= (1 — 7^) V(x^),
from which
ν(χ^)=(γ[(1-ΊΛν(χ^).
Assume that 7^ < 1 for all fc, for otherwise the result holds trivially. Note
that x^ —► x* if and only if V(x^)
—► 0. By the equation above we see that
this occurs if and only if Πί1ο(1 ~~ Ίί) — 0> which, in turn, holds if and only
if Y^LQ — log(l — 7i) = 00 (we get this simply by taking logs). Note that by
Lemma 8.1, 1 — 7$ > 0 and log(l — 7*) is well-defined [log(0) is taken to be
—00]. Therefore, it remains to show that Σ ° ^ 0 ~~ l°s(l"" Ί%) = °° ^ a n d οη^Υ
if
00
We first show that J^SoTi = °° ^ m P ^ e s that Σ ^ ο — l°s(l ~~ 7») — °°.
For this, first observe that for any x G R, x > 0, we have log(x) < x — 1
144
GRADIENT METHODS
[this is easy to see simply by plotting log(x) and x — 1 versus x]. Therefore,
log(l - 7i) < 1 - 7i - 1 = -7», and hence - l o g ( l - 7<) > 7». Thus, if
Σ £ 0 7* = oo> t h e n clearly £ ~ 0 - log(l - 7*) = 00.
Finally, we show that X ^ 0 — log(l — 7») = 00 implies that X]°^0 7* = °°We proceed by contraposition. Suppose that Σ ^ 0 ^ < °°- Then, it must
be that 7^ —► 0. Now observe that for x G M, x < 1 and x sufficiently
close to 1, we have log(x) > 2(x — 1) [as before, this is easy to see simply
by plotting log(x) and 2(x — 1) versus #]. Therefore, for sufficiently large i,
log(l - 7<) > 2(1 - 7i - 1) = -27», which implies that - l o g ( l - 7 ^ < 27».
Hence, Σ ϊ ο 7 χ < oo implies that Σιίο ~ ^ β ί 1 ~ 7t) < °°This completes the proof.
I
The assumption in Theorem 8.1 that 7^ > 0 for all k is significant in that it
corresponds to the algorithm having the descent property (see Exercise 8.23).
Furthermore, the result of the theorem does not hold in general if we do not
assume that 7^ > 0 for all A;, as shown in the following example.
Example 8.3 We show, using a counterexample, that the assumption that
7fc > 0 in Theorem 8.1 is necessary for the result of the theorem to hold.
Indeed, for each k = 0 , 1 , 2 , . . . , choose otk in such a way that 72/e = —1/2
and 72&+1 = 1/2 (we can always do this if, for example, Q = In). From
Lemma 8.1 we have
y(a.(2(*+D))
=
(1 _ i/2)(l + 1/2)V(*<2*>) = (3/4)V(a5<2*>).
Therefore, V(x^2k>>) -» 0. Because V{x^2k+l">) = (3/2)V{xW),
we also have
that y(x( 2 f c + 1 )) -» 0. Hence, V(x^)
-> 0, which implies that ajW -> 0 (for
all a;(0)). On the other hand, it is clear that
A
1
for all k. Hence, the result of the theorem does not hold if 7^ < 0 for some A;.
■
Using the general theorem above, we can now establish the convergence of
specific cases of the gradient algorithm, including the steepest descent algorithm and algorithms with fixed step size. In the analysis to follow, we use
Rayleigh's inequality, which states that for any Q = QT > 0, we have
(Q)H«H2 < xTQx < Amax(Q)||x||2,
where A m i n (Q) denotes the minimal eigenvalue of Q and A max (Q) denotes the
maximal eigenvalue of Q. For Q = Q > 0, we also have
Amin(Q
) — T
^maxlW
)
=
T
77v\"'
77y\'
ANALYSIS OF GRADIENT METHODS
145
and
Lemma 8.2 Let Q = QT > 0 be an n x n real symmetric positive definite
matrix. Then, for any x G R n , we have
(xTx)2
(Q)
T
T
1
Amax(Q) " {x Qx)(x Q- x)
^
A
max
(Q)
~ A m i n (Q)'
D
Proof. Applying Rayleigh's inequality and using the properties of symmetric
positive definite matrices listed previously, we get
(xTx)2
T
T
||x|| 4
<
1
(X QX)(X Q~ X)
_Amax(Q)
2
~ XminiQ^xW^miniQ-^Wxll
A m i n (Q)
and
(xTx)2
(x Qx)(xTQ-1x)
\\x\\4
_ Aml„(Q)
~ A max (Q)|| iC ||2A max (Q- 1 )||a ; ||2
A m a x (Q)·
>
T
We are now ready to establish the convergence of the steepest descent
method.
Theorem 8.2 In the steepest descent algorithm, we have x^
—> x* for any
Proof IfflfW = Ofor some A;, then x^ = x* and the result holds. So assume
that gW ^ 0 for all k. Recall that for the steepest descent algorithm,
g(k)Tg(k)
Oik
=
g(k)TQg(k)'
Substituting this expression for a^ in the formula for 7^ yields
(g(fc>y*>)2
lk
(gWTQgW)(gWTQ~1gW)'
Note that in this case 7/- > 0 for all k. Furthermore, by Lemma 8.2, we have
7fc > (A m in(Q)/A max (Q)) > 0. Therefore, we have ^fcLo^fe = °°> a n d hence
by Theorem 8.1 we conclude that x^ —> x*.
I
Consider now a gradient method with fixed step size; that is, ctk = et G R
for all k. The resulting algorithm is of the form
*(*+!)= a ; « _ a f l <*).
146
GRADIENT METHODS
We refer to the algorithm above as a fixed-step-size gradient algorithm. The
algorithm is of practical interest because of its simplicity. In particular, the
algorithm does not require a line search at each step to determine ctk, because
the same step size a is used at each step. Clearly, the convergence of the
algorithm depends on the choice of a, and we would not expect the algorithm
to work for arbitrary a. The following theorem gives a necessary and sufficient
condition on a for convergence of the algorithm.
Theorem 8.3 For the fixed-step-size gradient algorithm, x^
χ(°ϊ if and only if
—» x* for any
^max(Q)
D
Proof <=: By Rayleigh's inequality we have
Xmin(Q)9WT9W
<gMTQgW
\m^Q)g{k)19(fe)
<
and
Therefore, substituting the above into the formula for 7^, we get
Therefore, 7/- > 0 for all k, and 5^fcL07fc = °°· Hence, by Theorem 8.1 we
conclude that x^ —» x*.
=>: We use contraposition. Suppose that either a < 0 or a > 2/A m a x (Q).
Let χ(°ϊ be chosen such that x^ — x* is an eigenvector of Q corresponding
to the eigenvalue A m a x (Q). Because
x(fc+l) = x{k)
_ a(Qx{k)
_ty=
x(k)
_ a(Qx{k)
_ QX*^
we obtain
x{k+l)
_ χ*
=
x(k)
_ χ* _ a(Qx{k)
_
Qx*j
= (I n -aQ)(*< f c >-**)
=
(In-aQ)k+1(xW-x*)
= (l-aAmax(Q))fc+1(^°)-x*),
where in the last line we used the property that x^
Q. Taking norms on both sides, we get
— x* is an eigenvector of
|| χ (*+ι) - x*|| = |i _ aA m a x (Q)| f c + 1 \\x™ _ χ · | | .
ANALYSIS OF GRADIENT METHODS
147
Because a < 0 or a > 2/A m a x (Q),
|l-c*Amax(Q)|>l.
Hence, ||x( fc+1 ) — aj*|| cannot converge to 0, and thus the sequence {x^}
not converge to x*.
does
I
Example 8.4 Let the function / be given by
f{X)=xAi 2fL+xTM+24.
We wish to find the minimizer of / using a fixed-step-size gradient algorithm
x(k+i) = x ( fc )
_
aV/(x
W)i
where a G R is a fixed step size.
To apply Theorem 8.3, we first symmetrize the matrix in the quadratic
term of / to get
/(*) =
\xT
8
2\/2
2v^
10 x + x
+ 24.
The eigenvalues of the matrix in the quadratic term are 6 and 12. Hence,
using Theorem 8.3, the algorithm converges to the minimizer for all x^ if
and only if a lies in the range 0 < a < 2/12.
I
Convergence Rate
We now turn our attention to the issue of convergence rates of gradient algorithms. In particular, we focus on the steepest descent algorithm. We first
present the following theorem.
Theorem 8.4 In the method of steepest descent applied to the quadratic function, at every step k we have
(Q)
v
''
D
Proof In the proof of Theorem 8.2, we showed that 7^ > A m i n (Q)/A m a x (Q).
Therefore,
V(xW) - F(*( fc+1 >)
^ A min (Q)
— = 7fe > Amax(Q)
V(x(k))
148
GRADIENT METHODS
and the result follows.
I
Theorem 8.4 is relevant to our consideration of the convergence rate of the
steepest descent algorithm as follows. Let
r=^
^
= IIQIIIIQ- 1 ll,
called the condition number of Q. Then, it follows from Theorem 8.4 that
n*(fc+1)) < (i - 1 )
v(x^).
The term (1 — 1/r) plays an important role in the convergence of
{V(x^)}
to 0 (and hence of {x^} to x*). We refer to (1 — 1/r) as the convergence
ratio. Specifically, we see that the smaller the value of (1 — 1/r), the smaller
y(#(*+!)) will be relative to V(x^),
and hence the "faster" V(x^) converges
to 0, as indicated by the inequality above. The convergence ratio (1 — 1/r)
decreases as r decreases. If r = 1, then A max (Q) = A m i n (Q), corresponding to
circular contours of / (see Figure 8.6). In this case the algorithm converges
in a single step to the minimizer. As r increases, the speed of convergence of
{V(x^)}
(and hence of {x^}) decreases. The increase in r reflects that fact
that the contours of / are more eccentric (see, e.g., Figure 8.7). We refer the
reader to [88, pp. 238, 239] for an alternative approach to the analysis above.
To investigate the convergence properties of {x^} further, we need the
following definition.
Definition 8.1 Given a sequence {x^}
that converges to x*, that is,
limfc^oo ||x(fc) — a5*|| = 0, we say that the order of convergence is p, where
p e R, if
0 < hm V T M
If for all p > 0,
k^oo
,.
hm
\\xW
^ < °°-
-x*\\P
||χ(* +1 > - x*
fc-^oo \\x(k)
-X*\\P
then we say that the order of convergence is oo.
I
Note that in the definition above, 0/0 should be understood to be 0.
The order of convergence of a sequence is a measure of its rate of convergence; the higher the order, the faster the rate of convergence. The order of
convergence is sometimes also called the rate of convergence (see, e.g., [96]).
If p = 1 (first-order convergence) and limfc-+oc ||χ^ +1 ^ — x*||/||a5^ —x*\\ = 1,
we say that the convergence is sublinear. If p = 1 and lim^oo ||a;(fc+1) —
a;*||/||aj(fc) — x*|| < 1, we say that the convergence is linear. If p > 1, we say
that the convergence is superlinear. If p = 2 (second-order convergence), we
say that the convergence is quadratic.
ANALYSIS OF GRADIENT METHODS
Example 8.5
1. Suppose that x^
= l/k and thus x^
149
—> 0. Then,
|g(fc+i)| _ l/(fc + l) _ k?
\x(V\p ~
1/kP
~ k + 1'
If p < 1, the sequence above converges to 0, whereas if p > 1, it grows to
oo. If p = 1, the sequence converges to 1. Hence, the order of convergence
is 1 (i.e., we have linear convergence).
2. Suppose that x^
= 7fc, where 0 < 7 < 1, and thus x^
|„.(fc+l)|
-.fc+1
|x(fc)|p
(7fc)P
J
—> 0. Then,
J
If p < 1, the sequence above converges to 0, whereas if p > 1, it grows
to 00. If p = 1, the sequence converges to 7 (in fact, remains constant at
7). Hence, the order of convergence is 1.
3. Suppose that x^
Then,
— 7 ^ \ where q > 1 and 0 < 7 < 1, and thus x^
u(fc+i)i
~(<7fc+1)
\XW\P
(<y(Qk))P
,
fc+1
,
fcx
1
.
—► 0.
k
1
If p < q, the sequence above converges to 0, whereas if p > q, it grows to
00. If p = g, the sequence converges to 1 (in fact, remains constant at 1).
Hence, the order of convergence is q.
4. Suppose that x^
= 1 for all &, and thus x^
—► 1 trivially. Then,
|s(fc+i)_l| _ 0 _ Q
\x(k) _ I|P " OP ~
for all p. Hence, the order of convergence is 00.
The order of convergence can be interpreted using the notion of the order
symbol O, as follows. Recall that a = O(h) ("big-oh of ft") if there exists
a constant c such that \a\ < c\h\ for sufficiently small h. Then, the order of
convergence is at least p if
\\x^k+V -x*\\=0(\\x{k)
-χ*ψ)
(see Theorem 8.5 below). For example, the order of convergence is at least 2
if
\\xtk+l)-x*\\=0(\\x{k)
-x*\\2)
(this fact is used in the analysis of Newton's algorithm in Chapter 9).
150
GRADIENT METHODS
Theorem 8.5 Let {x^}
be a sequence that converges to x*. / /
\\x^1)^X*\\=0(\\xik)-X*\\p),
then the order of convergence (if it exists) is at least p.
Proof Let s be the order of convergence of {x^}.
□
Suppose that
\\χ&+ν-χ*\\=0(\\χΜ-χ*\\ρ).
Then, there exists c such that for sufficiently large k,
||<Β<*+Ι)_Χ*"
x
(fc) _
x*\\p
<C.
Hence,
ll*«^)-**!!
e
l l ^ ^ - ^ lp l ^ w . ^ p - a
||x(fc) - a j * | |
\\x^
-x*\\
-x*\\p~s.
<c\\x^
Taking limits yields
||aj( fc + 1 ) -
x*\\
lim ^
V 1 < c lim ||*(*> - * Τ " * .
fc-^oo ||a;(*0 -x*\\s
~
fc-oo"
"
Because by definition s is the order of convergence,
||a;(*+i)_
fc^oo
k
\\X( )
χ*||
-X*\\s
Combining the two inequalities above, we get
c lim \\x^
k—>oo
-x*\\p-s>0.
Therefore, because lim^oo \\x^ — x*\\ — 0, we conclude that s > p; that is,
the order of convergence is at least p.
I
By an argument similar to the above, we can show that if
||a;(fc+1)-»*||=o(||«(fc)-a5*||p),
then the order of convergence (if it exists) strictly exceeds p.
Example 8.6 Suppose that we are given a scalar sequence {x^}
verges with order of convergence p and satisfies
hm
|x(*+i)_2|
Λ
, ,Μ——-z- = 0.
that con-
ANALYSIS OF GRADIENT METHODS
151
The limit of {x^} must be 2, because it is clear from the equation that
|x(fc+i) — 2| —► 0. Also, we see that | χ ^ + 1 ) - 2| = ο(|ζ<*> - 2| 3 ). Hence, we
conclude that p > 3.
I
It turns out that the order of convergence of any convergent sequence cannot be less than 1 (see Exercise 8.3). In the following, we provide an example
where the order of convergence of a fixed-step-size gradient algorithm exceeds
1.
Example 8.7 Consider the problem of finding a minimizer of the function
/ : R -> R given by
Suppose that we use the algorithm x^k+l^ — x^ — aff(x^)
with step size
a = 1/2 and initial condition x^ = 1. (The notation / ' represents the
derivative of /.)
We first show that the algorithm converges to a local minimizer of / . Indeed, we have f'{x) = 2x — x2. The fixed-step-size gradient algorithm with
step size a = 1/2 is therefore given by
χ(*+υ=χ(*)_α/'(χ(*))
=
Ι( χ (*))2.
With χ(°ϊ = 1, we can derive the expression x^ — (1/2) 2
algorithm converges to 0, a strict local minimizer of / .
Next, we find the order of convergence. Note that
-1
. Hence, the
|a?(fc+1>| _ 1
|χ(*0|2 ~ 2'
Therefore, the order of convergence is 2.
I
Finally, we show that the steepest descent algorithm has an order of convergence of 1 in the worst case; that is, there are cases for which the order of
convergence of the steepest descent algorithm is equal to 1. To proceed, we
will need the following simple lemma.
Lemma 8.3 In the steepest descent algorithm, if g^
7fc = 1 if and only if g^ is an eigenvector of Q.
Proof. Suppose that g^
algorithm,
φ 0 for all k, then
□
φ 0 for all k. Recall that for the steepest descent
(gwyfc>)2
(g( fc ) T Qg( fe ))(g( fe ) T Q- 1 g( fc ))'
Sufficiency is easy to show by verification. To show necessity, suppose that
7fc = 1. Then, V{x^k+1^) = 0, which implies that x^k+i^> = x*. Therefore,
lk
x* = x{k) -
akg{k).
152
GRADIENT METHODS
Premultiplying by Q and subtracting b from both sides yields
0=
gW-akQgW,
which can be rewritten as
QgW = - l f l «
Oik
Hence, gW is an eigenvector of Q.
I
By the lemma, if g^ is not an eigenvector of Q, then 7& < 1 (recall that
7fc cannot exceed 1). We use this fact in the proof of the following result on
the worst-case order of convergence of the steepest descent algorithm.
Theorem 8.6 Let {x^}
be a convergent sequence of iterates of the steepest
descent algorithm applied to a function f. Then, the order of convergence of
{x^}
is 1 in the worst case; that is, there exist a function f and an initial
condition x^ such that the order of convergence of {x^} is equal to 1. D
Proof Let / : R n —» R be a quadratic function with Hessian Q. Assume that
the maximum and minimum eigenvalues of Q satisfy A max (Q) > Xmin(Q). To
show that the order of convergence of {x^} is 1, it suffices to show that there
exists χ(°) such that
||a5<fc+1>-x*H > c | | x ( f c ) - x * | |
for some c > 0 (see Exercise 8.2). Indeed, by Rayleigh's inequality,
y ( > + D ) = i ( a ^ + 1 ) - * * ) T Q ( a ^ + 1 ) - x*)
<Xm^Q)\\X(k+V-x*\\2.
Similarly,
V
^ k )
)
> ^ ^
l l x
( k ) _
x
^ 2
m
2
Combining the inequalities above with Lemma 8.1, we obtain
Therefore, it suffices to choose x^ such that 7^ < d for some d < 1.
Recall that for the steepest descent algorithm, assuming that g^ φ 0 for
all fc, 7fc depends on g^ according to
(<7 ( / c ) V f c ) ) 2
Ik = (g(VTQg(k))(g(k)TQ-lg(k)}
EXERCISES
153
First consider the case where n = 2. Suppose that χ(°> Φ x* is chosen such
that χ(°ϊ — x* is not an eigenvector of Q. Then, gW = Q(x^ — x*) φ 0 is
also not an eigenvector of Q. By Proposition 8.1, gW = (x( fc+1 ) —x^)/ak
is
not an eigenvector of Q for any k [because any two eigenvectors corresponding
to A max (Q) and A m i n (Q) are mutually orthogonal]. Also, g^ lies in one of
two mutually orthogonal directions. Therefore, by Lemma 8.3, for each k, the
value of 7^ is one of two numbers, both of which are strictly less than 1. This
proves the n = 2 case.
For the general n case, let V\ and V2 be mutually orthogonal eigenvectors
corresponding to A max (Q) and A m i n (Q). Choose x^ such that x^
—x*^0
lies in the span of v\ and v% but is not equal to either. Note that g^ =
Q(x^ — x*) also lies in the span of V\ and V2, but is not equal to either.
By manipulating cc(fc+1) = x^ — akg^ as before, we can write </(fc+1) =
(I — OLkQ)g^'. Any eigenvector of Q is also an eigenvector of I — a^Q.
Therefore, gW lies in the span of v\ and v2 for all k; that is, the sequence
{g^} is confined within the two-dimensional subspace spanned by v\ and
V2. We can now proceed as in the n = 2 case.
I
In the next chapter we discuss Newton's method, which has order of convergence at least 2 if the initial guess is near the solution.
EXERCISES
8.1 Perform two iterations leading to the minimization of
/ O i , x 2 ) = xi + 2 X 2 + 9 X i + x 2 +
3
using the steepest descent method with the starting point x^
determine an optimal solution analytically.
= 0. Also
8.2 Let {x^} be a sequence that converges to cc*. Show that if there exists
c > 0 such that
||^+1)_χ*||
>c\\xW-X*\\*
for sufficiently large /c, then the order of convergence (if it exists) is at most
p.
8.3 Let {x^} be a sequence that converges to x*. Show that there does not
exist p < 1 such that
b m ' - ^ " - - ; ' > t t
fc-+oo
8.4 Consider the sequence {x^}
\\x(k)
-X*\\P
given by x^
=2
2
.
154
GRADIENT METHODS
a. Write down the value of the limit of
b . Find the order of convergence of
{x^}.
{x^}.
8.5 Consider the two sequences {x^}
lows:
and {y^}
defined iteratively as fol-
x(k+i)=ax(k)^
where a G R, b G R, 0 < a < 1, b > 1, χ^
a. Derive a formula for x^
x<*> -» 0.
φ 0, y^
φ 0, and \y^>\ < 1.
in terms of x^
and a. Use this to deduce that
b . Derive a formula for yW in terms of y^
yW _, 0.
and b. Use this to deduce that
c. Find the order of convergence of {x^}
and the order of convergence of
{y{k)}.
d. Calculate the smallest number of iterations k such that \x^\ < c | x ^ | ,
where 0 < c < 1.
Hint: The answer is in terms of a and c. You may use the notation \z\
to represent the smallest integer not smaller than z.
e. Calculate the smallest number of iterations k such that \y^\
where 0 < c < 1.
< c|?/°)|,
f. Compare the answer of part e with that of part d, focusing on the case
where c is very small.
8.6 Suppose that we use the golden section algorithm to find the minimizer
of a function. Let Uk be the uncertainty range at the kth. iteration. Find the
order of convergence of {uk}.
8.7 Suppose that we wish to minimize a function / : R —> R that has a
derivative / ' . A simple line search method, called derivative descent search
(DDS), is described as follows: given that we are at a point x^h\ we move
in the direction of the negative derivative with step size a; that is, x^k+1^ =
k
where a > 0 is a constant.
x(k) _ af'(x( )^
In the following parts, assume that / is quadratic: f(x) = \ax2 — bx + c
(where a, 6, and c are constants, and a > 0).
a. Write down the value of x* (in terms of a, 6, and c) that minimizes / .
EXERCISES
155
b . Write down the recursive equation for the DDS algorithm explicitly for
this quadratic / .
c. Assuming that the DDS algorithm converges, show that it converges to
the optimal value x* (found in part a).
d. Find the order of convergence of the algorithm, assuming that it does
converge.
e. Find the range of values of a for which the algorithm converges (for this
particular / ) for all starting points x^.
8.8 Consider the function
f(x) = 3{χ2λ + x\) + 4xix 2 + 5xi + 6x 2 + 7,
where x = [xi,x 2 ] T £ I^2· Suppose that we use a fixed-step-size gradient
algorithm to find the minimizer of / :
x(W)=x(*)-QV/(x(fc)).
Find the largest range of values of a for which the algorithm is globally convergent.
8.9 This exercise explores a zero-finding algorithm.
Suppose that we wish to solve the equation h(x) = 0, where
. , ,
|~4 + 3χι + 2χ 2 1
h(x)
v J = \
[l + 2xi+3x2J
Consider using an algorithm of the form cc^ +1 ^ — x^
is scalar constant that does not depend on k.
— ah(x^),
where a
a. Find the solution of h(x) = 0.
b . Find the largest range of values of a such that the algorithm is globally
convergent to the solution of h(x) = 0.
c. Assuming that a is outside the range of values in part b, give an example
of an initial condition x^ of the form [xi, 0] T such that the algorithm is
guaranteed not to satisfy the descent property.
8.10 Consider the function / : R 2 —► R given by
3
f(x) = -{x\ + x\) + (1 + a)xix2 - (xi + x 2 ) + b,
156
GRADIENT METHODS
where a and b are some unknown real-valued parameters.
a. Write the function / in the usual multivariable quadratic form.
b . Find the largest set of values of a and b such that the unique global
minimizer of / exists, and write down the minimizer (in terms of the
parameters a and b).
c. Consider the following algorithm:
5
Find the largest set of values of a and b for which this algorithm converges
to the global minimizer of / for any initial point χ(°\
8.11 Consider the function / : R -+ R given by f(x) = \{x - c) 2 , c G R. We
are interested in computing the minimizer of / using the iterative algorithm
x(^)=x(V-akf(xW),
where / ' is the derivative of / and ak is a step size satisfying 0 < o^ < 1.
a. Derive a formula relating /(x^ fc+1 ^) with f(x^),
involving α^.
b . Show that the algorithm is globally convergent if and only if
oo
Σα*= °°·
k=0
Hint: Use part a and the fact that for any sequence {a^} C (0,1), we
have
oo
oo
k=0
fc=0
J | ( l - a f c ) = 0<^>^a f c = oo.
8.12 Consider the function / : R —► R given by f(x) = x3 — x. Suppose that
we use a fixed-step-size algorithm x(fc+1) = x^ — aff(x^)
to find a local
minimizer of / . Find the largest range of values of a such that the algorithm
is locally convergent (i.e., for all XQ sufficiently close to a local minimizer #*,
we have x^ —► x*).
8.13 Consider the function / given by f(x) = (x — l ) 2 , x G R. We are
interested in computing the minimizer of / using the iterative algorithm
/c /
x(k+i) — x(k) _ a 2 ~ / ( x ^ ^ ) , where / ' is the derivative of / and 0 < a < 1.
Does the algorithm have the descent property? Is the algorithm globally
convergent?
EXERCISES
157
8.14 Let / : R —> R, / G C3, with first derivative / ' , second derivative / " ,
and unique minimizer x*. Consider a fixed-step-size gradient algorithm
X(*+D
= XW _
a/'(a.(*)).
Suppose that f"(x*) φ 0 and a = l/f"(x*).
Assuming that the algorithm
converges to #*, show that the order of convergence is at least 2.
8.15 Consider the problem of minimizing f(x) = \\ax — 6|| 2 , where a and b
are vectors in R n , and a φ 0.
a. Derive an expression (in terms of a and b) for the solution to this problem.
b . To solve the problem, suppose that we use an iterative algorithm of the
form
a-ik+D = x(k) _ a / ' ^ W ) ,
where f is the derivative of / . Find the largest range of values of a (in
terms of a and 6) for which the algorithm converges to the solution for
all starting points x^°\
8.16 Consider the optimization problem
minimize
\\Ax — 6|| 2 ,
where A e R m X n , m > n, and b G R m .
a. Show that the objective function for this problem is a quadratic function,
and write down the gradient and Hessian of this quadratic.
b . Write down the fixed-step-size gradient algorithm for solving this optimization problem.
c. Suppose that
Find the largest range of values for a such that the algorithm in part b
converges to the solution of the problem.
8.17 Consider a function / : R n -» R n given by f(x) = Ax + i>, where
A G R n x n and b G R n . Suppose that A is invertible and x* is the zero of /
[i.e., f(x*) — 0]. We wish to compute x* using the iterative algorithm
where a S 1 , a > 0. We say that the algorithm is globally monotone if for
any χ(°>, ||x(fc+!) - x*|| < ||xW - x*|| for all Jfe.
158
GRADIENT METHODS
a. Assume that all the eigenvalues of A are real. Show that a necessary
condition for the algorithm above to be globally monotone is that all the
eigenvalues of A are nonnegative.
Hint: Use contraposition.
b . Suppose that
A =
3
2
2
3
b =
3
-1
Find the largest range of values of a for which the algorithm is globally
convergent (i.e., x^ —> x* for all x^).
8.18 Let / : R n -► R be given by f(x) = \χΎQx - xTb, where b <E R n and
Q is a real symmetric positive definite n x n matrix. Suppose that we apply
the steepest descent method to this function, with χ^ φ Q~xb. Show that
the method converges in one step, that is, x^ — Q~1b, if and only if x^ is
chosen such that g^ = Qx^ — b is an eigenvector of Q.
8.19 Suppose that we apply the steepest descent algorithm cc^+i) _ x(k) _
&k9^ to a quadratic function / with Hessian Q > 0. Let A max and Amin be
the largest and smallest eigenvalue of Q, respectively. Which of the following
two inequalities are possibly true? (When we say here that an inequality is
"possibly" true, we mean that there exists a choice of / and x^ such that
the inequality holds.)
a. a0 > 2/A max .
b . a0 > 1/Amin.
8.20 Suppose that we apply a fixed-step-size gradient algorithm to minimize
f{x) = xT
"3/2
2 '
0
3/2
x + xT
' 3"
-1
-22.
a. Find the range of values of the step size for which the algorithm converges
to the minimizer.
b . Suppose that we use a step size of 1000 (which is too large). Find an
initial condition that will cause the algorithm to diverge (not converge).
8.21 Consider a fixed-step-size gradient algorithm applied to each of the
functions / : R 2 —> R in parts a and b below. In each case, find the largest
range of values of the step size a for which the algorithm is globally convergent.
a. f(x) = 1 + 2xi + Z(x\ + xl) + 4xix 2 .
EXERCISES
b. f(x) -=O x. T
3
1
159
3
χ + [16,23]χ + π 2
3
8.22 Let / : R n -+ R be given by f{x) = \χΎQx - xTb, where b G R n and
Q is a real symmetric positive definite n x n matrix. Consider the algorithm
where £<*> = Qx^ - b, ak = 9{k)T g(k) / g{k)T Qg{k\ and β € R is a given
constant. (Note that the above reduces to the steepest descent algorithm if
β = 1.) Show that { a ^ ) } converges to x* = Q~lb for any initial condition
χ<°) if and only if 0 < β < 2.
8.23 Let / : R n -> R be given by / ( « ) = \xTQx - xTb, where 6 G R n
and Q is a real symmetric positive definite nxn matrix. Consider a gradient
algorithm
x(k+i)
=
x(k)
_
akg(k)^
where g^ = Qx^ — b is the gradient of / at x^ and ak is some step size.
Show that the algorithm has the descent property [i.e., f(x^k+1^) < f(x^k>})
whenever g^ φ 0] if and only if 7/~ > 0 for all k.
8.24 Given / : R n —> R, consider the general iterative algorithm
x(k+i) = xw
+ afcd(fc)5
where (Γλ\(Γ2\...
are given vectors in R n and α& is chosen to minimize
(fc)
/(xW+ad
);thatis,
ak = argmin/(ic ( / c ) +
Show that for each &, the vector x( fc+1 ) — x^
(assuming that the gradient exists).
ad{k)).
is orthogonal to V/(x^ fc+1 ^)
8.25 Write a simple MATLAB program for implementing the steepest descent algorithm using the secant method for the line search (e.g., the MATLAB function of Exercise 7.11). For the stopping criterion, use the condition
HiJ^II < ε, where ε — 1 0 - 6 . Test your program by comparing the output
with the numbers in Example 8.1. Also test your program using an initial
condition of [—4,5,1]T, and determine the number of iterations required to
satisfy the stopping criterion. Evaluate the objective function at the final
point to see how close it is to 0.
8.26 Apply the MATLAB program from Exercise 8.25 to Rosenbrock's function:
/(x) = 100(x2-z?)2 + ( l - z i ) 2 .
Use an initial condition of x^ = [—2, 2] T . Terminate the algorithm when the
norm of the gradient of / is less than 1 0 - 4 .
CHAPTER 9
NEWTON'S METHOD
9.1
Introduction
Recall that the method of steepest descent uses only first derivatives (gradients) in selecting a suitable search direction. This strategy is not always
the most effective. If higher derivatives are used, the resulting iterative algorithm may perform better than the steepest descent method. Newton's
method (sometimes called the Newton-Raphson method) uses first and second
derivatives and indeed does perform better than the steepest descent method
if the initial point is close to the minimizer. The idea behind this method is
as follows. Given a starting point, we construct a quadratic approximation to
the objective function that matches the first and second derivative values at
that point. We then minimize the approximate (quadratic) function instead
of the original objective function. We use the minimizer of the approximate
function as the starting point in the next step and repeat the procedure iteratively. If the objective function is quadratic, then the approximation is exact,
and the method yields the true minimizer in one step. If, on the other hand,
the objective function is not quadratic, then the approximation will provide
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
161
162
NEWTON'S METHOD
f.q
Current Point
Predicted Minimizer*
x(k + i) · .
Figure 9.1
Quadratic approximation to the objective function using first and
second derivatives.
only an estimate of the position of the true minimizer. Figure 9.1 illustrates
this idea.
We can obtain a quadratic approximation to the twice continuously differentiable objection function / : R n —► R using the Taylor series expansion of /
about the current point x^k\ neglecting terms of order three and higher. We
obtain
f(x) « /(x<*>) + (a: - *(*>) V f c ) + \(x ~ x(k))TF{x^)(x
where, for simplicity, we use the notation g^
FONC to q yields
0 = Wq{x) = g{k) + F(x^)(x
If F(x^)
= Vf(x^).
- *<*>) = q(x),
Applying the
- *<*>).
> 0, then q achieves a minimum at
a .(*+i) =a .(fc)_jF( a .(fc))-i^(fc).
This recursive formula represents Newton's method.
Example 9.1 Use Newton's method to minimize the Powell function:
/ ( x i , x 2 , ^ 3 , ^ 4 ) = {x\ + IOX2)2 + 5(x 3 - X4)2 + (x2 - 2χβ) 4 + 10(xi
Use as the starting point x^ = [3, —1,0,1] T . Perform three iterations.
Note that / ( x ( 0 ) ) = 215. We have
V/(a)
2(xi + 10x2) + 40(xi - x4)3 '
20(xi + 10x2) + 4(x2 - 2x3)3
10(a?3 - X4) ~ 8(^2 - 2 x 3 ) 3
—10(0:3 - X4) - 40(xi - X4) 3
-Xi)4·
INTRODUCTION
163
and F(x) is given by
2 + 120(xi - x 4 ) 2
20
0
-120(xi-z4)2
2
2
200 + 12(x2 - 2x 3 )
-24(x 2 - 2x 3 )
0
20
-24(x 2 - 2x 3 ) 2 10 + 48(x2 - 2x3)2
-10
0
0
-10
10 + 120(ari - x 4 ) 2 .
-120(xi - x4)2
Iteration 1
^(°) = [ 3 0 6 , - 1 4 4 , - 2 , - 3 1 0 ] T ,
' 482
20
F(*(°>) =
0
-480
20
212
-24
0
" 0.1126
-0.0089
0.0154
_ 0.1106
(0)N-1
*V U >)
0
-24
58
-10
-0.0089
0.0057
0.0008
-0.0087
-480"
0
-10
490
0.0154
0.0008
0.0203
0.0155
0.1106
-0.0087
0.0155
0.1107
F ( a j ( 0 ) ) - V 0 ) = [1.4127,-0.8413,-0.2540,0.7460] T .
Hence,
XW
f(x™)
=x(o)
_ F ( a : ( 0 ) ) - V 0 ) = [1-5873,-0.1587,0.2540,0.2540] T ,
= 31.8.
Iteration 2
0 ( 1 ) = [94.81,-1.179,2.371,-94.81] T ,
F(x™)
=
215.3
20
0
-213.3
20
205.3
-10.67
0
0
-10.67
31.34
-10
-213.3
0
-10
223.3
F i a j ' ^ J - V 1 ' = [0.5291,-0.0529,0.0846,0.0846] T .
Hence,
x (2) = x (
f(x&)
1
) _^(χ(ΐ))-ι^(ΐ)
= 6.28.
=
[1.0582,-0.1058,0.1694,0.1694] T ,
164
NEWTON'S METHOD
Iteration 3
gW = [28.09, -0.3475,0.7031, -28.08] T ,
Γ 96.80
f(x&)
0
202 4
-4.744
0
-4.744
19.49
[-94.80
0
-10
r,/ (2U
F(xK { })J = \
x (3)
20
20
·
-94.80]
0
-10
104.80j
= [0.7037,-0.0704,0.1121,0.1111] T ,
= 1.24.
■
Observe that the kth iteration of Newton's method can be written in two
steps as
1. Solve F(xW)d{k)
= -gW
for
d{k\
2. S e t x ^ + 1 ) = ^ ) + d ( / c ) .
Step 1 requires the solution of an n x n system of linear equations. Thus, an
efficient method for solving systems of linear equations is essential when using
Newton's method.
As in the one-variable case, Newton's method can also be viewed as a
technique for iteratively solving the equation
g(x) = 0,
where a ; G l n and g : R n —► R n . In this case F(x) is the Jacobian matrix of
g at x\ that is, F(x) is the n x n matrix whose (i,j) entry is
(dgi/dxj)(x),
ij = 1 , 2 , . . . , n .
9.2
Analysis of Newton's Method
As in the one-variable case there is no guarantee that Newton's algorithm
heads in the direction of decreasing values of the objective function if
F(x^)
is not positive definite (recall Figure 7.7 illustrating Newton's method for
functions of one variable when / " < 0). Moreover, even if F(x^)
> 0,
Newton's method may not be a descent method; that is, it is possible that
/(x( f c + 1 )) > f(x^).
For example, this may occur if our starting point x^ is
far away from the solution. See the end of this section for a possible remedy
to this problem. Despite these drawbacks, Newton's method has superior
convergence properties when the starting point is near the solution, as we
shall see in the remainder of this section.
The convergence analysis of Newton's method when / is a quadratic function is straightforward. In fact, Newton's method reaches the point x* such
ANALYSIS OF NEWTON'S METHOD
that V/(aj*) = 0 in just one step starting from any initial point χ(°\
this, suppose that Q — QT is invertible and
f(x) = -xTQx
Then,
To see
— xTb.
g(x) = Vf(x) =
and
165
Qx-b
F(x) = Q.
Hence, given any initial point χ(°\ by Newton's algorithm
x(i)=a.(o)_jF(a.(o))-i^(o)
1
=
=
XW-Q-
[QXW-b]
1
Qb
= x*.
Therefore, for the quadratic case the order of convergence of Newton's algorithm is oo for any initial point x^ (compare this with Exercise 8.18, which
deals with the steepest descent algorithm).
To analyze the convergence of Newton's method in the general case, we
use results from Section 5.1. Let {x^} be the Newton's method sequence
for minimizing a function / : R n —► R. We show that {x^} converges to the
minimizer x* with order of convergence at least 2.
T h e o r e m 9.1 Suppose that f G C3 and x* G W1 is a point such that
V/(a?*) = 0 and F(x*) is invertible. Then, for all x^ sufficiently close
to x*, Newton's method is well-defined for all k and converges to x* with an
order of convergence at least 2.
D
Proof. The Taylor series expansion of V / about x^
V / ( « ) - V / ( x ( 0 ) ) - F(x^)(x
- x^)
yields
= 0(\\x - x ( 0 ) || 2 ).
Because by assumption f £ C3 and F(x*) is invertible, there exist constants
ε > 0, C\ > 0, and c2 > 0 such that if χ(°\χ G {x : \\x — x*|| < ε}, we have
||V/(a?) - V / ( x ( 0 ) ) - F(x^)(x
and by Lemma 5.3, F(x)~1
- x^)\\
< cx\\x -
a^f
exists and satisfies
\\F(x)-l\\<c2.
The first inequality above holds because the remainder term in the Taylor
series expansion contains third derivatives of / that are continuous and hence
bounded on {x : \\x — x*\\ < ε}.
166
NEWTON'S METHOD
Suppose that x^ e {x : \\x — x*\\ < ε}. Then, substituting x = x* in the
inequality above and using the assumption that V/(x*) = 0, we get
\\F(xW)(x<® - x*) - V/(ir ( 0 ) )|| < ci||x ( 0 ) - x*|| 2 .
Now, subtracting x* from both sides of Newton's algorithm and taking norms
yields
\\χ™ - x*\\ = ||x (0) - x* 0
Fix^^Vfix^W
1
= | | F ( x ( ) ) - ( F ( x ^ ) ( x ^ - x*) - V / ( x ( 0 ) ) ) | |
- x*) - V / ( x ( 0 ) ) | | .
< WFix^y^lWFix^ix^
Applying the inequalities above involving the constants c\ and c2 gives
\\χΜ-χ*\\
Suppose that x^
<cic2||x(0)-ir*||2.
is such that
||*(0)-*!<—,
ClC2
where a € (0,1). Then,
||a; ( 1 ) -a;*|| < a||x ( 0 ) - x*\\.
By induction, we obtain
||a.(*+i) - os*|| < ciC2||a:(ls) - aj*|| 2 ,
Ua-ifc+i) — as*|| < a\\xw
Hence,
- x*\\.
lim ||x<*> - sc*|| = 0,
and therefore the sequence {x^} converges to x*. The order of convergence
is at least 2 because \\x<<k+l>>-x*|| < cic 2 ||x ( / c ) -ir*|| 2 ; that is, | | x ^ + 1 ) - x * | | =
0(||xW-x*|| 2 ).
■
Warning: In the Theorem 9.1, we did not state that x* is a local minimizer.
For example, if x* is a local maximizes then provided that / G C3 and F(x*)
is invertible, Newton's method would converge to x* if we start close enough
to it.
As stated in Theorem 9.1, Newton's method has superior convergence properties if the starting point is near the solution. However, the method is not
guaranteed to converge to the solution if we start far away from it (in fact, it
may not even be well-defined because the Hessian may be singular). In particular, the method may not be a descent method; that is, it is possible that
ANALYSIS OF NEWTON'S METHOD
167
/(ic( fc+1 )) > f(x^).
Fortunately, it is possible to modify the algorithm such
that the descent property holds. To see this, we need the following result.
Theorem 9.2 Let {x^}
be the sequence generated by Newton's method for
minimizing a given objective function f(x). If the Hessian F(x^)
> 0 and
g{k) _ v / ( a ; ^ ) ) φ 0, then the search direction
<*(*> = - F ( * < * > ) - V f c ) = *(/c+1) - *{k)
from x^ to χ^+^ is a descent direction for f in the sense that there exists
an ä > 0 such that for all a G (0, ä ) ,
f(x(k)+ad{k))<f(x^).
D
Proof Let
(j){a) = f{x{k)
+adw).
Then, using the chain rule, we obtain
φ'(α) = Vf{x {k) +
ad{k))Jd{k\
Hence,
<//(()) = Vf(x{k))Td{k)
= -0( fc > T F(a5< fc >)-V fc) < 0,
1
because F(x^)~
> 0 and g^ φ 0. Thus, there exists an ä > 0 so that for
all a G (0, α), φ(α) < φ(0). This implies that for all a G (0, ä),
f(xW+adW)<f(x^),
which completes the proof.
I
Theorem 9.2 motivates the following modification of Newton's method:
x(k+D=x(k)_akF{x(k)rig(k)y
where
ak = axgmin/(x<fc> - aF(a: ( f c ) )-^ ( f c ) );
a>0
that is, at each iteration, we perform a line search in the direction
-F(x<*>)-V f c ) · By Theorem 9.2 we conclude that the modified Newton's
method has the descent property; that is,
/(*( fc+i >) < /(*<*>)
whenever gW φ 0.
A drawback of Newton's method is that evaluation of F(x^)
for large n
can be computationally expensive. Furthermore, we have to solve the set of
n linear equations F(x^)d^
' = —g^k\ In Chapters 10 and 11 we discuss
methods that alleviate this difficulty.
Another source of potential problems in Newton's method arises from the
Hessian matrix not being positive definite. In the next section we describe a
simple modification of Newton's method to overcome this problem.
168
9.3
NEWTON'S METHOD
Levenberg-Marquardt Modification
If the Hessian matrix F(x^)
is not positive definite, then the search direction
(Γ ' = — F(x^)~1g^
may not point in a descent direction. A simple technique to ensure that the search direction is a descent direction is to introduce
the Levenberg-Marquardt modification of Newton's algorithm:
x<*+D = XW - (F(xW)
+ /ifc/rV0,
where μ^ > 0.
The idea underlying the Levenberg-Marquardt modification is as follows.
Consider a symmetric matrix F, which may not be positive definite. Let
λ ι , . . . , λ η be the eigenvalues of F with corresponding eigenvectors v i , . . . , vn.
The eigenvalues λ ι , . . . , λ η are real, but may not all be positive. Next, consider
the matrix G — F + μΐ, where μ > 0. Note that the eigenvalues of G are
λι + μ , . . . , λ η + μ. Indeed,
Gvi = {F + μΙ)υ{
= Fvi + μΐνι
= XiVi + μνί
which shows that for all i = 1 , . . . , n, Vi is also an eigenvector of G with
eigenvalue λζ + μ. Therefore, if μ is sufficiently large, then all the eigenvalues
of G are positive and G is positive definite. Accordingly, if the parameter μ^
in the Levenberg-Marquardt modification of Newton's algorithm is sufficiently
large, then the search direction d^ = —(F(x^) + μ/ 0 Ι)~ 1 ^^^ always points
in a descent direction (in the sense of Theorem 9.2). In this case if we further
introduce a step size α^ as described in Section 9.2,
β (*+υ
= XW - ak(F(xW)
+ M*J)-V f c ) ,
then we are guaranteed that the descent property holds.
The Levenberg-Marquardt modification of Newton's algorithm can be made
to approach the behavior of the pure Newton's method by letting μ^ —> 0.
On the other hand, by letting μ^ —► oo, the algorithm approaches a pure
gradient method with small step size. In practice, we may start with a small
value of μ& and increase it slowly until we find that the iteration is descent:
/ ( t f ^ 1 ) ) < /(*(*)).
9.4
Newton's Method for Nonlinear Least Squares
We now examine a particular class of optimization problems and the use of
Newton's method for solving them. Consider the following problem:
m
minimize
yj(rf(#))2,
2=1
NEWTON'S METHOD FOR NONLINEAR LEAST SQUARES
169
where r^ : R n —» R, i = 1 , . . . , ra, are given functions. This particular problem
is called a nonlinear least-squares problem. The special case where the rt- are
linear is discussed in Section 12.1.
E x a m p l e 9.2 Suppose that we are given m measurements of a process at
m points in time, as depicted in Figure 9.2 (here, m = 21). Let t i , . . . , t m
denote the measurement times and y i , . . . , y m the measurement values. Note
that t\ = 0 while £21 = 10- We wish to fit a sinusoid to the measurement
data. The equation of the sinusoid is
y = Asm(ujt + φ)
with appropriate choices of the parameters A, ω, and φ. To formulate the
data-fitting problem, we construct the objective function
^ ( ^ - Α β ί η Μ ί + ^)) 2 ,
2=1
representing the sum of the squared errors between the measurement values
and the function values at the corresponding points in time. Let x — [A, ω, φ]τ
represent the vector of decision variables. We therefore obtain a nonlinear
least-squares problem with
ri(x) = 2/i - ^4sin(u;£i + φ).
Defining r = [ n , . . . , r m ] T , we write the objective function as f(x) =
r(x)Tr(x).
To apply Newton's method, we need to compute the gradient
and the Hessian of / . The j t h component of Vf(x) is
(V/M),
£(.) _*£>,(.)£(.).
dx
2=1
Denote the Jacobian matrix of r by
J(x)
fe(«)
few
&r(«)
few
Then, the gradient of / can be represented as
V/(«) =
2J(x)Tr(x).
170
NEWTON'S METHOD
2.5
2
1.5
c
1
c 0.5
E
(O
i-0.5
-1
-1.5
-2
-2.5
4
Figure 9.2
Time
Measurement data for Example 9.2.
Next, we compute the Hessian matrix of / . The (k,j)th
Hessian is given by
d2
f
dxkdxj
( g ) =
10
6
d
component of the
(df(x))
dxk \dxj
)
-έ(φ.<.>&(->)
Letting S(x) be the matrix whose (A:,j)th component is
we write the Hessian matrix as
F(x) = 2(J(x)TJ(x)
+ S(x)).
Therefore, Newton's method applied to the nonlinear least-squares problem
is given by
x(k+i) = x(k)
_ (j(x)Tj(x)
+
3(Χ))-^(Χ)ΤΓ{Χ).
EXERCISES
171
In some applications, the matrix S(x) involving the second derivatives of
the function r can be ignored because its components are negligibly small. In
this case Newton's algorithm reduces to what is commonly called the GaussNewton method:
x (fc+i) = x(k)
_
(j(x)Tj(x))-ij(x)Tr(x).
Note that the Gauss-Newton method does not require calculation of the second
derivatives of r.
E x a m p l e 9.3 Recall the data-fitting problem in Example 9.2, with
ri(x) = 2/i - Asm(u;ti + φ),
2 = 1 , . . . ,21.
The Jacobian matrix J(x) in this problem is a 21 x 3 matrix with elements
given by
(J(ic)) ( M ) = -sin(u;ti + 0),
(J(x))(i,2) = -UAcos(uU + 0),
(J(»))(i,3) = -Acos(uti + φ),
i = 1 , . . . ,21.
Using the expressions above, we apply the Gauss-Newton algorithm tofindthe
sinusoid of bestfit,given the data pairs (ti, y i ) , . . . , (tm, y m ). Figure 9.3 shows
a plot of the sinusoid of bestfitobtained from the Gauss-Newton algorithm.
The parameters of this sinusoid are: A = 2.01, ω = 0.992, and φ = 0.541. I
A potential problem with the Gauss-Newton method is that the matrix
J(x)TJ(x)
may not be positive definite. As described before, this problem
can be overcome using a Levenberg-Marquardt modification:
x{k+i) = x(k)
_ (j^Yj(x}
+
ßkIyij(xYr(xy
This is referred to in the literature as the Levenberg-Marquardt algorithm,
because the original Levenberg-Marquardt modification was developed specifically for the nonlinear least-squares problem. An alternative interpretation
of the Levenberg-Marquardt algorithm is to view the term μ^I as an approximation to S(x) in Newton's algorithm.
EXERCISES
9.1 Let / : R —> R be given by f(x) = (x — xo)4, where XQ G R is a constant.
Suppose that we apply Newton's method to the problem of minimizing / .
a. Write down the update equation for Newton's method applied to the
problem.
172
NEWTON'S METHOD
2.5
y^T^\r
2
c
1.5
J
1
c
E
3
0.5
0
(0
co
CD
,
°/
\°
2-0.5h
\
-1
"
1,5
T
I
)
-2[
^~^c
-2.5
4
Figure 9.3
Time
6
10
Sinusoid of best fit in Example 9.3.
b . Let y^ = \x^ — XQ\, where x^ is the fcth iterate in Newton's method.
Show that the sequence {y^} satisfies 2/(fc+1) = |?/ f c ).
c. Show that x^
—► XQ for any initial guess χ(°\
d. Show that the order of convergence of the sequence {x^}
in part b is 1.
e. Theorem 9.1 states that under certain conditions, the order of convergence of Newton's method is at least 2. Why does that theorem not hold
in this particular problem?
9.2 This question relates to the order of convergence of the secant method,
using an argument similar to that of the proof of Theorem 9.1.
a. Consider a function / : R —> R, / G C 2 , such that x* is a local minimizer
and f"(x*) φ 0. Suppose that we apply the algorithm x^k+l^ = x^ —
&kf'(x^)
such that {a*;} is a positive step-size sequence that converges
to l/f"(x*).
Show that if x^ —► x*, then the order of convergence of
the algorithm is superlinear (i.e., strictly greater than 1).
b . Given part a, what can you say about the order of convergence of the
secant algorithm?
EXERCISES
173
9.3 Consider the problem of minimizing f(x) = xs = (^/χ) 4 , x G l . Note
that 0 is the global minimizer of / .
a. Write down the algorithm for Newton's method applied to this problem.
b . Show that as long as the starting point is not 0, the algorithm in part a
does not converge to 0 (no matter how close to 0 we start).
9.4 Consider Rosenbrock's Function: f(x) = 100(^2— #i) 2 + (l — #i) 2 > where
x = [xi,X2] T (known to be a "nasty" function—often used as a benchmark
for testing algorithms). This function is also known as the banana function
because of the shape of its level sets.
a. Prove that [1,1] T is the unique global minimizer of / over R 2 .
b . With a starting point of [0,0l T , apply two iterations of Newton's method.
Hint:
a b
c d
i
- 1
d
1
ad — be
a
c. Repeat part b using a gradient algorithm with a fixed step size of α^
0.05 at each iteration.
9.5 Consider the modified Newton's algorithm
x(*+1)=aj(fe)-afcF(x(fc))-V*),
where α& = a r g m i n a > 0 f(x^
— aF(x^)~1g^).
Suppose that we apply
the algorithm to a quadratic function f{x) = ^xTQx — x T 6 , where Q =
Q > 0. Recall that the standard Newton's method reaches point x* such
that V/(cc*) = 0 in just one step starting from any initial point χ(°\ Does
the modified Newton's algorithm above possess the same property?
CHAPTER 10
CONJUGATE DIRECTION METHODS
10.1
Introduction
The class of conjugate direction methods can be viewed as being intermediate
between the method of steepest descent and Newton's method. The conjugate
direction methods have the following properties:
1. Solve quadratics of n variables in n steps.
2. The usual implementation, the conjugate gradient algorithm, requires no
Hessian matrix evaluations.
3. No matrix inversion and no storage of an n x n matrix are required.
The conjugate direction methods typically perform better than the method
of steepest descent, but not as well as Newton's method. As we saw from
the method of steepest descent and Newton's method, the crucial factor in
the efficiency of an iterative search method is the direction of search at each
iteration. For a quadratic function of n variables f(x) = ^xTQx — x T 6 ,
x G R n , Q = Q > 0, the best direction of search, as we shall see, is in
the Q-conjugate direction. Basically, two directions S1' and cl·2' in R n are
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
175
176
CONJUGATE DIRECTION METHODS
said to be Q-conjugate if cr 1 ' QdS1' = 0. In general, we have the following
definition.
Definition 10.1 Let Q be a real symmetric n x n matrix. The directions
d (0) , d (1) , d ( 2 ) , . . . , d ( m ) are Q-conjugate if for all i φ j , we have d{i)TQd{j) =
0.
■
Lemma 10.1 Let Q be a symmetnc positive definite n x n matrix. If the
directions d (0) , d ( 1 ) , . . . , d{k) e Rn, k < n — 1, are nonzero and Q-conjugate,
then they are linearly independent.
□
Proof. Let cto,..., ak be scalars such that
a0d{0) + a i d ( 1 ) + · · · + akSk)
= 0.
Premultiplying this equality by d>^ Q, 0 < j < A:, yields
OLjd{j)TQd{j)
= 0,
because all other terms d^TQd^
= 0, i φ j , by Q-conjugacy. But
Q = QT > 0 and d{j) φ 0; hence αά = 0, j = 0 , 1 , . . . , k . Therefore,
k < n — 1, are linearly independent.
I
Example 10.1 Let
\3 0 Γ
Q = 0 4 2
[l 2 3
Note that Q = Q > 0. The matrix Q is positive definite because all its
leading principal minors are positive:
Δι = 3 > 0,
Δ 2 = det
3 0
= 12 > 0,
0 4
Δ 3 = det Q = 20 > 0.
Our goal is to construct a set of Q-conjugate vectors c r ° \ d^\
T
2)
Let d<°> = [1,0,0] , d™ = [d?\d£\dP] ,
require that d
(0)T
T
Qd
d^ Qd^
(1)
= 0. We have
"3 0 1"
= [1,0,0] 0 4 2
1 2 3
Let 4 ! ) = 1, 4 X ) = 0> 4 X ) = - 3 d^TQd^
= 0.
Then
d^2\
d^ = [ 4 , 4 , 4 2 ) ] τ . We
T
m
4
υ
1}
4J
>
rf(1)
2)
M^+d^.
= [ 1 , 0 , - 3 ] T , and thus
THE CONJUGATE DIRECTION ALGORITHM
177
To find the third vector cl·2', which would be Q-conjugate with cl·0' and
d , we require that d (0)T Qd{2) = 0 and d(1)TQd(2) = 0. We have
(1)
d^Qd™ =3di 2 ) +4 2 ) =0,
d^TQd^
-6d{2)-8d{2)=0.
=
If we take <r2' = [1,4,—3] T , then the resulting set of vectors is mutually
conjugate.
I
This method of finding Q-conjugate vectors is inefficient. A systematic
procedure for finding Q-conjugate vectors can be devised using the idea underlying the Gram-Schmidt process of transforming a given basis of R n into
an orthonormal basis of Mn (see Exercise 10.1).
10.2
The Conjugate Direction Algorithm
We now present the conjugate direction algorithm for minimizing the
quadratic function of n variables
f(x) = -xTQx
-
xTb,
where Q = Q T > 0 , x G R n . Note that because Q > 0, the function / has a
global minimizer that can be found by solving Qx = b.
Basic Conjugate Direction Algorithm. Given a starting point
and Q-conjugate directions d^0\d^\...,
<r n _ 1 '; for k > 0,
g(k) =Vf(x(k))
= QxW
x^
-b,
fe T (fc)
g( ) d
ak
T
~~d^ QdW
Theorem 10.1 For any starting point χ(°\ the basic conjugate direction algorithm converges to the unique x* (that solves Qx = b) in n steps; that is,
χ(ηϊ=χ*.
D
Proof. Consider x* — χ(°) e R n . Because the a^ are linearly independent,
there exist constants ft, i = 0 , . . . , n — 1, such that
tf*-z(°>=/3od(0)
+ --- + /?n-id ( n - 1 ) .
Now premultiply both sides of this equation by d^k' Q, 0 < k < n, to obtain
d^TQ(x*
- *«») =
ßkSk)TQd<k\
178
CONJUGATE DIRECTION METHODS
where the terms Φ ' Q<r%' = 0, k ψ i, by the Q-conjugate property. Hence,
d(fc)TQ(x*-x<0))
ßk
dWTQd(k)
■
Now, we can write
x<*> = x<°> + a0d<°> + · · · + a ^ - i d ^ - 1 ) .
Therefore,
x<fc> - *<°> = a0d<0> + · · · + α*-!^*" 1 *.
So writing
x* - x<0> = (x* - x « ) + (x<fe> - x< 0 ')
and premultiplying the above by S ' Q, we obtain
d ( * ) T Q(x* - xW) = d<*> T Q(x· - x<*>) = - d < f c > y * \
because gW — Qx^
— b and Qx* = 6. Thus,
^fc~
and x* = χ(η\
d( f c > T Qd( f e > _ a f e
which completes the proof.
Example 10.2 Find the minimizer of
f(xi,x2)
2χΤ
=
~4 2
2
2
X
-1
-xT
1
,X €
using the conjugate direction method with the initial point χ(°) = [0,0] T , and
Q-conjugate directions d (0) = [1,0] T and d (1) = [ - § , | ] T .
We have
l(°)
-& = [ i , - i ] T ,
and hence
<* 0 =
g (°)
0 T
T
d(0)
'd< > Qd<0>
[1,-1]
[1,0]
4
2
2
2
Thus,
x( 1 ) = x ( ° ) + a 0 d ( 0 )
1 1
0
=
0 " 4 0
1
4
0
THE CONJUGATE DIRECTION ALGORITHM
179
To find x^2\ we compute
gM = Qx^
-b =
1
4
"4 2"
2 2
-1
1
-
0
and
[0,-
g(i>Td(P
oil
'd^TQd^
0
=
3
2
3"
8
3
4
§]
2"
Γ_3 3] "4
L 8' 4J 2
2.
3]
38
2
4 J
Therefore,
1"
4
x W = ^ + a i d ^( i ) =_
+2
0
3
4
3
8
=
Because / is a quadratic function in two variables, x^
For a quadratic function
reaches the solution after n
possesses a certain desirable
suppose that we start at x^
-
'
-
-1
3
2 _
= x*.
I
of n variables, the conjugate direction method
steps. As we shall see below, the method also
property in the intermediate steps. To see this,
and search in the direction er 0 ' to obtain
-
We claim that
-
(
#
g(DT d (0)
&
=
)
-
■
α
To see this,
g^Td^
= (QxW - &)Trf(°>
= x( )TQd(0)
°
=
- { $ $ * ) d(°)TQd(0) - &Td(0)
g(o)Td(o)_g(o)Td(o)=a
The equation g^Td^ ' = 0 implies that £*o has the property that ao =
arg min φο{α), where φ0(α) = /(χ(°) + a d ( 0 ) ) . To see this, apply the chain
rule to get
^da( a ) = V/(*(°> + ad(°>)T<i(0)·
Evaluating the above at a = ao, we get
d
^(a )=gWdW=0.
da o
180
CONJUGATE DIRECTION METHODS
Because φο is a quadratic function of a, and the coefficient of the a2 term in
0o is d^TQd^
> 0, the above implies that a0 = argmin a G R 0o(a).
Using a similar argument, we can show that for all A:,
0(fc+i)Td(*)
and hence
=
0
ak = argmin/(ic ( f c ) +
aSk)).
In fact, an even stronger condition holds, as given by the following lemma.
L e m m a 10.2 In the conjugate direction algorithm,
0(*+i)T d «)
o
=
for all k, 0 < k <n — 1, and 0 < i < k.
□
Proof. Note that
Q ( a ^ + 1 ) - a<*>) = Qx(k+V - b - (QxW - b) = 0<fc+1> - g™,
because g^ = Qx^ — 6. Thus,
fc
fl(*+i)=fl<O+afcQd<
>.
We prove the lemma by induction. The result is true for k = 0 because
g^Td^
= 0, as shown before. We now show that if the result is true for
k - 1 (i.e., gWTd(i) = 0, i < k - 1), then it is true for k (i.e., g(k+VTd{i) = 0,
i <k). Fix k > 0 and 0 < i < k. By the induction hypothesis, g^Td^ = 0.
Because
0< fc+1) =ff< fc) +a fc Qd<*>,
and Sk'TQdl·1'
= 0 by Q-conjugacy, we have
g(k+i)Td(i)
=
g{k)Td(i)
+ akdWrQd(i)
=
Q
It remains to be shown that
g(k+DTd(k)
=
Q
Indeed,
g(k+l)Td(k)
=
(Qx(k+1)
_
6) T d (fc)
_ ( (k) _ 9{k)Td{k)
= 0,
(k)\
(k)
_
T
(k)
THE CONJUGATE DIRECTION ALGORITHM
Figure 10.1
181
Illustration of Lemma 10.2.
because Qx^ —b — g(k\
Therefore, by induction, for all 0 < k < n — 1 and 0 < i < &,
g{k + l)Td{i)
=
Q
By Lemma 10.2 we see that ^( fc+1 ) is orthogonal to any vector from the
subspace spanned by S°\ dSl\ . . . , er \ Figure 10.1 illustrates this statement.
The lemma can be used to show an interesting optimal property of the
conjugate direction algorithm. Specifically, we now show that not only does
/(x ( f c + 1 ) ) satisfy /(x ( f c + 1 ) ) = min a /(«<*) + a d ( f c ) ) , as indicated before, but
also
/(x (fe+1) )
min /
a0,...,ak
\
x (0)
+ yZ a ^ (0
i=0
In other words, if we write
Vfc-^+span[d^,d^,...,d^],
then we can express /(α?^ +1 ^) = mina.Gyfc f(x). As k increases, the subspace
span[d ( 0 ) ,d ( 1 ) ,... ,d (fc) ] "expands," and will eventually fill the whole of R n
(provided that the vectors d^'^cl·1',...,
are linearly independent). Therefore,
for some sufficiently large k, x* will lie in Vk- For this reason, the above result
is sometimes called the expanding subspace theorem (see, e.g., [88, p. 266]).
To prove the expanding subspace theorem, define the matrix D^ by
182
CONJUGATE DIRECTION METHODS
that is, d (i) is the ith column of D{k).
+ lZ(D{k))
Note that x^
= Vk. Also,
*(*+!) = x ( 0 ) + £ a . d ( 0
= x (o) + £ ) (fc) a )
where a = [<*o,..., a/c]T· Hence,
x(fc+1)6a.(0)+^(D(fc))=Vfc.
Now, consider any vector x G V&. There exists a vector a such that x = x^ +
D^a.
Let 0&(α) = f(x^
+ D^a).
Note that </>fc is a quadratic function
and has a unique minimizer that satisfies the FONC (see Exercises 6.33 and
10.7). By the chain rule,
D<i>k(a) = V / ( * ( 0 ) + Df f c O) T D ( f c ) .
Therefore,
D0 f c (a) - V / ( * ( 0 ) +
D{k)a)TD{k)
= V/(a^+1))TJD^)
= 0(fe+1)T£>(fc).
By Lemma 10.2, g(k^)TD(k)
= 0 T . Therefore, a satisfies the FONC for the
quadratic function <^, and hence a is the minimizer of </>&; that is,
/(x^1))
=
min/(x(°) + Dwa)
a
= min / ( x ) ,
xEVk
which completes the proof of our result.
The conjugate direction algorithm is very effective. However, to use the
algorithm, we need to specify the Q-conjugate directions. Fortunately, there
is a way to generate Q-conjugate directions as we perform iterations. In
the next section we discuss an algorithm that incorporates the generation of
Q-conjugate directions.
10.3
The Conjugate Gradient Algorithm
The conjugate gradient algorithm does not use prespecified conjugate directions, but instead computes the directions as the algorithm progresses. At
each stage of the algorithm, the direction is calculated as a linear combination of the previous direction and the current gradient, in such a way that all
the directions are mutually Q-conjugate—hence the name conjugate gradient
algorithm. This calculation exploits the fact that for a quadratic function of
THE CONJUGATE GRADIENT ALGORITHM
183
n variables, we can locate the function minimizer by performing n searches
along mutually conjugate directions.
As before, we consider the quadratic function
f(x) = ];XTQx
x e Rn,
- xTb,
where Q = Q > 0. Our first search direction from an initial point x^
the direction of steepest descent; that is,
is in
d<°> = - f l <°>.
Thus,
where
O
(0)TJ(0)
In the next stage, we search in a direction d^1' that is Q-conjugate to cr 0 '.
We choose er 1 ' as a linear combination of g^ and <r°\ In general, at the
(fc + l)th step, we choose cr + 1 ' to be a linear combination of g(k+1) and d> \
Specifically, we choose
d (*+D
= _ S (*+D + ßkSk),
k = 0,1,2,....
The coefficients /?&, k = 1,2,..., are chosen in such a way that d^fc+1^ is
Q-conjugate to c r ° \ d^\ . . . , d^k\ This is accomplished by choosing ßk to be
The conjugate gradient algorithm is summarized below.
1. Set k := 0; select the initial point χ(°).
2. g(°) = V/(x<°>). Ifff<°>= 0, stop; else, set d (0) =
o.
ak
-
d(k)TQd(k)
■
4. x(fe+1> = XW + akd{k).
5. ö ( fc+1 ) = V/(x< fe+1 )). If g( fc+1 ) = 0, stop.
7. d<*+i> = _ ff (*+i)
+ / ? f c d<
fc)
.
8. Set k :— /c + 1; go to step 3.
-gW.
184
CONJUGATE DIRECTION METHODS
Proposition 10.1 In the conjugate gradient
d (0) , d ( 1 ) , . . . , d ( n _ 1 ) are Q-conjugate.
algorithm,
the
directions
D
Proof. We use induction. We first show that (T°> Qd>1' = 0. To this end we
write
Substituting for
d^TQd^
P
°
in the equation above, we see that d ( 0 ) T Q d ( 1 ) = 0.
We now assume that S°\d^\...
,<r fc \ fc < n — 1, are Q-conjugate directions. From Lemma 10.2 we have gfr+^dW
= 0, j = 0 , 1 , . . . , fc. Thus,
g(k+i) i s or thogonal to each of the directions d^, d>1',..., d^k'. We now show
that
g{k+1)Tg{j)
= 0, j =
o,i,...,k.
Fix j e { 0 , . . . , fc}. We have
dÜ)
_ffÜ)+/j._ldÜ-i).
=
Substituting this equation into the previous one yields
fl(*+i)Tdü)
=
o = _ fl (*+i)T fl Ü) + ^ . . ^ + υ τ ^ - ΐ ) .
Because g(.k+1')Td(j~1) = 0, it follows that gC'+^gU) = o.
We are now ready to show that d^k+1'TQd^'
= 0, j = 0 , . . . , fc. We have
d(k+DTQdU)
{_g(k+i)+ßkd(k))TQd(j)_
=
If j < A:, then cr ^ Qd'·" = 0, by virtue of the induction hypothesis. Hence,
we have
_g(k+i)TQdU)_
d (fc+i)T Qd G·) =
But g^' +1 ) =flfW)+ ajQd(j).
Because
d (* + DT Q d o·) =
Thus,
d{k+1)TQd{j)
fc 1 T
flf(
+ ) gW = 0, i = 0 , . . . , fc,
_g(fc+i)T(g(j+1)-g(j))
=
0
= 0 , j = 0 , . . . , fc - 1.
It remains to be shown that d ( f c + 1 ) T Qd ( f e ) = 0. We have
d (*+i)T Qd (fc) =
(_fl(fc+D
Using the expression for ßk, we get <r
proof.
+ßkdW)TQdW.
' Qd> ' = 0, which completes the
I
THE CONJUGATE GRADIENT ALGORITHM
185
E x a m p l e 10.3 Consider the quadratic function
3
3
/(xi,x 2 ,X3) = -zx\ +2x2 + -x\ + £i#3 + 2x2X3 - 3xx - x 3 .
We find the minimizer using the conjugate gradient algorithm, using the starting point χ(°) = [0,0,0] T .
We can represent / as
-xTQx-xTb,
f{x) =
where
Q =
"3 0 il
0 4 2
1 2 3J
b =
We have
g(x) = V / ( x ) = Qx-b=
[3xi + x 3 - 3,4x 2 + 2x 3 , xi + 2x 2 + 3x 3 - 1] T .
Hence,
9<°>
= [-3,0,-l]T,
d (0) = -ff(0),
5 (°)
T (0
d >
10
ao = - d (o)T Q d (o) = _3 6 = 0.2778
and
xd)
= x (°) +a0d(0)
= [0.8333,0,0.2778] T .
The next stage yields
ö ( 1 ) = V / ( x ( 1 ) ) = [-0.2222,0.5556,0.6667] T ,
We can now compute
Sl)
= -g{1) + ß0d{0)
= [0.4630, -0.5556, -0.5864] T .
Hence,
«l = — T ^T F
d^ QdW
and
,(2)
— = 0.2187
= x ( 1 ) + a i d ( 1 ) = [0.9346, -0.1215,0.1495] T .
186
CONJUGATE DIRECTION METHODS
To perform the third iteration, we compute
t,(2) = V / ( x ( 2 ) ) = [-0.04673, -0.1869,0.1402] T ,
g( 2 ) T Qd ( 1 )
d{2) = -gW +ß1d{1)
Hence,
a
and
Note that
x (3)
= [0.07948,0.1476,-0.1817] T .
o( 2 ) T d ( 2 )
2 = —75rf
or; = ° · 8 2 3 1
d(2)TQd(2)
= x (2) + a 2 d ( 2 ) = [1.000,0.000,0.000] T .
g(3> = V/( e < 3 >) = 0,
as expected, because / is a quadratic function of three variables. Hence,
I
x*=xW.
10.4
The Conjugate Gradient Algorithm for Nonquadratic Problems
In Section 10.3, we showed that the conjugate gradient algorithm is a conjugate direction method, and therefore minimizes a positive definite quadratic
function of n variables in n steps. The algorithm can be extended to general
nonlinear functions by interpreting f(x) = ^xTQx — xTb as a second-order
Taylor series approximation of the objective function. Near the solution such
functions behave approximately as quadratics, as suggested by the Taylor series expansion. For a quadratic, the matrix Q, the Hessian of the quadratic,
is constant. However, for a general nonlinear function the Hessian is a matrix that has to be reevaluated at each iteration of the algorithm. This can
be computationally very expensive. Thus, an efficient implementation of the
conjugate gradient algorithm that eliminates the Hessian evaluation at each
step is desirable.
Observe that Q appears only in the computation of the scalars a& and ßk ·
Because
ak = argmin/(ic ( f c ) + a d ( ; c ) ) ,
a>0
the closed-form formula for α& in the algorithm can be replaced by a numerical line search procedure. Therefore, we need only concern ourselves with the
formula for ßk. Fortunately, elimination of Q from the formula is possible and
results in algorithms that depend only on the function and gradient values at
THE CONJUGATE GRADIENT ALGORITHM FOR NONQUADRATIC PROBLEMS
187
each iteration. We now discuss modifications of the conjugate gradient algorithm for a quadratic function for the case in which the Hessian is unknown
but in which objective function values and gradients are available. The modifications are all based on algebraically manipulating the formula ßk in such
a way that Q is eliminated. We discuss three well-known modifications.
Hestenes-Stiefel Formula. Recall that
g(k^)TQd{k)
_
ßk
d^TQd^
~
'
The Hestenes-Stiefel formula is based on replacing the term Q<r ^ by the
term (g^k+1^ — g^)/otkThe two terms are equal in the quadratic case, as
we now show. Now, cc(fc+1) = x^ -ho^cr^. Premultiplying both sides by Q,
subtracting b from both sides, and recognizing that g^ = Qx^ — 6, we get
{k)
= (g(k+V
-g^k))/ak.
fl(fc+i) = g(k) +akQd(V^
w hich we can rewrite as Qd
Substituting this into the original equation for ßk gives the Hestenes-Stiefel
formula
fl(*+i)Tto(fc+i)_g(fc)]
=
k)T
dfi \g(k+1)-gW]
Polak-Ribiere Formula. Starting from the Hestenes-Stiefel formula, we
multiply out the denominator to get
g(k+l)T[g{k+l)_g{k)]
By Lemma 10.2, d ( f c ) V f c + 1 ) = 0. Also, since d{k) = -gW +/? fc _id (fc ~ 1) , and
premultiplying this by g^T, we get
g(k)Td(k)
=
-g(k)Tg(k)+ßk_ig(k)Td(k-l)
_ ff (*)T fl (fc) )
=
where once again we used Lemma 10.2. Hence, we get the Polak-Ribiere
formula
gfr+l)T\g(k+l)
-gW]
=
fa
g(k)Tg(k)
'
Fletcher-Reeves Formula. Starting with the Polak-Ribiere formula, we
multiply out the numerator to get
g(k+l)Tg(k+l)
fa
=
*
g(k)Tg(k)
We now use the fact that g(k+1)TgW
g(k+i)Td(k)
_g(k+l)Tg(k)
=
= 0, which we get by using the equation
_g(k+i)Tgw
+/3 jb _ 1 ^(*+DT d (fc-i)
and applying Lemma 10.2. This leads to the Fletcher-Reeves formula
g(k+l)T
fa
=
g(k+l)
g(k)Tg(k)
'
188
CONJUGATE DIRECTION METHODS
The formulas above give us conjugate gradient algorithms that do not require explicit knowledge of the Hessian matrix Q. All we need are the objective function and gradient values at each iteration. For the quadratic case the
three expressions for ßk are exactly equal. However, this is not the case for a
general nonlinear objective function.
We need a few more slight modifications to apply the algorithm to general nonlinear functions in practice. First, as mentioned in our discussion of the steepest descent algorithm (Section 8.2), the stopping criterion
V/(cc( fc+1 )) = 0 is not practical. A suitable practical stopping criterion, such
as those discussed in Section 8.2, needs to be used.
For nonquadratic problems, the algorithm will not usually converge in n
steps, and as the algorithm progresses, the "Q-conjugacy" of the direction
vectors will tend to deteriorate. Thus, a common practice is to reinitialize the
direction vector to the negative gradient after every few iterations (e.g., n or
n + 1) and continue until the algorithm satisfies the stopping criterion.
A very important issue in minimization problems of nonquadratic functions
is the line search. The purpose of the line search is to minimize 0&(α) =
f(x^
+ aS )) with respect to a > 0. A typical approach is to bracket or
box in the minimizer and then estimate it. The accuracy of the line search
is a critical factor in the performance of the conjugate gradient algorithm. If
the line search is known to be inaccurate, the Hestenes-Stiefel formula for ßk
is recommended [69].
In general, the choice of which formula for ßk to use depends on the objective function. For example, the Polak-Ribiere formula is known to perform
far better than the Fletcher-Reeves formula in some cases but not in others.
In fact, there are cases in which the g^h\ k = 1,2,..., are bounded away from
zero when the Polak-Ribiere formula is used (see [107]). In the study by Powell in [107], a global convergence analysis suggests that the Fletcher-Reeves
formula for ßk is superior. Powell further suggests another formula for β^:
f
^
= Π 1 3 Χ
0
\ '
g
(fc+i)T[g(fc+i)_g(fc)n
^WTgW
/·
For general results on the convergence of conjugate gradient methods, we
refer the reader to [135]. For an application of conjugate gradient algorithms
to Wiener filtering, see [116], [117], and [118].
Conjugate gradient algorithms are related to Krylov subspace methods
(see Exercise 10.6). Krylov-subspace-iteration methods, initiated by Magnus
Hestenes, Eduard Stiefel, and Cornelius Lanczos, have been declared one of
the 10 algorithms with the greatest influence on the development and practice
of science and engineering in the twentieth century [40].
For control perspective on the conjugate gradient algorithm, derived from
a proportional-plus-derivative (PD) controller architecture, see [4]. In addition, these authors offer a control perspective on Krylov-subspace-iteration
methods as discrete feedback control systems.
EXERCISES
189
EXERCISES
10.1 (Adopted from [88, Exercise 9.8(1)]) Let Q be a real symmetric positive definite n x n matrix. Given an arbitrary set of linearly independent
vectors {p^°\ . . . ,p^ n _ 1 ^} in R n , the Gram-Schmidt procedure generates a set
of vectors { d ( 0 ) , . . . , d ( n - 1 ) } as follows:
d< 0 >=p< 0 \
έί
d(t)TQd(t)
Show that the vectors dr°\ . . . , (Γη~ι* are Q-conjugate.
10.2 Let / : R n -+ R be the quadratic function
/(a;) = -xTQx
-
xTb,
where Q = Q T > 0. Given a set of directions {d (0) , d ( 1 ) , . . . } C R n , consider
the algorithm
fc
a j (*+l)=x(*) + e f c d( ),
where α& is the step size. Suppose that g( f c + 1 ) T d ( i ) = 0 for all fc = 0 , . . . , n — 1
and t = 0 , . . . , fe, where g^+V = V / ( a ^ + 1 ) ) . Show that if gWTdw
φ 0 for
all k = 0 , . . . , n — 1, then dS°\ . . . , d^n~^ are Q-conjugate.
10.3 Let / : R n -► R be given by f(x) = ±x T Qa; - x T 6 , where 6 G R n
and Q is a real symmetric positive definite n x n matrix. Show that in the
conjugate gradient method for this / , d{k)TQd{k) = -d ( f c ) T 'Qg { k ) .
10.4 Let Q be a real nx n symmetric matrix.
a. Show that there exists a Q-conjugate set {d^\ . . . , d ^ } such that each
dy* (i = 1 , . . . , n) is an eigenvector of Q.
Hint: Use the fact that for any real symmetric nxn matrix, there exists
a set { u i , . . . , v n } of its eigenvectors such that vjvj = 0 for all i,j =
Ι , . , . , π , %φ j .
b . Suppose that Q is positive definite. Show that if {d^\.. .,d^n'} is a
Q-conjugate set that is also orthogonal (i.e., cr*' d^' = 0 for all i,j =
l , . . . , n , i ^ j), and d' 2 ' φ 0, i = 1 , . . . , n, then each d ^ , i = 1 , . . . , n, is
an eigenvector of Q.
10.5 Consider the following algorithm for minimizing a function / :
x<*+i> = a .(*) + a f c d<*>,
190
CONJUGATE DIRECTION METHODS
where α^ = argmin a f(x^
+ adP^). Let g^ = Vf(x^)
(as usual).
Suppose that / is quadratic with Hessian Q. We choose Sk+1^
=
lk9^k+l>} + d^k\ and we wish the directions
and (Γ + 1 ' to be Q-conjugate.
Find a formula for 7^ in terms of <r \ g^k+l\ and Q.
10.6 Consider the algorithm
with ah G R scalar and x^
given by
= 0, applied to the quadratic function / : R n —► R
f(x) = -xTQx
-
bTx,
where Q > 0. As usual, write gW = V / ( x ^ ) . Suppose that the search
directions are generated according to
d(*+i)=a f c 9 ( f c + 1 )+& f e d( f e \
where a& and bk are real constants, and by convention we take c r 1 ' = 0.
a. Define the subspace Vk = span[6, Qb,..., Q - 1 6] (called the Krylov subspace of order fc). Show that dP^ G Vk+i and x^ G Vfc.
Hint: Use induction. Note that Vo = {0} and Vi = span[6].
b . In light of part a, what can you say about the "optimality" of the conjugate gradient algorithm with respect to the Krylov subspace?
10.7 Consider the quadratic function / : R n —► R given by
f(x) = -xTQx
-
xTb,
where Q = QT > 0. Let D G R n x r be of rank r and x0 G R n . Define the
function φ : W -► R by
φ(α) = /(χ0 + Όα).
Show that φ is a quadratic function with a positive definite quadratic term.
10.8 Consider a conjugate gradient algorithm applied to a quadratic function.
a. Show that the gradients associated with the algorithm are mutually orthogonal. Specifically, show that g^+^gW
= 0 for all 0 < k < n - 1
and 0 < i < k.
Hint: Write g& in terms of d (i) and d{i~l\
b . Show that the gradients associated with the algorithm are Q-conjugate
if separated by at least two iterations.
Specifically, show that
= 0 for all 0 < fc < n - 1 and 0 < i < fc - 1.
g{k+i)TQg{i)
EXERCISES
191
10.9 Represent the function
/(a?i,x 2 ) = 2 X i +x2 - 3 x i x 2
-X2-7
in the form f(x) = ^xTQx — xTb + c. Then use the conjugate gradient
algorithm to construct a vector d' 1 ' that is Q-conjugate with d^0' = V/(x^ 0 ^),
where x^ = 0 .
10.10 Let / ( x ) , x = [xi,x 2 ] T G M2, be given by
5
1
f(x) = -x\ + -x\ + 2xix 2 - 3xi - x2.
a. Express f(x) in the form of f(x) = \xTQx
— xTb.
b . Find the minimizer of / using the conjugate gradient algorithm. Use a
starting point of x^ = [0,0] T .
c. Calculate the minimizer of / analytically from Q and 6, and check it with
your answer in part b.
10.11 Write a MATLAB program to implement the conjugate gradient algorithm for general functions. Use the secant method for the line search
(e.g., the MATLAB function of Exercise 7.11). Test the different formulas
for ßk on Rosenbrock's function (see Exercise 9.4) with an initial condition
χ(°ϊ = [—2,2]T. For this exercise, reinitialize the update direction to the
negative gradient every six iterations.
CHAPTER 11
QUASI-NEWTON METHODS
11.1
Introduction
Newton's method is one of the more successful algorithms for optimization. If
it converges, it has a quadratic order of convergence. However, as pointed out
before, for a general nonlinear objective function, convergence to a solution
cannot be guaranteed from an arbitrary initial point χ(°\ In general, if the
initial point is not sufficiently close to the solution, then the algorithm may
not possess the descent property [i.e., f(x^k+1^) jt f(x^)
for some k].
Recall that the idea behind Newton's method is to locally approximate the
function / being minimized, at every iteration, by a quadratic function. The
minimizer for the quadratic approximation is used as the starting point for
the next iteration. This leads to Newton's recursive algorithm
a.(*+
1
)=a.W_jF(a.W)-iÄ|(fc).
We may try to guarantee that the algorithm has the descent property by
modifying the original algorithm as follows:
1
x(*+i)=xW-akF(xW)-
gVe\
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
193
194
QUASI-NEWTON METHODS
where ak is chosen to ensure that
/(*( fc+i >) < /(*< fe )).
For example, we may choose ak = a r g m i n a > 0 f(x^
—
aF(x^)~1g^)
(see Theorem 9.2). We can then determine an appropriate value of ak by
performing a line search in the direction —F(x^)~1g^k\
Note that although the line search is simply the minimization of the real variable function
φΐζ{θί) = f{x^ — aF(x^)~1g^),
it is not a trivial problem to solve.
A computational drawback of Newton's method is the need to evaluate
F(x^)
and solve the equation F(x^)d{k)
= -g^k) [i.e., compute d{k) =
1
—F(x^)~ g^].
To avoid the computation of F(x^)~l,
the quasi-Newton
methods use an approximation to F(x^)~1
in place of the true inverse.
This approximation is updated at every stage so that it exhibits at least
some properties of F(x^)~1.
To get some idea about the properties that an
approximation to F(x^)~1
should satisfy, consider the formula
x(k+i)=x(k)-aHkgW,
where Hk is an n x n real matrix and a is a positive search parameter.
Expanding / about x^ yields
/ ( a ^ + D ) = /(*(*)) + f f W T (x( f e +D - XW) + ο(||χ(*+1> - a;<fc>||)
= /(*<*>) - agWTHkgW
+
o(\\Hkg^\\a).
As a tends to zero, the second term on the right-hand side of this equation
dominates the third. Thus, to guarantee a decrease in / for small a, we have
to have
)T
fc)
/ W
>o.
A simple way to ensure this is to require that Hk be positive definite. We
have proved the following result.
Proposition 11.1 Let f G C 1 , x{k) G R n , g{k) = V / ( « W ) φ 0, and
Hk an n x n real symmetric positive definite matrix. If we set cc(fc+1) =
x (fc) - akHkg^k\
where ak = argmin a > 0 /(x ( f c ) - aHkg^),
then ak > 0
(fc+1)
{k)
and /(x
) <f(x ).
ü
In constructing an approximation to the inverse of the Hessian matrix,
we should use only the objective function and gradient values. Thus, if we
can find a suitable method of choosing Hk, the iteration may be carried out
without any evaluation of the Hessian and without the solution of any set of
linear equations.
11.2
Approximating the Inverse Hessian
Let HQ,HI,H2,
.. · be successive approximations of the inverse
F(x^)~x
of the Hessian. We now derive a condition that the approximations should
APPROXIMATING THE INVERSE HESSIAN
195
satisfy, which forms the starting point for our subsequent discussion of quasiNewton algorithms. To begin, suppose first that the Hessian matrix F(x) of
the objective function / is constant and independent of x. In other words,
the objective function is quadratic, with Hessian F(x) = Q for all x, where
Q = QT. Then,
Ä(fc+i)_yW=Q(a.(fc+i)_xW).
Let
and
Then, we may write
Ag(k) = QAx(k).
We start with a real symmetric positive definite matrix HQ. Note that given
fc, the matrix Q - 1 satisfies
Q~lAg{i) = Ax{i\
0<i<k.
Therefore, we also impose the requirement that the approximation fffc+i of
the Hessian satisfy
= Δα ( < ) ,
H^AgW
0<i<k.
If n steps are involved, then moving in n directions Δ α τ ° \ Δατ 1 ', . . . , Αχ^η~^
yields
ΗηΔβ<°>=Δ*<°>,
HnAgW
HnAg{n-x)
=
Axw,
=
Ax^-V.
This set of equations can be represented as
Hn[AgV\Ag^,...,Ag^\
= [Αχ^,Αχ^,...
,Ax^~%
Note that Q satisfies
Q[Ax<°\AxW,...,
Δ ί ' " - 1 » ] = [AgM, Ag^,...,
Ag^}
and
Q-1[Ag^\Ag^\...1Ag^-^]
=
[Ax^\Ax^\...,Ax^-1\
Therefore, if [Ag^°\ Ag^\ . . . , Δ # ( η _ 1 ) ] is nonsingular, then Q _ 1 is determined uniquely after n steps, via
Q-l=Hn
=
[Ax(0\Ax^,...,Ax^-%Ag^\Ag^,...,A9(n-l)rl.
196
QUASI-NEWTON METHODS
As a consequence, we conclude that if Hn satisfies the equations HnAg^ =
Ax{i\
0 < i < n - 1, then the algorithm χ<*+1) = χΜ akHkg(k\
ak = a r g m i n a > 0 / ( £ C ^ — aHkg^),
is guaranteed to solve problems with
quadratic objective functions in n + 1 steps, because the update χ( η + 1 ) =
n
is equivalent to Newton's algorithm. In fact, as we shall see
x(n) _ anHng( )
below (Theorem 11.1), such algorithms solve quadratic problems of n variables
in at most n steps.
The considerations above illustrate the basic idea behind the quasi-Newton
methods. Specifically, quasi-Newton algorithms have the form
d« =
-HkgW,
ak = argmin/(a; ( f e ) +
χ
ad(k)),
a>0
(*+ΐ)=χ(*)+α^*>,
where the matrices Ho, Hi,...
are symmetric. In the quadratic case these
matrices are required to satisfy
Hk+1Ag(i)
0<z<fc,
= Ax^\
where Δ χ ( ΐ ) = χ^+^ - a?« = a<d(<) and Ag{i) = g^+V - g& = QAx^. It
turns out that quasi-Newton methods are also conjugate direction methods,
as stated in the following.
Theorem 11.1 Consider a quasi-Newton algorithm applied to a quadratic
function with Hessian Q = Q such that for 0 < k < n — 1,
Hk+1Ag{i)
where Hk+1
conjugate.
= Hj+1.
=Ax{i\
0<i<k,
If a{ φ 0, 0 < i < k, then rf(0),... ,d ( f c + 1 ) are QD
Proof. We proceed by induction. We begin with the k = 0 case: that d (0)
and d (1) are Q-conjugate. Because OLQ φ 0, we can write d^ =
Αχ^/αο·
Hence,
d^Qd^
=
-gWHlQd(0)
ΟΔχ(0)
mx
_ fl (DT
= -9(1)T
a0
THE RANK ONE CORRECTION FORMULA
197
But gW^dr·0' = 0 as a consequence of ao > 0 being the minimizer of φ(α) =
/(aj<°> + ad{0)) (see Exercise 11.1). Hence, d ( 1 ) T Q d ( 0 ) = 0.
Assume that the result is true for k — 1 (where k < n — 1). We now prove
the result for fc, that is, that d^°\... ,*r fc+1 ) are Q-conjugate. It suffices to
show that d ( f c + 1 ) T Qd ( i ) = 0, 0 < i < k. Given i, 0 < i < fc, using the same
algebraic steps as in the k = 0 case, and using the assumption that cti φ 0,
we obtain
d(fe+i)TQd(i) =
_fl(*+i)THfc+lQd«)
= _s(*+i)Td(Oi
Because
are Q-conjugate by assumption, we conclude from
Lemma 10.2 that ^( f c + 1 ) T d ( i ) = 0. Hence, d ( f c + 1 ) T Qd ( i ) = 0, which completes the proof.
I
By Theorem 11.1 we conclude that a quasi-Newton algorithm solves a
quadratic of n variables in at most n steps.
Note that the equations that the matrices Hk are required to satisfy do
not determine those matrices uniquely. Thus, we have some freedom in the
way we compute the Hk- In the methods we describe, we compute Hk+i by
adding a correction to Hk- In the following sections we consider three specific
updating formulas.
11.3
The Rank One Correction Formula
In the rank one correction formula, the correction term is symmetric and has
the form akz^z^T,
where ak e R and z^ G Rn. Therefore, the update
equation is
Hk^=Hk
+
akz^z^T.
Note that
W
rankz
(fc) (fc)T
z
= rank
M
[«*(*:)
,(*)
=1
/
and hence the name rank one correction [it is also called the single-rank symmetric (SRS) algorithm]. The product z^z^T
is sometimes referred to as
the dyadic product or outer product Observe that if Hk is symmetric, then
so is iffc+i·
Our goal now is to determine ak and z^k\ given Hk, Δ Α < * \ Δβ<*\ so
that the required relationship discussed in Section 11.2 is satisfied; namely,
198
QUASI-NEWTON METHODS
Hk+iAg^1' = Δχ(*\ i = 1 , . . . , k. To begin, let us first consider the condition
JTfc+iA<7^ = Ax^k\ In other words, given Hk, Ag (fc) , and Ax (fe) , we wish
to find ak and z^ to ensure that
= (Hk + akzWzWT)AgW
Hk+1AgW
First note that z^TAg^
=
Ax^.
is a scalar. Thus,
- H fc A fl ( fc ) =
Ax^
and hence
Ak)
=
( a ^ ' V ' ) ^ ,
AgW-HfcAgW
afe(2WTA9«)
We can now determine
(fc)
(AxW - H fc Ag( fc ))(AxW - H fc A g <*>) T
(fc)T =
at(2«TASW)2
*
Hence,
Hk+\ = Hk +
(Ax<fc) - HkAg(k)){Axik)
a fe (zWTA fl W)2
HkAg(k))T
The next step is to express the denominator of the second term on the righthand side of the equation above as a function of the given quantities Hk,
,(*)
Ag (fe) , and Aa?(fe). To accomplish this, premultiply Aa;(fe) - HkAg^
(akzWJ'Agw)zW
by Ag(k)r to obtain
Ag^TAx^
= Aff<fe>Tα**<*>z<*>TAg^.
- AgWTHkAgW
Observe that ak is a scalar and so is Ag (fe)T z (fe) = z ( f e ) T Ag ( f e ) . Thus,
Afl<fc>TA*<*> - AgWTHkAgW
=
ak(z^TAg^)2.
Taking this relation into account yields
„
„
fe+1
, (Ax{k) - HkAgW)(AxW
HkAg^)T
fe+
AgWT(AxM-HkAgM)
We summarize the above development in the following algorithm.
Rank One Algorithm
1. Set k := 0; select x^
and a real symmetric positive definite ϋ"ο·
2. Ifflf(fc)= 0, stop; else, d{k) =
-Hkg^k\
THE RANK ONE CORRECTION FORMULA
199
3. Compute
ak = argmin/(x ( f c ) +
a>0
ad{k)),
k
x(K+V=x(K)+akS
\
4. Compute
Ax^
=akdik\
AgW=g^-gW,
„
_ „ , (Aa;<fc> - HkAg^)(Ax^
HkAg^)T
k+1
k+
~
Ag^T(Ax^-HkAg^)
5. Set k := k + 1; go to step 2.
The rank one algorithm is based on satisfying the equation
Hk+1AgW
= Δ*<*>.
However, what we want is
Hk+1AgW=AxM,
i = 0,l,...,fc.
It turns out that the above is, in fact, true automatically, as stated in the
following theorem.
Theorem 11.2 For the rank one algorithm applied to the quadratic with Hessian Q = QT, we have Hk+iAg^
= Ax^\ 0 < i < k.
D
Proof. We prove the result by induction. Prom the discussion before the
theorem, it is clear that the claim is true for k — 0. Suppose now that the
theorem is true for k — 1 > 0; that is, HkAg^
— Ax^\ i < k. We now
show that the theorem is true for k. Our construction of the correction term
ensures that
Hk+1AgW
= Ax^.
So we only have to show that
Η*+ιΔ0(<)=Δ*(ί),
i<k.
To this end,fixi < k. We have
M
A«W H Λ « « + (Ag (fc) - HfcAg<fc>)(As<*> - HkAg^y
Hk+1Ag(
= HkAg(
+
A , « T ( A S B < * > _ Η *Δ,,<*>)
^
(i)
'
By the induction hypothesis, HkAg^ = Ax^. To complete the proof, it is
enough to show that the second term on the right-hand side of the equation
above is equal to zero. For this to be true it is enough that
(A* (fc) - HkAg{k))TAg^
= Ax{k)T
Ag^
- Ag^k)THkAg^
= 0.
200
QUASI-NEWTON METHODS
Indeed, since
Ag{k)THkAg(i)
= Ag{k)T (HkAg{i))
Ag{k)TAx{i)
=
by the induction hypothesis, and because Ag^k' = QAx^k\
Ag^THkAg^
= Ag™T Ax® = Ax^TQAx^
we have
= Ax^T
Ag^
Hence,
(Ax{k) - HkAg{k))TAg{i)
= Ax{k)TAg{i)
- Ax^k)TAg{i)
= 0,
which completes the proof.
Example 11.1 Let
= x\ + -x\ + 3.
f(xi,x2)
Apply the rank one correction algorithm to minimize / . Use x^
and Ho = I2 ( 2 x 2 identity matrix).
We can represent / as
\xT
/(*) =
2 0
x + 3.
0 1
Thus,
,(*)
Because HQ = I2,
2 0 (fc)
x
0 1
d<0> = - f l ( 0 ) = [ - 2 , - 2 ] T .
The objective function is quadratic, and hence
n(0)Tj(0)
a0 = a r g m i n / ( ^ ) + a d ( ° ) ) = - ^
a>0
[2,2] 2]
2I
2
2 0] I"2 ~ ~ 3 '
[2,2] 0 lj [2
and thus
x^=x^+a0d^
1 2
3'3
T
A _
=
THE RANK ONE CORRECTION FORMULA
201
We then compute
Δχ<°> = a 0 d ( 0 ) =
Γ 4 _4l
L _ 3' ~ 3 j
"_2 2 ] T
~ 3 ' 3J
V
0)
W
=9
(0)
~9
-|T
=
Because
Ag^T(Ax^-H0Ag^)
32
9'
=
we obtain
H1 = H0 +
(Ag(°> - H0Ag^)(Ax^
Α9^τ(ΑχΜ-Η0Α9Μ)
Therefore,
d'1*
-Hl9W
=
-
H0Ag^)T
\
0
0
1
1 2
3'~3
and
Oil
We now compute
~d^TQd^
= 1.
» ( 2 ) = x ( 1 ) + a i d ^ = [0,0] T .
Note that g^ = 0, and therefore x^ = x*. As expected, the algorithm
solves the problem in two steps.
Note that the directions Φ0' and d^1' are Q-conjugate, in accordance with
Theorem 11.1.
I
Unfortunately, the rank one correction algorithm is not very satisfactory,
for several reasons. First, the matrix Hk+i that the rank one algorithm
generates may not be positive definite (see Example 11.2 below) and thus
may not be a descent direction. This happens even in the quadratic
case (see Example 11.10). Furthermore, if
Ag^(Ax^
-HkAgW)
is close to zero, then there may be numerical problems in evaluating JTfc+i.
Example 11.2 Assume that Hk > 0. It turns out that if Ag^k)T (Ax^k) HkAg(h))
> 0, then B V n > 0 (see Exercise 11.7).
However, if
202
QUASI-NEWTON METHODS
Ag^k)T(Ax^k)
- HkAg(k))
< 0, then Hk+i may not be positive definite. As
an example of what might happen if Ag^T(Ax^k)
- HkAg^)
< 0, consider
applying the rank one algorithm to the function
/(*) = T + y
with an initial point
XlX2 + Xi - X2
x ( 0 ) = [0.59607,0.59607]T
and initial matrix
Ho =
0.94913
0.14318
0.14318
0.59702
Note that H0 > 0. We have
Δ 0 ( ο ) τ ( Δ χ ( ο ) - H0Ag(Q))
and
0.94481
0.23324
= -0.03276
0.23324
-1.2788
It is easy to check that H i is not positive definite (it is indefinite, with
eigenvalues 0.96901 and -1.3030).
I
Fortunately, alternative algorithms have been developed for updating Hk.
In particular, if we use a "rank two" update, then Hk is guaranteed to be
positive definite for all &, provided that the line search is exact. We discuss
this in the next section.
11.4
The D F P Algorithm
The rank two update was originally developed by Davidon in 1959 and was
subsequently modified by Fletcher and Powell in 1963: hence the name DFP
algorithm. The DFP algorithm is also known as the variable metric algorithm.
We summarize the algorithm below.
D F P Algorithm
1. Set k := 0; select x^
and a real symmetric positive definite HQ.
2. Ifflf(fc)= 0, stop; else, d{k) =
-HkgW.
3. Compute
ak = argmin/(x ( f c ) + ad ( f c ) ),
a>0
k
x(^)=xW+akS \
THE DFP ALGORITHM
203
4. Compute
Ag<fc>As<fc>T
_
k+1
k +
~
[HkAg^][HkAg^}T
Δ*<*>τΔβ<*>
AgWrHkAgW
'
5. Set A: := A: + 1; go to step 2.
We now show that the DFP algorithm is a quasi-Newton method, in the
sense that when applied to quadratic problems, we have Hk+iAg^1' = Ax^\
0<i<k.
Theorem 11.3 In the DFP algorithm applied to the quadratic with Hessian
Q = QTy We have Hk+1Ag{i)
= Ax{i), 0<i<k.
D
Proof. We use induction. For k = 0, we have
IW»
AgM - H ° V 0 ) V 0 ) T g Q
+ ΑΧ(0)ΑΧ(0)Τ
= iW°)
Ag (0
= Δχ(°>.
Assume that the result is true for k — 1; that is, HkAg^
= Δχ^, 0 <
i < fc - 1. We now show that Hk+iAg^
= Ax^\ 0 < i < k. First, consider
i = k. We have
= Δ*<*>.
It remains to consider the case i < k. To this end,
+
y
y
Δχ(ί:)τΔ0(ί:)
HkAgW
AgWTHkAgW
Ag{k)THkAg{k)
(Ag^Ax^).
Now,
Δχ ( Α : ) Τ Δ9 ( ί ) = Δχ ( Α : ) τ ζ?Δχ ( ί )
=
= 0,
akaid^TQd^
204
QUASI-NEWTON METHODS
by the induction hypothesis and Theorem 11.1. The same arguments yield
Ag(k)TAx{i)
= 0. Hence,
Hk+1Ag^
=
Ax^,
which completes the proof.
I
By Theorems 11.1 and 11.3 we conclude that the DFP algorithm is a conjugate direction algorithm.
E x a m p l e 11.3 Locate the minimizer of
4
2
fix) = 2 χ Τ 2 2
-1
-xT
X
xe
1
Use the initial point x^ = [0,0] T and H0 = 1*2,.
Note that in this case
-1
4 2 (*) _
2 2 x
,(*)
Hence,
9(0) = [ l , - l f ,
d<°> - -H0gW
~-l~
"l θ" " 1 "
1
0 1 -1 =
=
Because / is a quadratic function,
a0 = argmin/(x(0)+ad(°)) = - J
5 y T
a>0
[i,-i]
-l"
1
4 2~
[-1,1] 2 2
Therefore,
x
-ll
1.
1J
-r(O) + a d (0) = [-l,l] T .
= x(°>
0
(1) _
We then compute
Δχ(°'=ί1!(ΐ)-χ(ο)
,(i)
4 2
2 2
=
-1
1
[-ΐ)ΐ]ΐ
-
-1
1
=
-1
-1
A_
THE DFP ALGORITHM
and
205
= {-2,0]r.
AgW=gW-gW
Observe that
"-l"
1 [-i,i] =
Δ*<°>τΔβ<°> = -1,1]
/W°> =
-2]
0 1 = 2,
Γ-2
1 o] Γ-2
0 lj
Thus,
1 -1"
-1
1
L°
L°
' -i [-2,0] =
(H0Ag^)(H0Ag^)T
0
and
"1 0"
)] 0 1
Ag^TH0Ag^^[-2,0]
"-2I
0J
[4 0
[0 0
= 4.
Using the above, we now compute H\\
H1=H0
+
Ax(°W°>T
Ax^Ag^
"1 0" 1 1
0 1 4- 2- - 1
1
2
1
2
(H0AgM)(H0AgM)T
Ag^HoAg (0)
-1
1
1 4 0"
~ 4 0 0
1"
2
3
2
We now compute d (1) = -Ηλρ^
= [0,1] T and
αι = ar in/( (1) + ad(1)) =
f- "
Hence,
x (2)
~^^
=
*
= x (!) + a i d ( 1 ) = [-1,3/2] T = x*,
because / is a quadratic function of two variables.
Note that we have d ( 0 ) T Qd ( 1 ) = d ( 1 ) T Q d ( 0 ) = 0; that is, d (0) and d (1) are
Q-conjugate directions.
I
We now show that in the DFP algorithm, Hk+i inherits positive definiteness from Hk-
206
QUASI-NEWTON METHODS
Theorem 11.4 Suppose that gW φ 0.
positive definite, then so is Hk+i.
In the DFP algorithm, if Hk
is
□
Proof. We first write the following quadratic form:
τ„
_
x nk+lx-x
-X
τ„
tikx
τ
, xTAx^Ax^rx
+
Ax{k)TAg(k)
xr(HkAg^)(HkAg^)Tx
AgWrHkAgW
(χτΔχ^)2
MkX+
(xTHfcAgW)2
Ax{k)TAg{k)
Ag(k)THkAg{k)
■
Define
Δ
„1/2
a = Hk' x,
b±Hl/2AgW,
where
Note that because Hk > 0, its square root is well-defined; see Section 3.4
for more information on this property of positive definite matrices. Using the
definitions of a and 6, we obtain
xTHkX
xrHkAgW
and
AgWTHkAgW
= xJHjJ
Hj/ x = aTa, T
= xTHl/2Hl/2AgW
= a b,
= Ag^1ΉψΉψ:Ag^
= bTb.
Hence,
x Hk+1x
a
= a
+AxWTAg{k)--bTb~
_ ||α||2||6||2-((α,6))2
II&II
(χτΑχ^)2
2
Ax^TAg^'
We also have
Δ*« τ Δ0<*> = Δ*<*>τ(0<*+1> -gM)
since Ax{k)Tg(k+V
= akSk)Tg(k+V
cise 11.1). Because
Ax^=akSk)
we have
Ax^TAg^
=
-Ax^Tg^k\
= 0 by Lemma 10.2 (see also Exer=
-akHkgik\
= - Δ χ ^ V fc) = W ° T W f c ) -
THE BFGS ALGORITHM
207
This yields
τΜ
„
\\a\\2\\b\\2-((a,b))2
(xTAx^
akgWTHkgW
Both terms on the right-hand side of the above equation are nonnegative—the
first term is nonnegative because of the Cauchy-Schwarz inequality, and the
second term is nonnegative because Hk > 0 and ak > 0 (by Proposition 11.1).
Therefore, to show that xTHk+iX
> 0 for x φ 0, we only need to demonstrate
that these terms do not both vanish simultaneously.
The first term vanishes only if a and b are proportional, that is, if a = ßb
for some scalar ß. Thus, to complete the proof it is enough to show that if
a = ßb, then (xT Ax{k))2/(akg^T
Hkg^)
> 0. Indeed, first observe that
Η\/2χ
= a = ßb = βΗΐ'2Α9Μ
Hence,
=
H^ißAgW).
x = ßAg{k\
Using the expression for x above and the expression
akg^THkg^k\
we obtain
(a?TAa?(fc))2
akg(k)THkg(k)
_ ß2(AgWTAxW)2
_
k T
~ akg( ) HkgW
~
=
ß2akgWTHk9W>0.
Thus, for all x φ 0,
x1Hk+ix
which completes the proof.
ß2{akg{k)THkg^)2
akg(k)THkg(V
> 0,
I
The DFP algorithm is superior to the rank one algorithm in that it preserves the positive definiteness of Hk. However, it turns out that in the case
of larger nonquadratic problems the algorithm has the tendency of sometimes
getting "stuck." This phenomenon is attributed to Hk becoming nearly singular [19]. In the next section we discuss an algorithm that alleviates this
problem.
11.5
The BFGS Algorithm
In 1970, an alternative update formula was suggested independently by Broyden, Fletcher, Goldfarb, and Shanno. The method, now called the BFGS
algorithm, is discussed in this section.
To derive the BFGS update, we use the concept of duality, or complementarity, as presented in [43] and [88]. To discuss this concept, recall that the
208
QUASI-NEWTON METHODS
updating formulas for the approximation of the inverse of the Hessian matrix
were based on satisfying the equations
Hk+iAg{i)
= Ax{i\
0<i<k,
which were derived from Ag^ — QAx^l\ 0 < i < k. We then formulated
update formulas for the approximations to the inverse of the Hessian matrix
Q~l. An alternative to approximating Q~l is to approximate Q itself. To do
this let Bk be our estimate of Q at the /cth step. We require Bk+ι to satisfy
Ag{i) =BMAx{i\
0<i<k.
Notice that this set of equations is similar to the previous set of equations for
iffc+i? the only difference being that the roles of Δατ^ and Ag^ are interchanged. Thus, given any update formula for Hk, a corresponding update
formula for Bk can be found by interchanging the roles of Bk and Hk and
of Ag^ and Ax^k\ In particular, the BFGS update for Bk corresponds to
the DFP update for Hk- Formulas related in this way are said to be dual or
complementary [43].
Recall that the DFP update for the approximation Hk of the inverse Hessian is
, Δα^>Δ*<*>τ
HkAg^Ag^Hk
HDFP
= rf
k+1
k +
τ
T
Δχ<*> Δ0<*>
Ag^ HkAg^
'
Using the complementarity concept, we can easily obtain an update equation
for the approximation Bk of the Hessian:
Ag^Ag^T
k+
Ag^TAx^
fc+1
BkAx^Ax^TBk
Ax^TBkAx^
'
This is the BFGS update of Bk.
Now, to obtain the BFGS update for the approximation of the inverse
Hessian, we take the inverse of Bk+\ to obtain
TTBFGS
\-l
(JD
/
+
Ag^Ag^T
V " Δ^τΔχ^
BkAx^Ax^Bk
Ax^TBkAx^
\"'
)
'
To compute H^+^s by inverting the right-hand side of this equation, we
apply the following formula for a matrix inverse, known as the ShermanMorrison formula (see [63, p. 123] or [53, p. 50]).
Lemma 11.1 Let A be a nonsingular matrix. Let u and v be column vectors
such that 1 + vTA~lu
φ 0. Then, A + uvT is nonsingular, and its inverse
can be written in terms of A~ using the following formula:
THE BFGS ALGORITHM
(A + uvT)~1
= A'
(A^u^A'1)
l-h^A"1 u
209
D
Proof. We can prove the result easily by verification.
Prom Lemma 11.1 it follows that if A - 1 is known, then the inverse of the
matrix A augmented by a rank one matrix can be obtained by a modification
of the matrix A - 1 .
Applying Lemma 11.1 twice to Bk+i (see Exercise 11.12) yields
IT
TTBFGS
,
Δ*<*>Δ*<*>Ί
)
Ax™TAgW
AgWTHkAgW\
1+
Ag^TAx^
v
HkAg^Ax^T
(HkAg^Ax{k)T)T
+
T
AgW AxW
which represents the BFGS formula for updating Hk.
Recall that for the quadratic case the DFP algorithm satisfies
H^Ag{i)
= Ax{i\ 0<i<k.
Therefore, the BFGS update for Bk satisfies
Bk+iAx^
= Ag^\ 0 < i < k. By construction of the BFGS formula for
H
we
k+iS,
conclude that H^sAg{i)
= Ax(i\ 0 < i < k. Hence, the
BFGS algorithm enjoys all the properties of quasi-Newton methods, including the conjugate directions property. Moreover, the BFGS algorithm also
inherits the positive denniteness property of the DFP algorithm; that is, if
gW φ 0 and Hk > 0, then H%£fs > 0.
The BFGS update is reasonably robust when the line searches are sloppy
(see [19]). This property allows us to save time in the line search part of
the algorithm. The BFGS formula is often far more efficient than the DFP
formula (see [107] for further discussion).
We conclude our discussion of the BFGS algorithm with the following numerical example.
Example 11.4 Use the BFGS method to minimize
f(x) = -xTQx
- xTb + log(7r),
where
Q
5
-3
-3
2
Take H0 = J 2 and x<0' = [0,0] T . Verify that H2 = Q~l
We have
d (0)
= -gW
=
_ ( Q x ( 0 ) _ ft) =
b =
I°
210
QUASI-NEWTON METHODS
The objective function is a quadratic, and hence we can use the following
formula to compute a0:
OLO =
-
9
(o)Td(o)
!
d(0)TQd(0)
2'
Therefore,
aj(i)=x(0)+a0d
To compute Ηχ = HfFGS,
0
1/2
(0)
we need the following quantities:
0
V2
g™ = Qx™ - b --
-3/2
0
-3/2
1
AgW=gU-gM:
Therefore,
H i = Ho +
χ
1+
Δ0(°)τΔχ(°)
Ax^Ag^TH0
1
3/2
j Δχ<°>τΔβ<°>
+ H0Afl<0W0>T
(0)
Ag^TAx
3/2
11/4
Hence, we have
3/2
9/4
ö(DTd(i)
«1
<fi»TQ<fi»
2.
Therefore,
2
1
1
cc( ) = a 5 ( ) + a i d ( > =
Because our objective function is a quadratic on R 2 , x^
Notice that the gradient at x^ is 0; that is, g^ = 0.
is the minimizer.
EXERCISES
To verify that H2 = Q
211
λ
Ax^
, we compute
= XW - XW =
AgV=gW-gU
3
9/2
3/2]
0
Hence,
H2 = Hi+[l
+
Δχ( 1 >Δχ( 1 ) τ
Ag^H^A
Ag^TAx^
)
Ax(1)TAgW
Αχ^Α9^ΎΗ1+Η1Α9^Αχ^τ
Ag^TAx^
2 3
3 5
Note that indeed H2Q = QH2 — I2, and hence H2 = Q
1
.
■
For nonquadratic problems, quasi-Newton algorithms will not usually converge in n steps. As in the case of the conjugate gradient methods, here, too,
some modifications may be necessary to deal with nonquadratic problems.
For example, we may reinitialize the direction vector to the negative gradient
after every few iterations (e.g., n or n + 1), and continue until the algorithm
satisfies the stopping criterion.
EXERCISES
11.1 Given / : Rn -* K, / € C1, consider the algorithm
where c r ^ c r 2 ' , . . . are vectors in R n , and α^ > 0 is chosen to minimize
/ ( x ( f c ) + a d ( f c ) ) ; that is,
ak = argmin/(x ( ; c ) +
a>0
aS-k)).
Note that the general algorithm above encompasses almost all algorithms that
we discussed in this part, including the steepest descent, Newton, conjugate
gradient, and quasi-Newton algorithms.
Let gW = V/(«<fc>), and assume that d(fc) V * ° < 0.
212
QUASI-NEWTON METHODS
a. Show that d^k' is a descent direction for / in the sense that there exists
ä > 0 such that for all a G (0, ä],
/(x^+ad(fc))</(x(fc)).
b . Show that ak > 0.
c. Show that d{k)1'g(k+V
= 0.
d. Show that the following algorithms all satisfy the condition d^k' g^
if0<*>^O:
< 0,
1. Steepest descent algorithm.
2. Newton's method, assuming that the Hessian is positive definite.
3. Conjugate gradient algorithm.
4. Quasi-Newton algorithm, assuming that Hk > 0.
e. For the case where f(x) = \χτQx
— xTb, with Q = Q
expression for ak in terms of Q, <r \ and g(k\
> 0, derive an
11.2 Consider Newton's algorithm applied to a function f e C2:
x(k+i) = x(k)
_
α^(χ(*0)-ΐν/(χ<*>),
where ak is chosen according to a line search. Is this algorithm a member of
the quasi-Newton family?
11.3 In some optimization methods, when minimizing a given function
/ ( # ) , we select an initial guess x^ and a real symmetric positive definite
matrix Ho- Then we iteratively compute Hk, cr ' = —Hkg^
(where
gW = Vf(x^)),
and χ^+^ = » « + akd{k\ where
ak = arg m i n / (χ^
α>0
\
+ ad ( f c ) ) .
/
Suppose that the function we wish to minimize is a standard quadratic of the
form
f(x) = -xTQx - xTb + c, Q = QT > 0.
a. Find an expression for ak in terms of Q, Hk, g^k\ and d> ';
b . Give a sufficient condition on Hk for ak to be positive.
11.4 Consider the algorithm
a.(fc+i) = x<*) _ Ηί,<*\
EXERCISES
where, as usual, g^
213
— V / ( x ^ ) and H is a fixed symmetric matrix.
a. Suppose that / G C3 and there is a point x* such that V/(x*) = 0 and
F ( x * ) _ 1 exists. Find H such that if x^ is sufficiently close to cc*, then
x^ converges to a?* with order of convergence of at least 2.
b . With the setting of H in part a, is the given algorithm a quasi-Newton
method?
11.5 Minimize the function
o" x
~1
0
2
--xT
1"
-1
+7
using the rank one correction method with the starting point x^
= 0.
11.6 Consider the algorithm
x(k+i) = x(k)
_
{k)
akMkWf(x
),
where / : R 2 -► R, / G C1, M f c G R 2 x 2 is given by
Mfc =
1 0
0 a
with a G R, and
ak = arg min / ( « « - aM f e V/(x ( f c ) )).
α>0
Suppose that at some iteration k we have V / ( a ; ^ ) = [1,1] T . Find the
largest range of values of a that guarantees that ak > 0 for any / .
11.7 Consider the rank one algorithm. Assume that Hk > 0. Show that if
Ag(k)T(Ax{k)
- HkAg{k))
> 0, then Hk+1 > 0.
11.8 Based on the rank one update equation, derive an update formula using
complementarity and the matrix inverse formula.
11.9 Let
/ = -xTQx
= 2X
— xTb + c
1
0
o" x
2
--xT
"1 "
-1
+7
214
QUASI-NEWTON METHODS
and χ(°^ = 0. Use the rank one correction method to generate two Qconjugate directions.
11.10 Apply the rank one algorithm to the problem in Example 11.3.
11.11 Consider the DFP algorithm applied to the quadratic function
f(x) = -xTQx
- xTb,
where Q = QT > 0.
a. Write down a formula for ak in terms of Q, g^k\ and <rk\
b . Show that if gW φ 0, then ak > 0.
11.12 Use Lemma 11.1 to derive the BFGS update formula based on the
DFP formula, using complementarity.
Hint: Define
A 0 = Bk,
u0 =
Ui =
vj =
A1
Ag^TAx^'
BkAx{k)
Ax^TBkAx^'
Ax^TBk,
Ag^Ag^T
D
= Bk+
A
Ag^Ax^=A0
+
U0V
°'
Using the notation above, represent J3fc+i as
Bk+1
= A0 +
= Ai
U0VQ
+
uivj
-\-uxvJ.
Apply Lemma 11.1 to the above.
11.13 Assuming exact line search, show that if HQ = In (n x n identity
matrix), then the first two steps of the BFGS algorithm yield the same points
x^ and x^ as conjugate gradient algorithms with the Hestenes-Stiefel, the
Polak-Ribiere, and the Fletcher-Reeves formulas.
11.14 Let / : M71 —> R be such that f e C1. Consider an optimization
algorithm applied to this / , of the usual form x( fc+1 ) = χ^ + ak<r \ where
EXERCISES
Oik > 0 is chosen according to line search. Suppose that S ' =
where g^ = Vf(x^)
and Hk is symmetric.
215
—Hkg^k\
a. Show that if Hk satisfies the following conditions whenever the algorithm
is applied to a quadratic, then the algorithm is quasi-Newton:
1· -fffc+i = Hk + Uk2. UkAg(k) = Ax{k) -
HkAg{k\
3. Uk = α<*>Δχ ( * )τ + b{k)Ag{k)THk,
where <»(*> and b(k) are in R n .
b . Which (if any) among the rank-one, DFP, and BFGS algorithms satisfy
the three conditions in part a (whenever the algorithm is applied to a
quadratic)? For those that do, specify the vectors a^ and b^ \
11.15 Given a function / : R n —► R, consider an algorithm aj(fe+1) = x^ —
OLkHkg^ for finding the minimizer of / , where gW = V / ( x ^ ) and Hk G
R n x n is symmetric. Suppose that Hk = <t>H%FP + (1 - φ)Η%FGS, where
φ e R, and H%FP and H%FGS are matrices generated by the DFP and BFGS
algorithms, respectively.
a. Show that the algorithm above is a quasi-Newton algorithm. Is the above
algorithm a conjugate direction algorithm?
b . Suppose that 0 < φ < 1. Show that if H°FP > 0 and H%FGS > 0, then
Hk > 0 for all k. What can you conclude from this about whether or
not the algorithm has the descent property?
11.16 Consider the following simple modification of the quasi-Newton
family of algorithms. In the quadratic case, instead of the usual quasiNewton condition Hk+iAg^> = Ax^\ 0 < i < k, suppose that we have
Hk-^iAg^
= ριΑχ^\
0 < i < fe, where pi > 0. We refer to the set of
algorithms that satisfy the condition above as the symmetric Huang family.
Show that the symmetric Huang family algorithms are conjugate direction
algorithms.
11.17 Write a MATLAB program to implement the quasi-Newton algorithm
for general functions. Use the secant method for the line search (e.g., the
MATLAB function of Exercise 7.11). Test the various update formulas for
Hk on Rosenbrock's function (see Exercise 9.4), with an initial condition
a?(°) = [—2,2]T. For this exercise, reinitialize the update direction to the
negative gradient every six iterations.
11.18 Consider the function
f(x) = -± + y - ΧλΧ2 +Xi-
X2.
216
QUASI-NEWTON METHODS
a. Use MATLAB to plot the level sets of / at levels -0.72, -0.6, - 0 . 2 , 0.5,
2. Locate the minimizers of / from the plots of the level sets.
b . Apply the DFP algorithm to minimize the function above with the following starting initial conditions: (i) [0,0] T ; (ii) [1.5,1] T . Use Ho = I2.
Does the algorithm converge to the same point for the two initial conditions? If not, explain.
CHAPTER 12
SOLVING LINEAR EQUATIONS
12.1
Least-Squares Analysis
Consider a system of linear equations
Ax = 6,
where A e R m X n , b G M m , m > n, and rank A = n. Note that the number
of unknowns, n, is no larger than the number of equations, m. If b does not
belong to the range of A, that is, if b 0 1Z(A), then this system of equations
is said to be inconsistent or overdetermined. In this case there is no solution
to the above set of equations. Our goal then is to find the vector (or vectors)
x minimizing \\Ax — b\\2. This problem is a special case of the nonlinear
least-squares problem discussed in Section 9.4.
Let x* be a vector that minimizes \\Ax — 6|| 2 ; that is, for all x G l n ,
\\Ax-b\\2>
||Ax*-6||2.
We refer to the vector x* as a least-squares solution to Ax = b. In the case
where Ax = b has a solution, then the solution is a least-squares solution.
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
217
218
SOLVING LINEAR EQUATIONS
Otherwise, a least-squares solution minimizes the norm of the difference between the left- and right-hand sides of the equation Ax = b. To characterize
least-squares solutions, we need the following lemma.
Lemma 12.1 Let A G M m x n , m > n. Then, rank A = n if and only if
rank A A = n (i.e., the square matrix A A is nonsingular).
□
Proof. =>: Suppose that rank A = n. To show rank A A = n, it is equivalent
to show λί(ΑτA)
= {0}. To proceed, let x G λί(ΑτA);
that is, ATAx = 0.
Therefore,
\\Axf = xTATAx
= 0,
which implies that Ax = 0. Because rank A = n, we have x = 0.
<=: Suppose that rank A A = n; that is, Λ/*(Α A) = {0}. To show
rank A = n, it is equivalent to show that λί(Α) = {0}. To proceed, let
x G Λ/*(Α); that is, Ax = 0. Then, AT Ax = 0, and hence x = 0.
I
Recall that we assume throughout that rank A = n. By Lemma 12.1 we
conclude that (AT A ) - 1 exists. The following theorem characterizes the leastsquares solution.
Theorem 12.1 The unique vector x* that minimizes \\Ax — b\\2 is given by
the solution to the equation A T A x = A b; that is, x* = (A A)~lA b.
D
Proof. Let x* = (ATA)-1ATb.
First observe that
\\Ax - b\\2 = \\A(x - x*) + (Ax* - &)||2
= (A(x - x*) + (Ax* - b))T(A{x
2
- x*) + (Ax* - b))
2
= ||A(x - x*)|| + ||Ax* - 6|| + 2[A(x - x*)] T (Ax* - b).
We now show that the last term in this equation is zero. Indeed, substituting
the expression above for x*,
[A(x - x*)] T (Ax* - b) - (x - x*)TAT[A(ATA)-1AT
= (x-
T
T
T
1 T
x*) [(A A){A A)- A
- In]b
- AT}b
= (x-x*)T(AT-AT)6
= 0.
Hence,
||Ax - 6|| 2 = ||A(x - x*)|| 2 + ||Ax* - 6|| 2 .
If x φ x*, then ||A(x — x*)|| 2 > 0, because rank A = n. Thus, if x φ χ*, we
have
| | A x - 6 | | 2 > ||Ax*-6||2.
Thus, x* = (ΑτΑ)~λA
b is the unique minimizer of ||Ax — 6|| 2 .
I
LEAST-SQUARES ANALYSIS
Figure 12.1
219
Orthogonal projection of b on the subspace ΊΖ(Α).
We now give a geometric interpretation of the Theorem 12.1. First note
that the columns of A span the range 1Z(A) of A, which is an n-dimensional
subspace of R m . The equation Ax = b has a solution if and only if b lies
in this n-dimensional subspace 1Z(A). If m = n, then b G 1Z(A) always,
and the solution is x* = A~lb. Suppose now that m > n. Intuitively, we
would expect the "likelihood" of b G 1Z( A) to be small, because the subspace
spanned by the columns of A is very "thin." Therefore, let us suppose that b
does not belong to 1Z(A). We wish to find a point h G 11(A) that is "closest"
to b. Geometrically, the point h should be such that the vector e = h — b
is orthogonal to the subspace 11(A) (see Figure 12.1). Recall that a vector
e G Mm is said to be orthogonal to the subspace 11(A) if it is orthogonal to
every vector in this subspace. We call h the orthogonal projection of b onto
the subspace 11(A). It turns out that h = Ax* = A(ATA)~1ATb.
Hence,
the vector h G H(A) minimizing \\b — h\\ is exactly the orthogonal projection
of b onto 1Z(A). In other words, the vector x* minimizing \\Ax — b\\ is exactly
the vector that makes Ax — b orthogonal to 1Z(A).
To proceed further, we write A = [αχ,..., α η ], where α ι , . . . , an are the
columns of A. The vector e is orthogonal to 1Z( A) if and only if it is orthogonal
to each of the columns α ι , . . . , an of A, To see this, note that
(e,o») = 0,
% = Ι,.,.,η
if and only if for any set of scalars {xi, X2? ...,^η}) we also have
(β,ΧιΟι Η
YXn^n) =
Any vector in 11(A) has the form Χχα,ι +
0.
h £ηαη·
Proposition 12.1 Let h G H(A) be such that h — b is orthogonal to 1Z(A).
Then, h = Ax* = A(ATA)~1Aib.
D
Proof. Because h G 1Z(A) = span[ai,... , a n ] , it has the form h = X\d\ +
• · · + Xn^ni where # i , . . . , xn G R. To find # i , . . . , x n , we use the assumption
220
SOLVING LINEAR EQUATIONS
that e = h — b is orthogonal to s p a n [ a i , . . . , a n ] ; that is, for alH = 1 , . . . , n,
we have
(h-b,a,i)
= 0,
or, equivalently,
(h,a,i) = {b,a,i).
Substituting h into the equations above, we obtain a set of n linear equations
of the form
(ai,ai)xi
H
h (an,a,i)xn
= (6,a»),
i = 1 , . . . ,n.
In matrix notation this system of n equations can be represented as
(αι,αι)
■··
<οι,α η >
(οη,αι)
hzi
(*>,αι)
An? Q"n)
%n
\^? ^ π /
Note that we can write
(ΟΙ,ΟΙ)
···
(αη,θι)
ATA =
(oi,on)
···
(an,an)
We also note that
αϊ
ai · · · anJ.
^
(6,ai)
= AT6 =
(b, on)
av
a'
Because rank A = n, A A is nonsingular, and thus we conclude that
Xl
X
=
Λ\-1 L l AT
= {A\Tl A)~
Ab
= x*.
Notice that the matrix
(θι,αι)
···
(οη,αι)
(αι,αη)
···
(an,an)
T
A A-
plays an important role in the least-squares solution. This matrix is often
called the Gram matrix (or Grammian).
LEAST-SQUARES ANALYSIS
221
An alternative method of arriving at the least-squares solution is to proceed
as follows. First, we write
f(x) =
=
\\Ax-b\\2
(Ax-b)T(Ax-b)
= ]-χΎ{2ΑΎΑ)χ
- xT(2ATb)
+ bTb.
Therefore, / is a quadratic function. The quadratic term is positive definite
because rank A = n. Thus, the unique minimizer of / is obtained by solving
the FONC (see Exercise 6.33); that is,
V/(x) = 2ATAx - 2ATb = 0.
The only solution to the equation V/(a?) = 0 is x* =
(ATA)~1ATb.
Example 12.1 Suppose that you are given two different types of concrete.
The first type contains 30% cement, 40% gravel, and 30% sand (all percentages
of weight). The second type contains 10% cement, 20% gravel, and 70% sand.
How many pounds of each type of concrete should you mix together so that
you get a concrete mixture that has as close as possible to a total of 5 pounds
of cement, 3 pounds of gravel, and 4 pounds of sand?
The problem can be formulated as a least-squares problem with
A =
Γθ.3
0.4
[0.3
O.l"
0.2
0.7
where the decision variable is x = [xi,^2] T and x\ and X2 are the amounts
of concrete of the first and second types, respectively. After some algebra, we
obtain the solution:
x* =
(ATA)-1ATb
1
Γ 0.54
~ (0.34)(0.54) - (0.32)2 | -0.32
-0.32] Γ3.9
0.34 | | 3.9
10.6
0.961
(For a variation of this problem solved using a different method, see Example 15.7.)
■
We now give an example in which least-squares analysis is used to fit measurements by a straight line.
222
SOLVING LINEAR EQUATIONS
Experimental Data for Example 12.2.
Table 12.1
0
2
3
i
U
Vi
1
3
4
2
4
15
Example 12.2 Line Fitting. Suppose that a process has a single input i E l
and a single output y e R. Suppose that we perform an experiment on the
process, resulting in a number of measurements, as displayed in Table 12.1.
The ith. measurement results in the input labeled U and the output labeled
t/i. We would like to find a straight line given by
y = mt + c
that fits the experimental data. In other words, we wish to find two numbers,
m and c, such that yi = mti + c, i = 0,1,2. However, it is apparent that
there is no choice of m and c that results in the requirement above; that is,
there is no straight line that passes through all three points simultaneously.
Therefore, we would like to find the values of m and c that best fit the data.
A graphical illustration of our problem is shown in Figure 12.2.
16
14
12
10
8
6
4
2
°0
Figure 12.2
1
2
3
4
5
^t
Fitting a straight line to experimental data.
LEAST-SQUARES ANALYSIS
223
We can represent our problem as a system of three linear equations of the
form
2m + c = 3
3m + c = 4
4m + c = 15.
We can write this system of equations as
Ax = 6,
where
A =
2
3
4
1
1
1
that since
6 =
3
4
15
® =
m
c
rank A < rank [A, 6],
the vector b does not belong to the range of A. Thus, as we have noted before,
the system of equations above is inconsistent.
The straight line of best fit is the one that minimizes
\Ax - 6||2 = ^2 (mti + c "~ Vi)2
2=0
Therefore, our problem lies in the class of least-squares problems. Note that
the foregoing function of m and c is simply the total squared vertical distance (squared error) between the straight line defined by m and c and the
experimental points. The solution to our least-squares problem is
x
77Γ
C*
(ATA)-1ATb
=
6
-32/3
Note that the error vector e = Ax* — b is orthogonal to each column of A.
I
Next, we give an example of the use of least-squares in wireless communications.
Example 12.3 Attenuation Estimation.
A wireless transmitter sends a
discrete-time signal {so, si, S2} (of duration 3) to a receiver, as shown in Figure 12.3. The real number s2 is the value of the signal at time i.
The transmitted signal takes two paths to the receiver: a direct path, with
delay 10 and attenuation factor ai, and an indirect (reflected) path, with delay
12 and attenuation factor α2· The received signal is the sum of the signals
from these two paths, with their respective delays and attenuation factors.
224
SOLVING LINEAR EQUATIONS
Si 4
Transmitter
Receiver
Wireless transmission in Example 12.3.
Figure 12.3
Suppose that the received signal is measured from times 10 through 14 as
?"io> r n , . . . , ri4, as shown in the figure. We wish to compute the least-squares
estimates of a\ and a 2 , based on the following values:
s2 n o
1 4
sp si
1 2
ri2
8
rn
7
n3
6
ri4
3 '
The problem can be posed as a least-squares problem with
Si
0"
0
S2
so
so
0
0
L
Sl
5
αλ
x =
a?, J
"nol
ni
b =
n2
ri3\
_r14_
2_
The least-squares estimate is given by
(ATA)-1ATb
=
\\s\\
S0S2
6
1
1
6
sos2
11*112
-1
1_
35 - 1
-1
50^10 + s i r n + s2r12
«0^*12 + SiTis + 5 2 ri4
4+14 + 8
8 + 12 + 3
-1 I 26
23
1_ 133]
35 112
■
LEAST-SQUARES ANALYSIS
225
We now give a simple example where the least-squares method is used in
digital signal processing.
Example 12.4 Discrete Fourier Series. Suppose that we are given a discretetime signal, represented by the vector
b= [6l,&2,...,&m] T ·
We wish to approximate this signal by a sum of sinusoids. Specifically, we
approximate b by the vector
+ Zk*{k))
yocM + J2 (Vk^
fc=l
where yo, y i , . . . , yn, zi, · · · , zn G l and the vectors c^
.(0)
Λ*)
1
1
1
and s^
are given by
|T
72'75''"'7i
.2/CTT\
cos ( 1
f
2kn\
] , cos I 2
I , . . . , cos I m
I , AC — 1 , . . . , 7 1 ,
m J
\ m I
\
m J
2kn\
2kn
2kn\
M
, k = 1 , . . . , n.
sin 1
, sin 2
, . . . , sin m
m
m )
\
m J
We call the sum of sinusoids above a discrete Fourier series (although,
strictly speaking, it is not a series but a finite sum). We wish to find
2/o, 2/i, · · ·, yn, zu · · ·, Zn such that
\ ^ + J2ykc^ + zksA-b\
k=l
is minimized.
To proceed, we define
c (0)
L·
X=
,
c(l)
L·
, . . . ,
c(n)
L·
, ö
(1)
(n)'
,...,ö
[2/0,2/ΐ,.··,2/η,^1,..·,^η]
·
Our problem can be reformulated as minimizing
||A*-&||2.
We assume that m > 2n+1. To find the solution, we first compute A A. We
make use of the following trigonometric identities: For any nonzero integer k
that is not an integral multiple of ra, we have
771
•
2_.cos I i
2=1
^
2kn\
m J
Λ
= 0'
226
SOLVING LINEAR EQUATIONS
With the aid of these identities, we can verify that
l·
)Tcü) -
0
otherwise
)m/2
s(k)T SU) =
'Uk
Ί0
_ Q
c(k)Ts(j)
= 3
otherwise
fQr a n y ^ j
Hence,
=
A^A
? I- .2 n + l >
2
which is clearly nonsingular, with inverse
(ATA)-1
= -J2n+i.
m
Therefore, the solution to our problem is
X
*
r
*
*
*
*
* i I
=[2/0»ί/ΐ>···)2/η.2;1.···.2„]
= (ATA)-1AT6
m
We represent the solution as
ΙΌ = — 2 > >
2/fc = — > ^ c o s U
,
m ^—'
\ m 1
2=1
m ^^
X
\
k
l,...,n,
'
m J
We call these discrete Fourier coefficients.
I
Finally, we show how least-squares analysis can be used to derive formulas
for orthogonal projectors.
Example 12.5 Orthogonal Projectors. Let V C R n be a subspace. Given a
vector x G R n , we write the orthogonal decomposition of x as
X = X\; + Xy-L,
where X\? G V is the orthogonal projection of x onto V and xv± G V1- is the
orthogonal projection of x onto V^. (See Section 3.3; also recall that V1" is
THE RECURSIVE LEAST-SQUARES ALGORITHM
227
the orthogonal complement of V.) We can write xy — Ρχ f° r some matrix
P called the orthogonal projector. In the following, we derive expressions for
P for the case where V = ΊΖ(Α) and the case where V = N(A).
Consider a matrix A G R m x n , m > n, and rank A = n. Let V = H(A)
be the range of A (note that any subspace can be written as the range of
some matrix). In this case we can write an expression for P in terms of
A, as follows. By Proposition 12.1 we have Xy = A(A A)~lA
x, whence
P = A(A A)~lAT.
Note that by Proposition 12.1, we may also write
x\> — arg min ||?/ — x\\.
yev
Next, consider a matrix A G R m X n , m < n, and rank A = m. Let
V = λί(Α) be the nullspace of A (note that any subspace can be written
as the nullspace of some matrix). To derive an expression for the orthogonal
projector P in terms of A for this case, we use the formula derived above and
the identity λί(Α)1- = ΊΖ(ΑΤ) (see Theorem 3.4). Indeed, if U = ΊΙ(ΑΎ),
then the orthogonal decomposition with respect to U is x = Xu + #t/-L,
where xu = AT(AAT)~1Ax
(using the formula derived above). Because
λί(Α)1- = 1Z(AT), we deduce that xv± = xu = AT(AAT)~lAx.
Hence,
xv = x-xv±=x-
AT(AAT)-1Ax
= (I - AT
{AAT)~lA)x.
Thus, the orthogonal projector in this case is P = I — A (AA
12.2
) _ 1 A.
I
The Recursive Least-Squares Algorithm
Consider again the example in Section 12.1 We are given experimental points
(£o>2/o)> (£i? 2/1)5 and (£2,2/2), and we find the parameters m* and c* of the
straight line that best fits these data in the least-squares sense. Suppose that
we are now given an extra measurement point (£3,2/3), so that we now have a
set of four experimental data points: (to, 2/0)5 (ti,Vi), (h, 2/2)5 and (£3,2/3). We
can similarly go through the procedure for finding the parameters of the line
of best fit for this set of four points. However, as we shall see, there is a more
efficient way: We can use previous calculations of m* and c* for the three
data points to calculate the parameters for the four data points. In effect,
we simply "update" our values of m* and c* to accommodate the new data
point. This procedure, called the recursive least-squares (RLS) algorithm, is
the topic of this section.
To derive the RLS algorithm, first consider the problem of minimizing
\\A0x — b^||2.
We know that the solution to this is given by x^
=
GQXAQ b^°\ where Go — ^ o ^ o - Suppose now that we are given more data,
in the form of a matrix A\ and a vector fr1'. Consider now the problem of
minimizing
6(0)
\\A° x — ' 6(1) 11
J
liA
228
SOLVING LINEAR EQUATIONS
The solution is given by
x(
1
)=Gr
-i T r
Ao
Αχ
1
6(o)·
6«1)
where
Ao
Αχ
G1
Ao
Αχ
Our goal is to write a;'1) as a function of χ(°), Go, and the new data Αχ and
&(J). To this end, we first write G\ as
χ
Ao
Αχ
AjAo
+
AjAx
Go +
AjAx.
Gx
Λ
Λ
ο
Next, we write
~AÖ
τ -b(oY
■6(0)-
=
Αχ_
A
0
l
&*1)
A0rbW+Ajb^
=
To proceed further, we write A^b^
A
as
A 0 T 6( 0 )=GoGö 1 A 0 T 6(°)
= G 0 x(°)
= (Gx -
AjAx)x^
= Gias<°> -
AjAx x (0)
Combining these formulas, we see that we can write χ(*) as
Ao
Αχ
x M=G?
6(0)·
= G r 1 (GXX^ - AjAxx^
= xM +
Ajb^)
G1-1Aj(bW-AxxM),
where Gx can be calculated using
Gx=G0
+
+
AjAx.
THE RECURSIVE LEAST-SQUARES ALGORITHM
229
We note that with this formula, x^ can be computed using only χ(°\ Αχ,
b^\ and Go- Hence, we have a way of using our previous efforts in calculating
χ(°ϊ to compute x^1), without having to compute x^ directly from scratch.
The solution x^ is obtained from x^ by a simple update equation that
adds to aj(°) a "correction term" G^Aj
(b{1) - Αχχ&Α.
Observe that if
the new data are consistent with the old data, that is, A\x^
— b^\ then
the correction term is 0 and the updated solution x^ is equal to the previous
solution χ(°\
We can generalize the argument above to write a recursive algorithm for
updating the least-squares solution as new data arrive. At the (k + l)th
iteration, we have
Gk+i — Gk +
Ak+1Ak+i
*<*+*> = *W + G^ATk+1
( b ^
- Ak+lX^)
.
The vector fr +1^ — Ak+ix^ is often called the innovation. As before, observe
that if the innovation is zero, then the updated solution x^ + 1 ^ is equal to the
previous solution x^k\
We can see from the above that to compute x ^ + 1 ^ from x^ we need
G j ^ , rather than Gfc+i. It turns out that we can derive an update formula
for Gk+X itself. To do so, we need the following technical lemma, which
is a generalization of the Sherman-Morrison formula (Lemma 11.1), due to
Woodbury, and hence also called the Sherman-Morrison- Woodbury formula
(see [63, p. 124] or [53, p. 50]).
L e m m a 12.2 Let A be a nonsingular matrix. Let U and V be matrices such
that I + VA~lU
is nonsingular. Then, A + UV is nonsingular, and
(A + UV)'1
= A'1
- {Α-λυ)(Ι
+
VA^Uy^VA-1).
D
Proof. We can prove the result easily by verification.
I
Using Lemma 12.2 we get
G
kll
= [Gk +
A
~k+lAk+l)
= G~kl - G~klATk+l{I
+
A^G^Al^-'A^G,1.
For simplicity of notation, we rewrite G^1 as PkWe summarize by writing the RLS algorithm using Pk:
■Pfc+i — Pk - .PfcAfc+1(J + Afc+iPfc^-fc+i)- Afc+iPfc,
*(*+!> = *(*) + Pk+1ATk+1
(&<fc+1> - Afc+1*<*>) .
230
SOLVING LINEAR EQUATIONS
In the special case where the new data at each step are such that Α^+ι
is a matrix consisting of a single row, Α^+ι = α^ +1 , and b^ +1^ is a scalar,
& ( f c + 1 ) =6 f c + i, we get
P f c + 1 = Pk
-
x(k+i) = x(k) +
( 6fc+1 _ al+,χΜ)
pMak+1
.
E x a m p l e 12.6 Let
A0 =
1
0
1
0
1
1
= [2 1],
Al=aJ
1
1
1
6<°> =
5
5
&(!) = 6i = [3]
A2 = a.2 = [3 ]I],
= [4]
b^=b2-
First compute the vector x^ minimizing \\AQX — b^\\2.
algorithm to find x^ minimizing
\\Ao~
\\Al
x—
\[A2
r& (o) i 1
ft(1)
&(2)
J
We have
P 0 = (A^Ao)-1
Then, use the RLS
=
*<°> = Ρ ο Λ Τ & ( 0 ) =
2/3
-1/3
-1/3
2/3
2/3
2/3
Applying the RLS algorithm twice, we get
P0aiaJP0
1 + aJP0ai
*W = *<°> + Ριαι
P 2 = Pi
x^=x^
-
+
1/3
-1/3
(b! - aj"aj<°>) =
Pia2aJPi
1 + aJPia2
1/6
-1/4
P2a2{b2-aJx^)
-1/3
2/3
1
2/3
-1/4
5/8
13/12
5/8
SOLUTION TO A LINEAR EQUATION WITH MINIMUM NORM
We can easily check our solution by computing x^
x& = {ATA)~1ATb,
where
231
directly using the formula
w
Άο
b = 6«
Αι
A2
6 (2)
■
12.3
Solution t o a Linear Equation with Minimum Norm
Consider now a system of linear equations
Ax — 6,
where A G R m x n , b G R m , m < n, and rank A = ra. Note that the number
of equations is no larger than the number of unknowns. There may exist
an infinite number of solutions to this system of equations. However, as we
shall see, there is only one solution that is closest to the origin: the solution
to Ax = b whose norm ||a?|| is minimal. Let x* be this solution; that is,
Ax* = b and ||x*|| < ||x|| for any x such that Ax = b. In other words, x* is
the solution to the problem
minimize
subject to
||x||
Ax = b.
In Part IV, we study problems of this type in more detail.
Theorem 12.2 The unique solution x* to Ax = b that minimizes the norm
\\x\\ is given by
x* =
AT(AAT)-1b.
D
Proof. Let x* - AT(AAT)-1b.
Note that
2
ll*H H|(*-**) + **||2
- ((* - x*) + x*)T((x - x*) + »*)
- ||x - x*||2 + ||**||2 + 2x*T(x - x*).
We now show that
χ*Ύ{χ-χ*)=0
.
x*T(x - x*) = [AT(AAT)-1b}T[x
-
Indeed,
= bT(AATy1[Ax
=
-
bT(AAT)-1[b-b]=0.
AT(AAT)-1b]
{ΑΑΤ){ΑΑΤ)-^}
232
SOLVING LINEAR EQUATIONS
Therefore,
ii
\\x\\
n2
=
ii
\\x
* n2
i ii
+
X ~ X
* n2
.
Because \\x — cc*||2 > 0 for all x φ x*, it follows that for all x Φ #*,
ll*ll2 > ΙΙ*ΊΙ2,
which implies that
x > x*
Example 12.7 Find the point closest to the origin of R 3 on the line of intersection of the two planes defined by the following two equations:
Xl + 2x2 - X3 = 1,
4#i + x2 + 3x 3 = 0.
Note that this problem is equivalent to the problem
minimize
subject to
where
1
4
2
1
||x||
Ax = 6,
-1
3
Thus, the solution to the problem is
0.0952
x* = AT(AATy1b
=
0.3333
-0.2381
In the next section we discuss an iterative algorithm for solving Ax = b.
12.4
Kaczmarz's Algorithm
As in Section 12.3, let A e R m x n , b G M m , m < n, and rank A = m. We now
discuss an iterative algorithm for solving Ax = 6, originally analyzed by Kaczmarz in 1937 [70]. The algorithm converges to the vector x* =
AT(AAT)~1b
without explicitly having to invert the matrix AA . This is important from
a practical point of view, especially when A has many rows.
Let aj denote the jth row of A, and bj the j t h component of 6, and μ a
positive scalar, 0 < μ < 2. With this notation, Kaczmarz's algorithm is:
KACZMARZ'S ALGORITHM
233
1. Set i := 0, initial condition χ(°\
2. For j = 1 , . . . ,ra, set
x (im+j)
= x{im+j-\)
+ μ
^
_ aJx(im+j-l)j
_^_
3. Set i := i + 1; go to step 2.
In words, Kaczmarz's algorithm works as follows. For the first ra iterations
(fc = 0 , . . . , ra — 1), we have
*(*+*> = * « + μ (bk+1 - aJ+lXW)
V
- ^ - ,
/ afc+1afc+i
where, in each iteration, we use rows of A and corresponding components of
b successively. For the (ra + l)th iteration, we revert back to the first row of
A and the first component of 6; that is,
x{rn
We continue with the (ra+2)th iteration using the second row of A and second
component of 6, and so on, repeating the cycle every ra iteration. We can view
the scalar μ as the step size of the algorithm. The reason for requiring that
μ be between 0 and 2 will become apparent from the convergence analysis.
We now prove the convergence of Kaczmarz's algorithm, using ideas from
Kaczmarz's original paper [70] and subsequent exposition by Parks [102].
Theorem 12.3 In Kaczmarz's algorithm, if x^
AT(AAT)-1b
ask-^oo.
= 0, then x^
—► x* =
D
Proof. We may assume without loss of generality that ||ai|| = l,z = l , . . . , r a .
For if not, we simply replace each a* by α^/||α|| and each bi by 6i/||ai||.
We first introduce the following notation. For each j = 0 , 1 , 2 , . . . , let R(j)
denote the unique integer in { 0 , . . . , ra — 1} satisfying j = /ra + R(j) for some
integer Z; that is, R(j) is the remainder that results if we divide j by ra.
Using the notation above, we can write Kaczmarz's algorithm as
* ( f c + 1 ) = x{k) + μ(&Λ(*)+ι - «i(fc) + i^ (fc) )oß(fc)+i·
Using the identity \\x + y\\2 = \\x\\2 + \\y\\2 + 2(x,y),
||x<*+D - χψ
= ||χ(*) - x* + /i(6 Ä(fc)+1 -
we obtain
aTRik)+1xW)aR{k)+1f
= \\x{k) ~ * Ί Ι 2 + M 2 (^(fe) + i - «i( f c ) + i* ( f c ) ) 2
+ MbR(k)+i ~ al(k)+ix(k))al{k)+i(v{h)
Substituting aft(fc)+1a:* =
|| β (*+ΐ) _ x*f
=
£>Λ(/;)+Ι
~ **)·
into this equation, we get
||χ(«0 _ x *||2 _
μ{2
_ M )(& R(fc)+1 - aj (fc)+1 x< fc >) 2
= \\xW ~ «ΊΙ 2 - M(2 - M)(aS (fc)+1 (x (fc) - x*)) 2 ·
234
SOLVING LINEAR EQUATIONS
Because 0 < μ < 2, the second term on the right-hand side is nonnegative,
and hence
Wxfr+V-x'fKWxW-x'f.
fc
Therefore, {||x^ ^ — x*|| 2 } is a nonincreasing sequence that is bounded below,
because \\x^ — x*\\2 > 0 for all k. Hence, {||a;^ — #*|| 2 } converges (see
Theorem 5.3). Furthermore, we may write
fc-l
11*« - * i 2 = |*<°> - *ΊΙ 2 - M(2 - μ) Σ(°Α(ο+ι( χ(<) - **))2·
2=0
fc
2
Because {||a;( ) — x*|| } converges, we conclude that
oo
$>i(i)+1(tf«-tf*))2<oo,
z=0
which implies that
4(fe)+i(^(fe)-^)-o.
Observe that
|| χ (* + ΐ) _ χ (*)||2
= ß2{bR[k)+1
_ α ϊ ( Λ ) + 1 χ ( « ) 2 = μ 2 (α£ (Λ)+1 (*<*> " ^*)) 2
and therefore ||a?(fe+1) - χΜ\\2 -+ 0. Note also that because {||»(fc) - x*|| 2 }
converges, {x^} is a bounded sequence (see Theorem 5.2).
Following Kaczmarz [70], we introduce the notation x^r^ = x(rrn+s\ r =
0 , 1 , 2 , . . . , s — 0 , . . . , m — 1. With this notation, we have, for each s =
0 , . . . ,ra — 1,
a]+1(x^
- x*) ^ 0
as r —> oo. Consider now the sequence {χ(Γ'°) : r > 0}. Because this sequence
is bounded, we conclude that it has a convergent subsequence—this follows
from the Bolzano-Weierstr ass theorem (see [2, p. 70]; see also Section 5.1 for
a discussion of sequences and subsequences). Denote this convergent subsequence by {a^ r ' 0 ) : r G £ } , where E is a subset of { 0 , 1 , . . . } . Let z* be the
limit of {aj(r'°) : r G £ } . Hence,
aj(z* -x*) = 0.
Next, note that because ||x( fc+1 ) — x^^|| 2 —► 0 as k —► oo, we also have
ΙΙχί7*»1) — χ( Γ '°)|| 2 —► 0 as r —► oo. Therefore, the subsequence {x^r^ : r G £}
also converges t o z * . Hence,
a j ( z * - x * ) = 0.
Repeating the argument, we conclude that for each i — 1 , . . . , m,
aj(z*-x')
= 0.
KACZMARZ'S ALGORITHM
235
In matrix notation, the equations above take the form
A(z*
-x*)=0.
Now, χΜ G ΊΙ{ΑΎ) for all k because χ(°) = 0 (see Exercise 12.25). Therefore, z* G 1Z(AT), because 1Z(AT) is closed. Hence, there exists y* such that
z* = ATy*. Thus,
A(z* - x*) = A(ATy*
AT(AAT)-1b)
-
= (AA T )y* - b
= 0.
Because rank A = m, y* = (AAT)~1b
and hence z* = x*. Therefore, the
subsequence {||x r ' 0 -cc*|| 2 : r G £} converges to 0. Because {||£cr'° —cc*||2 : r G
£} is a subsequence of the convergent sequence {||χ^ — x*|| 2 }, we conclude
that the sequence {||x^fc^ - x*\\2} converges to 0; that is, x^ —► x*.
I
For the case where χ^ φ 0, Kaczmarz's algorithm converges to the unique
point on {x : Ax = b} minimizing the distance \\x—x^\\ (see Exercise 12.26).
If we set μ = 1, Kaczmarz's algorithm has the property that at each iteration A:, the "error" £>#(&)+1 — ^ ( M + I 3 ^ " 1 " 1 ^ satisfies
&Ä(fc)+i ~ « Ä ( f c ) + i * ( f c + 1 ) = 0
(see Exercise 12.28). Substituting 6ß(fc)+i = &]i(k)+ix*i we may write
aTR{k)+1(x{k+1)-x*)
= 0.
Hence, the difference between a?(fe+1) and the solution x* is orthogonal to
a
Ä(fc)+i· This property is illustrated in the following example.
Example 12.8 Let
1
0
-1
1
b=
i
3
In this case, x* = [5,3] T . Figure 12.4 shows a few iterations of Kaczmarz's
algorithm with μ = 1 and x^ = 0. We have aj = [1,-1], a j — [0,1],
bi = 2, 62 = 3. In Figure 12.4, the diagonal line passing through the point
[2,0] T corresponds to the set {x : ajx = 61}, and the horizontal line passing
through the point [0,3] T corresponds to the set {x : ajx = 62}. To illustrate
236
SOLVING LINEAR EQUATIONS
x2t
x <2)
χ(4)
Iterations of Kaczmarz's algorithm in Example 12.8.
Figure 12.4
the algorithm, we perform three iterations:
x
X
(1)
0
+ (2-0);
0
(2)
1
-1
x^
=
+ (3-(-l))
+ (2-(-2))-
0
1
1
-1
As Figure 12.4 illustrates, the property
4 ( f e ) + i(* ( f c + 1 ) -**) = o
holds at every iteration. Note the convergence of the iterations of the algorithm to the solution.
I
12.5
Solving Linear Equations in General
Consider the general problem of solving a system of linear equations
Ax = 6,
where A G R m x n , and rank A = r. Note that we always have r < min{m, n}.
In the case where A G Mnxn and rank A = n, the unique solution to the
equation above has the form x* = A~lb. Thus, to solve the problem in this
case it is enough to know the inverse A~l. In this section we analyze a general
approach to solving Ax = b. The approach involves defining a pseudoinverse
or generalized inverse of a given matrix A G R m x n , which plays the role of
SOLVING LINEAR EQUATIONS IN GENERAL
237
A - 1 when A does not have an inverse (e.g., when A is not a square matrix).
In particular, we discuss the Moore-Penrose inverse of a given matrix A,
denoted A*.
In our discussion of the Moore-Penrose inverse we use the fact that a
nonzero matrix of rank r can be expressed as the product of a matrix of
full column rank r and a matrix of full row rank r. Such a factorization is
referred to as a full-rank factorization, a term which in this context was proposed by Gantmacher [45] and Ben-Israel and Greville [6]. We state and prove
the above result in the following lemma.
Lemma 12.3 Full-Rank Factorization.
Let A G M m x n , rank A = r <
min{ra,n}. Then, there exist matrices B G M m X r and C G M r x n such that
A = BC,
where
rank A = rank B = rank C = r.
D
Proof. Because rank A = r, we can find r linearly independent columns of
A. Without loss of generality, let α χ , α 2 , . . . , a r be such columns, where a;
is the zth column of A. The remaining columns of A can be expressed as
linear combinations of αι, 0 2 , . . . , o r . Thus, a possible choice for the full-rank
matrices B and C are
αι,.. . , a r ] G M m X r ,
[~i · * ·
[o ·
0 c1?r+1
1
Cr^r-\-i
· ·•
Ci,n
Cr,n
where the entries Q j are such that for each j = r + 1 , . . . , n, we have aj =
cijOi H
+ crjar. Thus, A = BC.
I
Note that if m < n and rank A = m, then we can take
B = JTm, C = A,
where J m is the m x m identity matrix. If, on the other hand, m > n and
rank A = n, then we can take
B = A, 0 = Ιη.
Example 12.9 Let A be given by
2
1
3
1
0
-1
-2 5
-3 2
-13 5
238
SOLVING LINEAR EQUATIONS
Note that rank A = 2. We can write a full-rank factorization of A based on
the proof of Lemma 12.3:
~2 1 "
A = 1 0
3 -1
1
0
0
1
=
BC.
We now introduce the Moore-Penrose inverse and discuss its existence and
uniqueness. For this, we first consider the matrix equation
AX A = A,
where A G R m x n is a given matrix and X G M n x m is a matrix we wish
to determine. Observe that if A is a nonsingular square matrix, then the
equation above has the unique solution X = A - 1 . We now define the MoorePenrose inverse, also called the pseudoinverse or generalized inverse.
Definition 12.1 Given A G R m x n , a matrix A1" G R n x m is called a pseudoinverse of the matrix A if
AA^A = A,
and there exist matrices U eRnxn,
A1" = UAT
V eRrnXrn
and
such that
A + = A T V.
The requirement A* = UAT = ATV can be interpreted as follows. Each
row of the pseudoinverse matrix A' of A is a linear combination of the rows
of A T , and each column of A^ is a linear combination of the columns of A .
For the case in which a matrix A G R m X n with m > n and rank A = n,
we can easily check that the following is a pseudoinverse of A:
Af =
(ATA)-1AT.
Indeed, A(ATA)-1ATA
= A, and if we define U = (AJ A ) " 1 and V =
T
1 T
1 T
A(A A)- (A A)- A ,
then A f = UAT = A T V . Note that, in fact,
we have A*A = In. For this reason, (ATA)-1AT
is often called the left
pseudoinverse of A. This formula also appears in least-squares analysis (see
Section 12.1).
For the case in which a matrix A G R m x n with m < n and rank A = m,
we can easily check, as we did in the previous case, that the following is a
pseudoinverse of A:
Af
=AT(AAT)-1.
SOLVING LINEAR EQUATIONS IN GENERAL
239
Note that in this case we have A A* = J m . For this reason, AT (AAT)~X is
often called the right pseudoinverse of A. This formula also appears in the
problem of minimizing ||x|| subject to Ax = b (see Section 12.3).
Theorem 12.4 Let A G R m x n . / / a pseudoinverse A* of A exists, then it is
unique.
□
Proof. Let A[ and A\ be pseudoinverses of A. We show that A[ = A\. By
definition,
€ R n X n , VUV2
and there are matrices UuU2
A{ = U1AT
e M m x m such that
=ATV1,
A\ = U2AT
=
A\,U
-UUV
ATV2.
Let
D = A\-
= U2
= V2-
VL
Then, we have
O = ADA, D = UAT =
ATV.
Therefore, using the two equations above, we have
(DA)TDA
= ATDTDA
= ATVTADA
= O,
which implies that
DA
On the other hand, because DA
DDT
= o.
= o, we have
= DAUT = o,
which implies that
D =and hence
-A =-O
Ά
2
-A-
4+ -
Ά2
-
Prom Theorem 12.4, we know that if a pseudoinverse matrix exists, then it
is unique. Our goal now is to show that the pseudoinverse always exists. In
fact, we show that the pseudoinverse of any given matrix A is given by the
formula
A+ =
C]B\
where B^ and C* are the pseudoinverses of the matrices B and C that form
a full-rank factorization of A; that is, A = BC, where B and C are of full
240
SOLVING LINEAR EQUATIONS
rank (see Lemma 12.3). Note that we already know how to compute B^ and
C+ =
B^ = (BTB)1BT,
CT{CCT)~1.
Theorem 12.5 Let a matrix A G R m x n have a full-rank factorization A —
BC, with rank A = r a n k ß = r a n k C = r, B G R m x r , C G R r x n . Then,
A f = C*B*.
D
Proof. We show that
A1" = C f B f =
CT(CCT)-\BTB)-1BT
satisfies the conditions of Definition 12.1 for a pseudoinverse. Indeed, first
observe that
AC^B^A = BCCT {CCT)l{BT
Next, define
and
B)lBT
BC = BC = A.
U =
CT(CCT)-1(BTB)-1{CCT)-1C
V =
B(BTB)-1(CCT)-1{BT'B)'1BT.
It is easy to verify that the matrices U and V above satisfy
A f - C f B f = UAT
Hence,
ATV.
=
A+ = C f B f
is the pseudoinverse of A.
Example 12.10 Continued from Example 12.9. Recall that
2
1 - 2 5
A = 1 0 - 3 2
3
-1
-13
2
1
3
5
1
0
-1
1 0 - 3 2
0 1 4
1
We compute
B^ =
(BTB)I 1DBT
and
f
C =
T
TT\ - l
C (CC )
5
27 16
"9
1
5
76 - 7
23
2
1
5
-11
5]
7
13
17_
BC.
SOLVING LINEAR EQUATIONS IN GENERAL
Thus,
Af =
C ^
2052
125
137
173
387
23
17
-1
63
241
-10
-52
-178
-72
We emphasize that the formula A' = C^B^ does not necessarily hold if
A = BC is not a full-rank factorization. The following example, which is
taken from [45], illustrates this point.
Example 12.11 Let
Λ-[,].
Obviously, A' = A - 1 = A = [1]. Observe that A can be represented as
[0 1]
BC.
The above is not a full-rank factorization of A. Let us now compute B^ and
C]. We have
Bt=BT(BBT)-1 =
& = (C^C) -1 «?" 1 " = [ l / 2
I/2] .
(Note that the formulas for B' and C' here are different from those in Example 12.10 because of the dimensions of B and C in this example.) Thus,
Ctflt
1/2
which is not equal to A^.
I
We can simplify the expression
Af - C f B f to
A* =
C^CC^-^B"^)-^7"
CT(BTACT)-1BT.
The expression above is easily verified simply by substituting A = BC. This
explicit formula for A' is attributed to C. C. MacDuffee by Ben-Israel and
Greville [6]. Ben-Israel and Greville report that around 1959, MacDuffee was
the first to point out that a full-rank factorization of A leads to the above
242
SOLVING LINEAR EQUATIONS
explicit formula. However, they mention that MacDufFee did it in a private
communication, so there is no published work by MacDufFee that contains the
result.
We now prove two important properties of A' in the context of solving a
system of linear equations Ax = b.
Theorem 12.6 Consider a system of linear equations Ax = b, A G IR mXn ,
rank A = r. The vector x* — A^b minimizes \\Ax — b\\2 onW1. Furthermore,
among all vectors in W1 that minimize \\Ax — b\\2, the vector x* = A^b is the
unique vector with minimal norm.
□
Proof. We first show that x* = A^b minimizes \\Ax — b\\2 over R n . To this
end, observe that for any x G Mn,
\\Ax - b\\2 = \\A(x - x*) + Ax" - b\\2
= \\A{x - x*)|| 2 + ||Ac* - 6|| 2 + 2[A(x - x*)] T (Az* - 6).
We now show that
[A(x-x*)]T(Ax*
-b)=0.
Indeed,
[A(x - x*)]T(Ax*
-b)
= (x-
x*)T(ATAx*
= (x-
x*)T(ATAAf6 -
-
ATb)
ATb).
However,
ΑΎΑΑ]
= CT ΒΎ BCCT {CCT)l{BT
Β)-λΒΎ
= AT.
Hence,
[A{x - x*)]T(Ax*
-b)
x*)T{ATb
= {x-
- ATb) = 0.
Thus, we have
||A* - 6||2 = \\A{x - x*)\\2 + ||Ax* - 6|| 2 .
Because
we obtain
||Α(χ-χ*)||2>0,
\\Ax-b\\2>
\\Ax*-b\\2
and thus x* minimizes \\Ax — b\\2.
We now show that among all x that minimize || Ax — 6|| 2 , the vector x* —
A^b is the unique vector with minimum norm. So let x be a vector minimizing
\\Ax — b\\2. We have
PHI2 = I K * - * * ) + * 1
2
= \\x - x*\\2 + ||x*|| 2 + 2x*T(x - x*).
SOLVING LINEAR EQUATIONS IN GENERAL
Observe that
243
x * T ( i - x * ) = 0.
To see this, note that
x*T(x-x*)
=
(A^b)T(x-A^b)
= bTB{BTB)-T(CCT)-TC(x
-
= bTB{BTB)~T(CCT)-T[Cx
-
CT(CCT)-1(BTB)-1BTb)
{BTB)-1BTb},
where the superscript — T denotes the transpose of the inverse. Now, \\Ax —
b\\2 = \\B{Cx) — b\\2. Because x minimizes \\Ax — b\\2 and C is of full rank,
then y* = Cx minimizes \\By — b\\2 over W (see Exercise 12.29). Because
B is of full rank, by Theorem 12.1, we have Cx = y* =
(BTB)1BTb.
T
Substituting this into the equation above, we get x* (x — x*) = 0.
Therefore, we have
||χ|| 2 =
For all x φ £c*, we have
and hence
| | * 1
2
+
||χ-ζ*|| 2 .
P-**ll 2 >o,
P||2>||**||2
or, equivalently,
||χ||>||χΊ|.
Hence, among all vectors minimizing \Ax — 6|| 2 , the vector x* = Ä*b is the
unique vector with minimum norm.
I
The generalized inverse has the following useful properties (see Exercise 12.30):
a. (A T )t = ( A f ) T .
b . (A f )t = A.
These two properties are similar to those that are satisfied by the usual matrix
inverse. However, we point out that the property (Α1Α2Ϋ = A^A\ does not
hold in general (see Exercise 12.32).
Finally, it is important to note that we can define the generalized inverse
in an alternative way, following the definition of Penrose. Specifically, the
Penrose definition of the generalized inverse of a matrix A G R m x n is the
unique matrix A* G R n x m satisfying the following properties:
1. A A + A = A.
2. A f A A f = A f .
244
SOLVING LINEAR EQUATIONS
3. ( A A f ) T =
AAl
4. ( A f A ) T - A f A.
The Penrose definition above is equivalent to Definition 12.1 (see Exercise 12.31). For more information on generalized inverses and their applications, we refer the reader to the books by Ben-Israel and Greville [6] and
Campbell and Meyer [23].
EXERCISES
12.1 A rock is accelerated to 3, 5, and 6 m/s 2 by applying forces of 1, 2,
and 3 N, respectively. Assuming that Newton's law F = ma holds, where F
is the force and a is the acceleration, estimate the mass m of the rock using
the least-squares method.
12.2 A spring is stretched to lengths L = 3, 4, and 5 cm under applied forces
F = 1, 2, and 4 N, respectively. Assuming that Hooke's law L = a + bF
holds, estimate the normal length a and spring constant b using the leastsquares approach.
12.3 Suppose that we perform an experiment to calculate the gravitational
constant g as follows. We drop a ball from a certain height and measure its
distance from the original point at certain time instants. The results of the
experiment are shown in the following table.
Time (seconds)
Distance (meters)
1.00
5.00
2.00
19.5
3.00
44.0
The equation relating the distance s and the time t at which s is measured
is given by
1
2*
2
a. Find a least-squares estimate of g using the experimental results from the
table above.
b . Suppose that we take an additional measurement at time 4.00 and obtain
a distance of 78.5. Use the recursive least-squares algorithm to calculate
an updated least-squares estimate of g.
12.4 Suppose that we have a speech signal, represented as a finite sequence of
real numbers xi, x2,..., xn- Suppose that we record this signal onto magnetic
tape. The recorded speech signal is represented by another sequence of real
numbers yi,y2, · . . ,yn>
EXERCISES
245
Suppose that we model the recording process as a simple scaling of the
original signal (i.e., we believe that a good model of the relationship between
the recorded signal and the original signal is yi = axi for some constant
a that does not depend on i). Suppose that we know exactly the original
signal #1,0:2,... , # n as well as the recorded signal 3/1,2/2» · · · >2/n· Use the
least-squares method to find a formula for estimating the scale factor a given
this data. (You may assume that at least one X{ is nonzero.)
12.5 Suppose that we wish to estimate the value of the resistance R of a
resistor. Ohm's law states that if V is the voltage across the resistor and
/ is the current through the resistor, then V = IR. To estimate R, we
apply a 1-ampere current through the resistor and measure the voltage across
it. We perform the experiment on n voltage-measuring devices and obtain
measurements of V\,..., Vn. Show that the least-squares estimate of R is
simply the average of V i , . . . , Vn.
12.6 The table below shows the stock prices for three companies, X, Y, and
Z, tabulated over three days:
X
Y
z
1
6
1
2
Day
2
4
1
1
3
5
3
2
Suppose that an investment analyst proposes a model for the predicting the
stock price of X based on those of Y and Z:
Px =apY + bpz,
where ρ χ , py, and pz are the stock prices of X, Y, and Z, respectively, and
a and b are real-valued parameters. Calculate the least-squares estimate of
parameters a and b based on the data in the table above.
12.7 We are given two mixtures, A and B. Mixture A contains 30% gold,
40% silver, and 30% platinum, whereas mixture B contains 10% gold, 20%
silver, and 70% platinum (all percentages of weight). We wish to determine
the ratio of the weight of mixture A to the weight of mixture B such that
we have as close as possible to a total of 5 ounces of gold, 3 ounces of silver,
and 4 ounces of platinum. Formulate and solve the problem using the linear
least-squares method.
12.8 Background: If Ax + w = b, where w is a, "white noise" vector, then
define the least-squares estimate of x given b to be the solution to the problem
minimize \\Ax — b\
246
SOLVING LINEAR EQUATIONS
This problem is related to Wiener filtering.
Application: Suppose that a given speech signal {uk : k = 1 , . . . , n} (with
Uk G R) is transmitted over a telephone cable with input-output behavior
given by yk = ayk-i + buk + Vk, where, at each time &, yk G R is the output,
Uk G R is the input (speech signal value), and Vk represents white noise. The
parameters a and b are fixed known constants, and the initial condition is
2/0 = 0.
We can measure the signal {yk} at the output of the telephone cable,
but we cannot directly measure the desired signal {uk} or the noise signal
{vk}- Derive a formula for the linear least-squares estimate of the signal
{uk : k — 1 , . . . , n} given the signal {yk : k = 1 , . . . , n } .
Note: Even though the vector v = [vi,..., vn]T is a white noise vector, the
vector Dv (where D is a matrix) is, in general, not.
12.9 Line Fitting, Let [#i,?/i] T ,..., [xp,yp]T, p > 2, be points in R 2 . We
wish to find the straight line of best fit through these points ("best" in the
sense that the total squared error is minimized); that is, we wish to find
a*, b* G R to minimize
p
/(a,6) = ^2(aXi-\-b-yi)2
.
i=l
Assume that the Xi, i — 1 , . . . ,p, are not all equal. Show that there exist
unique parameters a* and b* for the line of best fit, and find the parameters
in terms of the following quantities:
X
Ϋ
1
P
1
P
1
P
2=1
ΎΫ
1
V
1
P
2=1
12.10 Suppose that we take measurements of a sinusoidal signal y(t) —
sin(u;£ + Θ) at times t i , . . . , t p , and obtain values yi,..., yp, where — π/2 <
üjti + θ < π/2, i = 1 , . . . ,p, and the U are not all equal. We wish to determine
the values of the frequency ω and phase Θ.
EXERCISES
247
a. Express the problem as a system of linear equations.
b . Find the least-squares estimate of ω and Θ based on part a. Use the
following notation:
F
1
2=1
P
TY = - V^ U arcsinyi,
F
i=l
1
V
P
arcsin yi.
P?=i
12.11 We are given a point [xo, yo]T G R 2 . Consider the straight line on the
R 2 plane given by the equation y = mx. Using a least-squares formulation,
find the point on the straight line that is closest to the given point [ffo,2/o]j
where the measure of closeness is in terms of the Euclidean norm on R 2 .
Hint: The given line can be expressed as the range of the matrix A = [1, m ] T .
12.12 Consider the affine function / : R n —> R of the form f(x) = aTx + c,
where a G R n and c G R.
a. We are given a set of p pairs (»1,2/1),.. ·, (xp, yp), where X{, e R n , yi G R,
i — 1 , . . . ,p. We wish to find the affine function of best fit to these points,
where "best" is in the sense of minimizing the total square error
Yi(f(xi)-Vi)2
i=l
Formulate the above as an optimization problem of the form: minimize
\\Az — b\\2 with respect to z. Specify the dimensions of A, z, and b.
b . Suppose that the points satisfy
Xi H
h xp = 0
and
2/iXi 4-
h ypxp = 0.
Find the affine function of best fit in this case, assuming that it exists
and is unique.
248
SOLVING LINEAR EQUATIONS
Uk
Figure 12.5
► Vk
A
Input-output system in Exercise 12.13.
12.13 For the system shown in Figure 12.5, consider a set of input-output
pairs (wi,2/i),..., {un,yn), where uk G R, yk G R, k = 1 , . . . ,n.
a. Suppose that we wish to find the best linear estimate of the system based
on the input-output data above. In other words, we wish to find a θη G
R to fit the model yk = OnUk, k = l , . . . , n . Using the least-squares
approach, derive a formula for θη based onui,...,un
and y i , . . . , yn.
b . Suppose that the data in part a are generated by
yk = 9uk + efc,
where Θ G R and Uk = 1 for all k. Show that the parameter θη in part a
converges to Θ as n —» oo if and only if
1
n
lim - y ^ ek = 0.
n—»oo 77, ^—-^
fc=l
12.14 Consider a discrete-time linear system χ^+ι = αχ^ + buk, where Uk
is the input at time k, Xk is the output at time k, and a, 6 G R are system
parameters. Suppose that we apply a constant input Uk = 1 for all k > 0
and measure the first four values of the output to be XQ = 0, x\ — 1, x<i = 2,
x3 = 8. Find the least-squares estimate of a and b based on the data above.
12.15 Consider a discrete-time linear system Xk+i — axk + buk, where Uk
is the input at time fc, Xk is the output at time /c, and a, b G R are system
parameters. Given the first n + 1 values of the impulse response /io, · · ·, hn,
find the least-squares estimate of a and 6. You may assume that at least one
hk is nonzero.
Note: The impulse response is the output sequence resulting from an input
of UQ = 1, Uk = 0 for k φ 0 and zero initial condition XQ = 0.
12.16 Consider a discrete-time linear system x/c+i = axk + fax^, where life
is the input at time /c, x^ is the output at time /c, and a, 6 G R are system
parameters. Given the first n +1 values of the step response s o , . . . , sn, where
n > 1, find the least-squares estimate of a and b. You may assume that at
least one Sk is nonzero.
Note: The step response is the output sequence resulting from an input of
Uk = 1 for k > 0, and zero initial condition XQ = 0 (i.e., so = XQ = 0).
EXERCISES
249
12.17 Consider a known discrete-time signal on the time interval { 1 , . . . , n},
represented by the vector x G R n (xi is the value of the signal at time Ϊ). We
transmit the signal ax over a communication channel, where o G R represents
the "amplitude" of the transmission, a quantity unknown to the receiver.
The receiver receives a signal y G R n , which is a distorted version of the
transmitted signal (so that y may not be equal to ax for any a). Formulate
the problem of estimating the quantity a according to a least-squares criterion,
and solve it (stating whatever appropriate assumptions are necessary, if any).
12.18 Let A G R m x n , b G R m , m > n, and rank A = n. Consider the
constrained optimization problem
minimize
-x x — x b
2
x G 11(A),
subject to
where ΊΖ(Α) denotes the range of A. Derive an expression for the global
minimizer of this problem in terms of A and b.
12.19 Solve the problem
minimize
subject to
\\x
-x0
[l
11 1
1 11~U = 1,
where x0 = [ 0 , - 3 , 0 ] T .
12.20 Let A G R m x n , b G R m , m < n, rank A = m, and x0 G R n . Consider
the problem
minimize
subject to
\\x — Xo\\
Ax = b.
Show that this problem has a unique solution given by
x* - AT(AAT)~lb
12.21 Given A G R m x n , m>n,
the problem
minimize
+ (In -
AT(AAT)-1A)x0.
rank A = n, and & i , . . . , bp G R m , consider
\\Ax - 6i|| 2 + \\Ax - b2\\2 H
h \\Ax - bp\\2.
Suppose that x* is a solution to the problem
minimize ||Ax — ö^||2,
where i = 1 , . . . ,p. Write the solution to the problem in terms of x j , . . . , x*
250
SOLVING LINEAR EQUATIONS
12.22
Given A G R m x n , ra > n, rank A = n, bu...,bp
G R m , and
a i , . . . , ap G R, consider the problem
minimize
α ι | | Α χ — 611|2 -f 0:2!!Ax — 6 2 || 2 H
H a p | | A x — bp\\2.
Suppose that x* is the solution to the problem
minimize
11 Ax — bi \ | 2 ,
where i = 1 , . . . ,p. Assuming that a\ H
ha p > 0, derive a simple expression
for the solution to this problem in terms of x j , . . . , x* and c*i,..., ap.
12.23 Let A G R m x n , 6 G R m , m < n, and rank A = m. Show that
x* = A T ( A A ) _ 1 6 is the only vector in 1Z(A ) satisfying Ax* = 6.
12.24 The purpose of this question is to derive a recursive least-squares
algorithm where we remove (instead of add) a data point. To formulate the
algorithm, suppose that we are given matrices AQ and A\ such that
Ao
Ax
aj
where a\ G R n . Similarly, suppose that vectors 6 ^ and b^
satisfy
6<°> =
where b\ G R. Let χ(°) be the least-squares solution associated with (Ao, o^')
and x^1) the least-squares solution associated with (Αι,&^ 1 '). Our goal is to
write x^1) in terms of χ(°) and the data point "removed," (αχ,&ι). As usual,
let Go and G\ be the Grammians associated with χ(°) and x^l\ respectively.
a. Write down expressions for the least-squares solutions x^0) and x^1) in
terms of A 0 , 6 ( 0 ) , Au and b ( 1 ) .
b . Derive a formula for G\ in terms of Go and a,\.
c. Let Po = GQ1 and P i — G f λ . Derive a formula for P\ in terms of Po
and d\. (The formula must not contain any matrix inversions.)
d. Derive a formula for Ä^b^'
in terms of G i , χ(°\ and a\.
e. Finally, derive a formula for x^1) in terms of χ(°\ P i , αχ, and b\. Use
this and part c to write a recursive algorithm associated with successive
removals of rows
EXERCISES
12.25 Show that in Kaczmarz's algorithm, if χ<°) = 0, then x^
for all k.
12.26 Consider Kaczmarz's algorithm with x^
251
G ΊΖ(ΑΤ)
^ 0.
a. Show that there exists a unique point minimizing \\x — 2c(°)|| subject to
{x : Ax = b}.
b . Show that Kaczmarz's algorithm converges to the point in part a.
12.27 Consider Kaczmarz's algorithm with x^ = 0, where m = 1; that
is, A = [aT] G R l x n and a φ 0, and 0 < μ < 2. Show that there exists
0 < 7 < 1 such that ||x(fc+1) - x*\\ < 7 ||χ(*) - x*|| for all jfc > 0.
12.28
a
Show that in Kaczmarz's algorithm, if μ = 1, then &#(&)+1 —
ic +1
5(fc)+i ^ ^
=
^ *°Γ e a c n ^·
12.29 Consider the problem of minimizing \\Ax — b\\2 over R n , where A G
R m x n , b G R m . Let x* be a solution. Suppose that A = BC is a full-rank
factorization of A; that is, rank A = ranki? = r a n k C = r, and i? G R m x r ,
C G R r X n . Show that the minimizer of \\By - b\\ over R r is Cx*.
12.30 Prove the following properties of generalized inverses:
a. (A T )t = ( A 1 ) 7 .
b . (A f )t = A.
12.31 Show that the Penrose definition of the generalized inverse is equivalent
to Definition 12.1.
12.32 Construct matrices Αχ and A<i such that {ΑχΑ^
φ
AL2ALX.
CHAPTER 13
UNCONSTRAINED OPTIMIZATION AND
NEURAL NETWORKS
13.1
Introduction
In this chapter we apply the techniques of previous chapters to the training
of feedforward neural networks. Neural networks have found numerous practical applications, ranging from telephone echo cancellation to aiding in the
interpretation of EEG data (see, e.g., [108] and [72]). The essence of neural
networks lies in the connection weights between neurons. The selection of
these weights is referred to as training or learning. For this reason, we often
refer to the weights as the learning parameters. A popular method for training
a neural network is the backpropagation algorithm, based on an unconstrained
optimization problem and an associated gradient algorithm applied to the
problem. This chapter is devoted to a description of neural networks and the
use of techniques developed in preceding chapters for the training of neural
networks.
An artificial neural network is a circuit composed of interconnected simple
circuit elements called neurons. Each neuron represents a map, typically with
multiple inputs and a single output. Specifically, the output of the neuron
is a function of the sum of the inputs, as illustrated in Figure 13.1. The
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
253
254
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
Figure 13.1
Single neuron.
function at the output of the neuron is called the activation function. We use
the symbol shown in Figure 13.2 to represent a single neuron. Note that the
single output of the neuron may be used as an input to several other neurons,
and therefore the symbol for a single neuron has multiple arrows emanating
from it. A neural network may be implemented using an analog circuit. In
this case inputs and outputs may be represented by currents and voltages.
A neural network consists of interconnected neurons, with the inputs to
each neuron consisting of weighted outputs of other neurons. The interconnections allow exchange of data or information between neurons. In a feedforward neural network, the neurons are interconnected in layers, so that the
data flow in only one direction. Thus, each neuron receives information only
from neurons in the preceding layer: The inputs to each neuron are weighted
outputs of neurons in the preceding layer. Figure 13.3 illustrates the structure
of feedforward neural networks. The first layer in the network is called the
input layer, and the last layer is called the output layer. The layers in between
the input and output layers are called hidden layers.
We can view a neural network as simply a particular implementation of a
map from W1 to R m , where n is the number of inputs x\,... ,xn and m is
the number of outputs y\,..., ?/m. The map that is implemented by a neural network depends on the weights of the interconnections in the network.
Therefore, we can change the mapping that is implemented by the network
by adjusting the values of the weights in the network. The information about
the mapping is "stored" in the weights over all the neurons, and thus the
neural network is a distributed representation of the mapping. Moreover, for
any given input, computation of the corresponding output is achieved through
the collective effect of individual input-output characteristics of each neuron;
therefore, the neural network can be considered as a parallel computation
device. We point out that the ability to implement or approximate a map
encompasses many important practical applications. For example, pattern
Figure 13.2
Symbol for a single neuron.
INTRODUCTION
Input Layer
Figure 13.3
Hidden Layers
Output Layer
Structure of a feedforward neural network.
recognition and classification problems can be viewed as function implementation or approximation problems.
Suppose that we are given a map F : W1 —► R m that we wish to implement
using a given neural network. Our task boils down to selecting the interconnection weights in the network appropriately. As mentioned earlier, we
refer to this task as training of the neural network or learning by the neural
network. We use input-output examples of the given map to train the neural network. Specifically, let (iCd,i>2/d,i)j · · ·>(xd,p,Vd,p) € ^ n x ^ m > where
each ydi is the output of the map F corresponding to the input Xd,i\ that
is, ydti = F(xdyi).
We refer to the set {(a5d,i,!/d,i), · · · > (xd,P,Vd,p)} as the
training set We train the neural network by adjusting the weights such that
the map that is implemented by the network is close to the desired map F.
For this reason, we can think of neural networks as function approximators.
The form of learning described above can be thought of as learning with
a teacher. The teacher supplies questions to the network in the form of
x
d,\,..., Xd,P and tells the network the correct answers 2/d,i» · · ·»Vd,p· Training of the network then comprises applying a training algorithm that adjusts
weights based on the error between the computed and desired outputs; that
is, the difference between yd i = F(xd,i) and the output of the neural network
corresponding to xd^ Having trained the neural network, our hope is that
the network correctly generalizes the examples used in the training set. By
this we mean that the network should correctly implement the mapping F
and produce the correct output corresponding to any input, including those
that were not a part of the training set.
As we shall see in the remainder of this chapter, the training problem can
be formulated as an optimization problem. We can then use optimization
techniques and search methods (e.g., steepest descent, conjugate gradients
[69], and quasi-Newton) for selection of the weights. The training algorithms
are based on such optimization algorithms.
256
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
In the literature, for obvious reasons, the form of learning described above
is referred to as supervised learning, a term which suggests that there is also
a form of learning called unsupervised learning. Indeed, this is the case. However, unsupervised learning does not fit into the framework described above.
Therefore, we do not discuss the idea of unsupervised learning any further.
We refer the interested reader to [60].
13.2
Single-Neuron Training
Consider a single neuron, as shown in Figure 13.4. For this particular neuron,
the activation function is simply the identity (linear function with unit slope).
The neuron implements the following (linear) map from R n to R:
= 2_\wixi
— #TW5
where x = [ x i , . . . , # n ] T £ ^ n is the vector of inputs, y G R is the output,
and w = [wi,... ,wn]T G R n is the vector of weights. Suppose that we are
given a map F : R n —► R. We wish to find the value of the weights w\,..., wn
such that the neuron approximates the map F as closely as possible. To do
this, we use a training set consisting of p pairs {(asd,i,yd,i)> · · · > (xd,p,Vd,p)}i
where x^% G R n and yd,i G R , i = l , . . . , p . For each i, yd^ = F(xdJi) is the
"desired" output corresponding to the given input Xd,i. The training problem
can then be formulated as the following optimization problem:
minimize
1 p
- ^ (yd4 -
xjtiw)'
where the minimization is taken over all w — [wi,..., wn]T G R n . Note that
the objective function represents the sum of the squared errors between the
desired outputs y^i and the corresponding outputs of the neuron xJiW. The
factor of 1/2 is added for notational convenience and does not change the
minimizer.
The objective function above can be written in matrix form as follows.
First define the matrix Xd G R n X p and vector yd G W by
Xd = [xd,i
' ^d.i
2/d,i
Vd =
Vd,P
Then, the optimization problem becomes
minimize
hyd-XTdw\\2.
SINGLE-NEURON TRAINING
257
^ y
Figure 13.4
Single linear neuron.
There are two cases to consider in this optimization problem: p < n and
p > n. We first consider the case where p < n, that is, where we have at most
as many training pairs as the number of weights. For convenience, we assume
that rank Xd = p. In this case there are an infinitely many points satisfying
yd = Xd w. Hence, there are infinitely many solutions to the optimization
problem above, with the optimal objective function value of 0. Therefore,
we have a choice of which optimal solution to select. A possible criterion for
this selection is that of minimizing the solution norm. This is exactly the
problem considered in Section 12.3. Recall that the minimum-norm solution
is w* = Xd(Xd Xd)-1 Vd- An efficient iterative algorithm for finding this
solution is Kaczmarz's algorithm (discussed in Section 12.4). Kaczmarz's
algorithm in this setting takes the form
\\Xd,R(h)+l\\
where w^
= 0 and
ek = 2/d,Ä(fc)+i -
T
(k)
ttd,Ä(fc)+i^
·
Recall that R(k) is the unique integer in { 0 , . . . ,p— 1} satisfying k = lp+R(k)
for some integer /; that is, R(k) is the remainder that results if we divide k
by p (see Section 12.4 for further details on the algorithm).
The algorithm above was applied to the training of linear neurons by
Widrow and Hoff (see [132] for some historical remarks). The single neuron together with the training algorithm above is illustrated in Figure 13.5
and is often called Adaline, an acronym for adaptive linear element.
We now consider the case where p > n. Here, we have more training points
than the number of weights. We assume that r a n k X j = n. In this case
the objective function \\[yd — Xdw\\2 *s simply a strictly convex quadratic
function of w, because the matrix XdXd
is a positive definite matrix. To
solve this optimization problem, we have at our disposal the whole slew of
unconstrained optimization algorithms considered in earlier chapters. For
example, we can use a gradient algorithm, which in this case takes the form
w(^)=wW+akXde^\
258
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
Figure 13.5
Adaline.
where e^ =ydXjw^k\
The discussion above assumed that the activation function for the neuron
is the identity map. The derivation and analysis of the algorithms can be
extended to the case of a general differentiable activation function fa. Specifically, the output of the neuron in this case is given by
y= α
$ (Σ
WiXi = a xTw
) f ( ) ·
The algorithm for the case of a single training pair (x<i, yd) has the form
where the error is given by
ek = yd~ fa (*Jw ( f c ) ) ·
For a convergence analysis of the algorithm above, see [64].
13.3
The Backpropagation Algorithm
In Section 13.2 we considered the problem of training a single neuron. In this
section we consider a neural network consisting of many layers. For simplicity
of presentation, we restrict our attention to networks with three layers, as
depicted in Figure 13.6. The three layers are referred to as the input, hidden,
and output layers. There are n inputs x^ where i = 1 , . . . ,n. We have m
outputs ys, s = 1 , . . . , m. There are / neurons in the hidden layer. The outputs
THE BACKPROPAGATION ALGORITHM
Input
Layer
Hidden
Layer
Output
Layer
i
j
k
Figure 13.6
259
Three-layered neural network.
of the neurons in the hidden layer are Zj, where j = 1 , . . . ,Z. The inputs
distributed to the neurons in the hidden layer. We may think
of the neurons in the input layer as single-input-single-output linear elements,
with each activation function being the identity map. In Figure 13.6 we do
not explicitly depict the neurons in the input layer; instead, we illustrate the
neurons as signal spliters. We denote the activation functions of the neurons
in the hidden layer by / j 1 , where j — 1 , . . . , /, and the activation functions of
the neurons in the output layer by f°, where s = 1 , . . . ,ra. Note that each
activation function is a function from R to M.
We denote the weights for inputs into the hidden layer by w^, i = 1 , . . . , n,
j = 1 , . . . , /. We denote the weights for inputs from the hidden layer into the
output layer by w°j, j = 1 , . . . , / , s = l , . . . , r a . Given the weights w^ and
Wgj, the neural network implements a map from M.n to W71. To find an explicit
formula for this map, let us denote the input to the jth neuron in the hidden
layer by Vj and the output of the j t h neuron in the hidden layer by Zj. Then,
we have
The output from the sth neuron of the output layer is
ys = f°s Σ < ^j .
260
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
Therefore, the relationship between the inputs #;, i = 1 , . . . ,n, and the 5th
output ys is given by
\J=1
\t=l
= -Γ β ^Χΐ , . . . , Χγι)-
The overall mapping that the neural network implements is therefore given
by
Fi(xi,...,xn)
V\
Vn
■Tm\%l ? · · · ) ^ n j
We now consider the problem of training the neural network. As for the
single neuron considered in Section 13.2, we analyze the case where the training set consists of a single pair (xd,yd), where xd G Mn and yd G R m . In
practice, the training set consists of many such pairs, and training is typically
performed with each pair at a time (see, e.g., [65] or [113]). Our analysis is
therefore also relevant to the general training problem with multiple training
pairs.
The training of the neural network involves adjusting the weights of the
network such that the output generated by the network for the given input
Xd = [xdi, · · · ,Xdn]T is as close to yd as possible. Formally, this can be
formulated as the following optimization problem:
minimize
^
m
Z
8=1
■Va)
where ys, s = 1 , . . . , ra, are the actual outputs of the neural network in response to the inputs xd\, · . . , Xdn, as given by
\i=l
This minimization is taken over all w^, w°j, i = Ι , . , . , η , j = 1 , . . . , / , s =
1 , . . . , m. For simplicity of notation, we use the symbol w for the vector
w = {w^w°sj
:i = l , . . . , n , j = 1 , . . . , / , s = l , . . . , r a }
THE BACKPROPAGATION ALGORITHM
261
and the symbol E for the objective function to be minimized; that is,
m
1
( ) = ö2 , Σ (y<js ~ y^2
E w
=1
To solve the optimization problem above, we use a gradient algorithm with
fixed step size. To formulate the algorithm, we need to compute the partial
derivatives of E with respect to each component of w. For this, let us first
fix the indices i, j , and s. We first compute the partial derivative of E with
respect to w°y For this, we write
where for each q = 1 , . . . , /,
z
* = f£ [Σ™*χ*] ·
Using the chain rule, we obtain
BE
i-(w) = - (yds - ys) f°'
sj
sj
^2w°sqzq
\q=\iq=
Zj,
)
where / ° : R —► R is the derivative of / ° . For simplicity of notation, we write
as = (yds ~ y8) fs ί Σ
w
wz* )
We can think of each Ss as a scaled output error, because it is the difference
between the actual output ys of the neural network and the desired output
yds, scaled by / ° (Σσ=ι w°qzq)·
dE
dwSJ
Using the Ss notation, we have
(
\
x
We next compute the partial derivative of E with respect to w^. We start
with the equation
E(*) = \ Σ (väp ~ f°P
(Σ
«&/* (it < * * ) ) ) ·
262
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
Using the chain rule once again, we get
/
m
dE
w
h
(
)
=
~
Σ
( » * ~ Vr) fp
ßw
UW
ji
p=l
'
Σ
\g=l
w
mzi
/
» y f
Σ
\r=l
w
Jrxdr
/
Xdi,
where /'3j 1 : E —> E is the derivative of f!f. Simplifying the above yields
dE
9w
?i
•7*
(«o = -v Σ*Ρ<)//(^*.
i
/
\p=l
/
We are now ready to formulate the gradient algorithm for updating the
weights of the neural network. We write the update equations for the two sets
of weights w°j and w^ separately. We have
\P=1
where η is the (fixed) step size and
(fc)
v^
i=l
z{k)
3
M*0
3*
=/;W fc) )·
öik) = (yds-y^f?(jtw?qk)4k))·
The update equation for the weights w°j of the output layer neurons is illustrated in Figure 13.7, whereas the update equation for the weights w^ of the
hidden layer neurons is illustrated in Figure 13.8.
The update equations above are referred to in the literature as the backpropagation algorithm. The reason for the name backpropagation is that the
output errors δ[ ',... ,6m are propagated back from the output layer to the
hidden layer and are used in the update equation for the hidden layer weights,
as illustrated in Figure 13.8. In the discussion above we assumed only a single
hidden layer. In general, we may have multiple hidden layers—in this case the
update equations for the weights will resemble the equations derived above.
In the general case the output errors are propagated backward from layer to
layer and are used to update the weights at each layer.
THE BACKPROPAGATION ALGORITHM
263
s-th output neuron
From
Hidden
Layer
To
Hidden
Layer
o(k+1)
w,sj
Figure 13.7
Illustration of the update equation for the output layer weights.
We summarize the backpropagation algorithm qualitatively as follows. Using the inputs Xdi and the current set of weights, we first compute the quantities Vj , Zj , yi \ and öi , in turn. This is called the forward pass of the
algorithm, because it involves propagating the input forward from the input
layer to the output layer. Next, we compute the updated weights using the
quantities computed in the forward pass. This is called the reverse pass of the
algorithm, because it involves propagating the computed output errors 6s
backward through the network. We illustrate the backpropagation procedure
numerically in the following example.
Example 13.1 Consider the simple feedforward neural network shown in
Figure 13.9. The activation functions for all the neurons are given by f(v) =
1/(1 + e~v). This particular activation function has the convenient property
264
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
j-th hidden neuron
(k)
i
To Output
Layer
M
M
From
Output
Layer
h(k+l)
W;
Figure 13.8
Illustration of the update equation for the hidden layer weights.
THE BACKPROPAGATION ALGORITHM
265
*-yi
Figure 13.9
that f'{v) = f(v){l — f(v))<*i = (yd-yi)f
Neural network for Example 13.1.
Therefore, using this property, we can write
\^2w<iqzq
w
= (yd-yi)flJ2w°qz<*)
= (2/d-2/i)2/i(l-2/i).
(IH)
Suppose that the initial weights are wx[ = 0 . 1 , w^ = 0.3, w2[ = 0.3,
= 0.4, wir = 0.4, and w$} = 0.6. Let xd = [0.2,0.6] T and yd = 0.7.
Perform one iteration of the backpropagation algorithm to update the weights
of the network. Use a step size of 77 = 10.
To proceed, we first compute
HO)
w.22
v[0) = wi[0)xdl
„(o)
+ wQ0)xd2
= 0.2,
r
,M°)Xd\ + ^ 2h(0)
^21
2 Xd2 = 0 . 3 .
Next, we compute
40) = M0)) = Ί + e-°·2 *Γ = / Κ ' ) = Ϊ
1
+ e -0.3
0.5498,
= 0.5744.
We then compute
„(°> = / (wTz[0) + *>T40)) = /(0.5646) = 0.6375,
266
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
which gives an output error of
ö[V
0.01444.
= (yd-yW)y[°\l-yW)=
This completes the forward pass.
To update the weights, we use
w°i1] =w°{20) +ηδ[0)ζί0)
and, using the fact that f'{vf])
WW
= „MO
«,#> = „#°>
= f(vf])(l
=0.6830,
- f(vf))
= zf\l
- zf\
+ vsWwo(o)zW{1
_ z(o))xdi
= 0 1029)
r,sWwof)zW{1
_ z(o))xd2
=
+
we get
a3086)
«&(1) = «# 0) + ^ 0) <4 0) 4 0) (1 - 4 0) )^i = 0.3042,
# = «&0) + ^ 0 ) ^ 0 ) 4 0 ) ( 1 - 4 ° W = 0.4127.
Thus, we have completed one iteration of the backpropagation algorithm. We
can easily check that y[ ' = 0.6588, and hence \y<i — y[ I < \yd — y[ |; that
is, the actual output of the neural network has become closer to the desired
output as a result of updating the weights.
After 15 iterations of the backpropagation algorithm, we get
w^ 15 ) = 0.6365,
15)
= 0.8474,
w^
(15)
^
= 0.1105,
15
w ^ > = 0.3315,
(15)
w,2! = 0.3146,
^
™22
= 0.4439.
The resulting value of the output corresponding to the input Xd = [0.2,0.6] T
is ^ 1 5 ) = 0.6997.
■
In the example above, we considered an activation function of the form
/(")
1
1 + e-
This function is called a sigmoid and is a popular activation function used in
practice. The sigmoid function is illustrated in Figure 13.10. It is possible to
use a more general version of the sigmoid function, of the form
l \
β
THE BACKPROPAGATION ALGORITHM
Figure 13.10
267
Sigmoid function.
The parameters ß and Θ represent scale and shift (or location) parameters
respectively. The parameter Θ is often interpreted as a threshold. If such
an activation function is used in a neural network, we would also want to
adjust the values of the parameters ß and 0, which also affect the value of
the objective function to be minimized. However, it turns out that these
parameters can be incorporated into the backpropagation algorithm simply
by treating them as additional weights in the network. Specifically, we can
represent a neuron with activation function g as one with activation function
/ with the addition of two extra weights, as shown in Figure 13.11.
1+e-(vi"e)
ν2=ν1-θ
Figure 13.11
Two configurations that are equivalent.
268
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
Example 13.2 Consider the same neural network as in Example 13.1. We
introduce shift parameters 0i, 02, and 03 to the activation functions in the
neurons. Using the configuration illustrated in Figure 13.11, we can incorporate the shift parameters into the backpropagation algorithm. We have
vi = w^Xdi
+ Wi2Xd2 - 0i,
V2 = W21Xdl + W%2xd2 ~ #2,
Zl = / ( V l ) ,
Z2 =
f(v2),
2/1 = / (w^Zi
+ w{2Z2 - 0 3 ) ,
i i = (2/rf — 2/i)2/i(l - 2 / 1 ) ,
where / is the sigmoid function:
The components of the gradient of the objective function E with respect to
the shift parameters are
8E_
(w)
901
dE
(w)
Ö02
as (w)
Ö03
5^1^1(1 -Zi),
öiw°2z2{l - z2),
δι.
■
In the next example, we apply the network discussed in Example 13.2 to
solve the celebrated exclusive OR (XOR) problem (see [113]).
Example 13.3 Consider the neural network of Example 13.2. We wish to
train the neural network to approximate the exclusive OR (XOR) function,
defined in Table 13.1. Note that the XOR function has two inputs and one
output.
To train the neural network, we use the following training pairs:
*d,i = [0,0] T ,
T
yd,i=0,
Xd,2 = [0,1] ,
Xd,3 = [1,0] T ,
2/d,2 = 1,
yd,3 = 1,
xdA = [1,1] T ,
VdA = 0 ·
We now apply the backpropagation algorithm to train the network using the
training pairs above. To do this, we apply one pair per iteration in a cyclic
THE BACKPROPAGATION ALGORITHM
Truth Table for XOR Function
Table 13.1
X\
F(xi,x2)
0
1
1
0
X2
0
0
1
1
Table 13.2
269
0
1
0
1
Response of the Trained Network of Example 13.3
x\
0
0
1
1
X2
2/i
0
1
0
1
0.007
0.99
0.99
0.009
fashion. In other words, in the /cth iteration of the algorithm, we apply the
pair (xd,R(k)+i,yd,R(k)+i)i where, as in Kaczmarz's algorithm, R(k) is the
unique integer in {0,1,2,3} satisfying k = 4/ + R(k) for some integer /; that
is, R(k) is the remainder that results if we divide A: by 4 (see Section 12.4).
The experiment yields the following weights (see Exercise 13.5):
w°n = - 1 1 . 0 1 ,
<2
— 10.92,
«tfl
= -7.777,
«>12
= -8.403,
™21 = - 5 . 5 9 3 ,
« & = -5.638,
0i = - 3 . 2 7 7 ,
02 = - 8 . 3 5 7 ,
03 = 5.261.
Table 13.2 shows the output of the neural network with the weights above
corresponding to the training input data. Figure 13.12 shows a plot of the
function that is implemented by this neural network.
I
For a more comprehensive treatment of neural networks, see [58], [59], or
[137]. For applications of neural networks to optimization, signal processing,
and control problems, see [28] and [67].
270
UNCONSTRAINED OPTIMIZATION AND NEURAL NETWORKS
Figure 13.12
Example 13.3.
Plot of the function implemented by the trained network of
EXERCISES
13.1 Consider a single linear neuron, with n inputs (see Figure 13.4). Suppose
that we are given Xd G R n x p and yd G MP representing p training pairs, where
p > n. The objective function to be minimized in the training of the neuron
is
f(w) =
\\\yd-XTdwf.
a. Find the gradient of the objective function.
b . Write the conjugate gradient algorithm for training the neuron.
c. Suppose that we wish to approximate the function F : M2 —> R given by
F(x) = (sin #i) (cos #2)·
Use the conjugate gradient algorithm from part b to train the linear
neuron, using the following training points:
{x : £ι,#2 = —0.5,0,0.5}.
It may helpful to use the MATLAB program from Exercise 10.11.
d. Plot the level sets of the objective function for the problem in part c, at
levels 0.01, 0.1, 0.2, and 0.4. Check if the solution in part c agrees with
the level sets.
EXERCISES
271
e. Plot the error function e(x) = F(x) — w*Tx versus X\ and #2? where w*
is the optimal weight vector obtained in part c.
13.2 Consider the Adaline, depicted in Figure 13.5. Assume that we have a
single training pair (xd, yd), where Xd φ 0. Suppose that we use the WidrowHoff algorithm to adjust the weights:
X
d
where e^—yd —
Xd
xjw^k\
a. Write an expression for e^+i as a function of e& and μ.
b . Find the largest range of values for μ for which e^ —► 0 (for any initial
condition it/ 0 )).
13.3 As in Exercise 13.2, consider the Adaline. Consider the case in which
there are multiple pairs in the training set {(xd,i, 2/d,i)> · · · j (xd,p, 2/d,p)}> where
p <n and rankX^ = p (the matrix Xd has x^% as its zth column). Suppose
that we use the following training algorithm:
„,(*+!> = „,(*) +
where e^
= yd — Xjw^
Xd{XJxd)-^e^\
and μ is a given constant p x p matrix.
a. Find an expression for e^ + 1 ^ as a function of e^
and μ.
b . Find a necessary and sufficient condition on μ for which e^
any initial condition w^).
—► 0 (for
13.4 Consider the three-layered neural network described in Example 13.1
(see Figure 13.9). Implement the backpropagation algorithm for this network
in MATLAB. Test the algorithm for the training pair Xd = [0,1] T and yd = 1.
Use a step size of η = 50 and initial weights as in the Example 13.1.
13.5 Consider the neural network of Example 13.3, with training pairs for the
XOR problem. Use MATLAB to implement the training algorithm described
in Example 13.3, with a step size of η = 10.0. Tabulate the outputs of the
trained network corresponding to the training input data.
CHAPTER 14
GLOBAL SEARCH ALGORITHMS
14.1
Introduction
The iterative algorithms in previous chapters, in particular gradient methods,
Newton's method, conjugate gradient methods, and quasi-Newton methods,
start with an initial point and then generate a sequence of iterates. Typically,
the best we can hope for is that the sequence converges to a local minimizer.
For this reason, it is often desirable for the initial point to be close to a global
minimizer. Moreover, these methods require first derivatives (and also second
derivatives in the case of Newton's method).
In this chapter we discuss various search methods that are global in nature in the sense that they attempt to search throughout the entire feasible
set. These methods use only objective function values and do not require
derivatives. Consequently, they are applicable to a much wider class of optimization problems. In some cases, they can also be used to generate "good"
initial (starting) points for the iterative methods discussed in earlier chapters.
Some of the methods we discuss in this chapter (specifically, the randomized search methods) are also used in combinatorial optimization, where the
feasible set is finite (discrete), but typically large.
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
273
274
GLOBAL SEARCH ALGORITHMS
14.2
The Nelder-Mead Simplex Algorithm
The method originally proposed by Spendley, Hext, and Himsworth [122] in
1962 was improved by Neider and Mead [97] in 1965 and it is now commonly
referred to as the Nelder-Mead simplex algorithm. A contemporary view of
the algorithm is provided in the well-written paper by Lagarias et al. [82]. In
our exposition, we use the notation of this paper.
The Nelder-Mead algorithm is a derivative-free method. The method uses
the concept of a simplex. A simplex is a geometric object determined by an
assembly of n + 1 points, p0>Pi> · · · iPm m the ^-dimensional space such that
det Ρθ
1
Pi
1
· ·
Pn
1
^0.
This condition ensures that two points in R do not coincide, three points in
R 2 are not colinear, four points in R 3 are not coplanar, and so on. Thus,
simplex in R is a line segment, in R 2 it is a triangle, while a simplex in R 3 is
a tetrahedron; in each case it encloses a finite n-dimensional volume.
Suppose that we wish to minimize / ( # ) , x G R n . To start the algorithm,
we initialize a simplex of n + 1 points. A possible way to set up a simplex,
as suggested by Jang, Sun, and Mizutani [67], is to start with an initial point
x(o) _ p o a n c j g e n e r a t e the remaining points of the initial simplex as follows:
Pi = Po + ^ e n
i = 1,2,..., n,
where the e^ are unit vectors constituting the natural basis of R n as described
in Section 2.1. The positive constant coefficients λ^ are selected in such a way
that their magnitudes reflect the length scale of the optimization problem.
Our objective is to modify the initial simplex stage by stage so that the resulting simplices converge toward the minimizer. At each iteration we evaluate
the function / at each point of the simplex. In the function minimization process, the point with the largest function value is replaced with another point.
The process for modifying the simplex continues until it converges toward the
function minimizer.
We now present the rules for modifying the simplex stage by stage. To aid
in our presentation, we use a two-dimensional example to illustrate the rules.
We begin by selecting the initial set of n + 1 points that are to form the initial
simplex. We next evaluate / at each point and order the n + 1 vertices to
satisfy
/ ( Ρ θ ) < / ( Ρ ΐ ) < •••</(Pn)·
For the
simplex
wish to
and pnl
two-dimensional case we let ph pnl, and ps denote the points of the
for which / is largest, next largest, and smallest; that is, because we
minimize / , the vertex ps is the best vertex, pl is the worst vertex,
is the next-worst vertex. We next compute pg, the centroid (center
THE NELDER-MEAD SIMPLEX ALGORITHM
Pr=Pg +
Figure 14.1
275
p(Pg-Pl)
Reflecting pt in p with a reflection coefficient p.
of gravity) of the best n points:
n-l
Σ Ρΐ
In our two-dimensional case, n = 2, we would have
1
/
Pg = 2 VPnl+Ps)'
We then reflect the worst vertex, pl, in pg using a reflection coefficient p > 0
to obtain the reflection point
Pr=Pg+p(Pg-Pl)'
A typical value is p = 1. The operation above is illustrated in Figure 14.1.
We proceed to evaluate / at pr to obtain fr = f (p r )· If /o < fr < fn-i [i-e.,
if fr lies between fs = f {ps) a n d fni = f {ρηι)]·>tnen t n e P o m t Pr replaces pl
to from a new simplex, and we terminate the iteration. We proceed to repeat
the process. Thus, we compute the centroid of the best n vertices of the new
simplex and again reflect the point with the largest function / value in the
centroid obtained for the best n points of the new simplex.
If, however, fr < fs = fo, so that the point pr yields the smallest function value among the points of the simplex, we argue that this direction is a
good one. In this case we increase the distance traveled using an expansion
coefficient χ > 1 (e.g., χ = 2) to obtain
Pe=Pg+X
(Pr ~ Pg) ·
The operation above yields a new point on the line P\Pgpr extended beyond
pr. We illustrate this operation in Figure 14.2. If fe < fr now, the expansion
is declared a success and pe replaces pt in the next simplex. If, on the other
hand, fe > / r , the expansion is a failure and pr replaces pt.
276
GLOBAL SEARCH ALGORITHMS
Figure 14.2
Figure 14.3
Expansion operation with the expansion coefficient χ.
Outside contraction operation for the case when fr € [fnu fi)-
Finally, if fr > fni, the reflected point pr would constitute the point with
the largest function value in the new simplex. Then in the next step it would
be reflected in pg, probably an unfruitful operation. Instead, this case is dealt
with by a contraction operation in one of two ways. First, if fr > fni and
fr < fi, then we contract (pr — pg) with a contraction coefficient 0 < 7 < 1
(e.g., 7 = 1/2) to obtain
Pc = Pg + 7 {Pr ~ Pg) ·
We refer to this operation as the outside contraction. See Figure 14.3 for an
illustration of this operation. If, on the other hand, fr > fni and fr > //,
then Pi replaces pr in the contraction operation and we get
Pc = Pg + 7 (Pi - Pg) ·
This operation, referred to as the inside contraction, is illustrated in Figure 14.4.
If, in either case, fc < //, the contraction is considered a success, and we
replace pl with pc in the new simplex. If, however, fc > fi, the contraction
THE NELDER-MEAD SIMPLEX ALGORITHM
277
Pnl
p9
Ps
Figure 14.4
Inside contraction operation for the case when fr > fi.
Pnl
Pnl+Ps
2
P.
Figure 14.5
Shrinkage operation.
is a failure, and in this case a new simplex can be formed by retaining ps
only and halving the distance from ps to every other point in the simplex.
We can refer to this event as a shrinkage operation. The shrinkage operation
is illustrated in Figure 14.5. In general, the shrink step produces the n new
vertices of the new simplex according to the formula
Vi=p8+
σ(ρ{ - p8),
i = 1,2,..., n,
where σ = 1/2. Hence, the vertices of the new simplex are p s , vi,...,
vn.
When implementing the simplex algorithm, we need a tie-breaking rule to
order points in the case of equal function values. Lagarias et al. [82] propose
tie-breaking rules that assign to the new vertex the highest possible index
consistent with the relation
/(Po)</(Pi)< ···</(?„)·
In Figure 14.6 we illustrate the simplex search method by showing the first
few stages of the search for a minimizer of a function of two variables. Our
278
GLOBAL SEARCH ALGORITHMS
Figure 14.6
two variables.
The simplex search method applied to minimization of a function of
drawing is inspired by a figure in Layton [84, p. 225]. The starting simplex is
composed of the vertices A, B, and C. The vertices D and E are obtained by
the expansion operation. The vertex F is obtained by the reflection operation.
The vertex G is obtained using the outside contraction operation, while the
vertex I is obtained employing the inside contraction operation. For the sake
of clarity we terminate the process with the simplex composed of the vertices
23, 22", and I. The process may, of course, be continued beyond this simplex.
We add that a variant of the simplex method described above is presented
in Jang et al. [67], where they use the centroid of the entire simplex rather
than the centroid of the best n vertices of the simplex. That is, Jang et al. [67]
compute the point pg using the n + 1 vertices of the simplex. In addition, they
use only the inside contraction and they do not use the outside contraction
operation.
14.3
Simulated Annealing
Randomized Search
Simulated annealing is an instance of a randomized search method. A randomized search method, also sometimes called a probabilistic search method,
is an algorithm that searches the feasible set of an optimization problem by
SIMULATED ANNEALING
279
considering randomized samples of candidate points in the set. The simulated annealing algorithm was first suggested for optimization by Kirkpatrick
et al. [75] based on techniques of Metropolis et al. [91]. An early application
to image processing was described by Geman and Geman [48].
As usual, suppose that we wish to solve an optimization problem of the
form
minimize
subject to
f(x)
x G Ω.
The basic assumption in randomized search is our ability to select a random
sample from the feasible set Ω. Typically, we start a randomized search process by selecting a random initial point a;(0) G Ω. Then, we select a random
next-candidate point, usually close to χ(°\
More formally, we assume that for any x G Ω, there is a set N(x) C Ω
such that we can generate a random sample from this set. Typically, N(x) is
a set of points that are "close" to x, and for this reason we usually think of
N(x) as a "neighborhood" of x [we use the term neighborhood for N(x) even
in the general case where the points in it are arbitrary, not necessarily close
to x). When we speak of generating a random point in N(x), we mean that
there is a prespecified distribution over N(x), and we sample a point with
this distribution. Often, this distribution is chosen to be uniform over N(x);
other distributions are also used, including Gaussian and Cauchy.
Before discussing the simulated annealing method, we first consider a simple randomized search algorithm, which we will call naive random search.
Naive Random Search Algorithm
1. Set k := 0. Select an initial point x ^ G Ω.
2. Pick a candidate point z^
at random from
3. If /(*<*>) < /(x ( f c ) ), then set χ^+^
N(x^).
= z « ; else, set χ ^ + 1 ) = »(*>.
4. If stopping criterion satisfied, then stop.
5. Set k := k + 1, go to step 2.
Note that the algorithm above has the familiar form x ^ + 1 ^ = x^ + dik\
where Φ ' is randomly generated. By design, the direction <rh' either is 0
or is a descent direction. Typical stopping criteria include reaching a certain
number of iterations, or reaching a certain objective function value.
Simulated Annealing Algorithm
The main problem with the naive random search method is that it may get
stuck in a region around a local minimizer. This is easy to imagine; for
280
GLOBAL SEARCH ALGORITHMS
example, if x^ is a local minimizer and N(x^)
is sufficiently small that all
points in it have no smaller objective function value than x^ [i.e., x^ is a
global minimizer of / over N(x^)],
then clearly the algorithm will be stuck
and will never find a point outside of N(x^).
To prevent getting stuck in
a region around a local minimizer, we need a way to consider points outside
this region. One way to achieve this goal is to make sure that at each fc,
the neighborhood N(x^)
is a very large set. Indeed, if N(x^)
is sufficiently
large, then we are guaranteed that the algorithm will converge (in some sense)
to a global minimizer. An extreme example of this case is where N(x) = Ω
for any x G Ω (in this case running k iterations of the naive random search
algorithm amounts to finding the best point among k randomly chosen points
in Ω). However, having too large a neighborhood in the search algorithm
results in a slow search process, because the sampling of candidate points to
consider is spread out, making it more unlikely to find a better candidate
point.
Another way to overcome the problem of getting stuck in a region around a
local minimizer is to modify the naive search algorithm so that we can "climb
out" of such a region. This means that the algorithm may accept a new point
that is worse than the current point. The simulated annealing algorithm
incorporates such a mechanism.
Simulated Annealing Algorithm
1. Set k := 0; select an initial point x^
2. Pick a candidate point z^
G Ω.
at random from
N(x^).
3. Toss a coin with probability of HEAD equal to p(kJ(z^)J(x^)).
HEAD, then set aj(fc+1> = *<*>; else, set χ^+^ = x^k\
If
4. If the stopping criterion is satisfied, then stop.
5. Set k := k + 1, go to step 2.
In step 3, the use of a "coin toss" is simply descriptive for a randomized
decision—we do not mean literally that an actual coin needs to be tossed.
As in naive random search, the simulated annealing algorithm above has
the familiar form x ^ * 1 ) = x^ + dS \ where <r ' is randomly generated.
But in simulated annealing the direction (r * might be an ascent direction.
However, as the algorithm progresses, we can keep track of the best-so-far
point—this is a point x[,Jß+ which, at each fc, is equal to a x^\ j G { 0 , . . . , fc},
such that f(x^)
< f(x™) for alH G { 0 , . . . , k}. The best-so-far point can
be updated at each step k as follows:
la!bL1)
otherwise.
SIMULATED ANNEALING
281
By keeping track of the best-so-far point, we can treat the simulated annealing algorithm simply as a search procedure; the best-so-far point is what we
eventually use when the algorithm stops. This comment applies not only to
simulated annealing, but other search techniques as well (including the randomized algorithms presented in the next two sections).
The major difference between simulated annealing and naive random search
is that in step 5, there is some probability that we set the next iterate to be
equal to the random point selected from the neighborhood, even if that point
turns out to be worse than the current iterate. This probability is called the
acceptance probability. For the algorithm to work properly, the acceptance
probability must be chosen appropriately. A typical choice is
p(k, / ( « « ) , /(*<*>)) = min{l, exp(-(/(z< fe >) - /(*< fc >))/T fc )},
where exp is the exponential function and Tk represents a positive sequence
called the temperature schedule or cooling schedule. This form of acceptance
probability is usually credited to Boltzmann and leads to a simulated annealing algorithm that behaves as a Gibbs sampler (a method of probabilistic
sampling based on the Gibbs distribution).
Notice that if /(z<*>) < /(x ( / e ) ), then p(fc,/(* ( f c ) ),/(x ( f c ) )) = 1, which
means that we set χ^+^ = ζ^ (i.e., we move to the point z^). However, if
f(z^)
> f(x^),
there is still a positive probability of setting χ^+^ = ζ^;
this probability is equal to
exp
/(gW)-/(«W)\
Tk
) -
Note that the larger the difference between f(z^)
and /(x^),
the less likely
we are to move to the worse point z^h\ Similarly, the smaller the value of X^,
the less likely we are to move to z^k\ It is typical to let the "temperature" Tk
be monotonically decreasing to 0 (hence the word cooling). In other words, as
the iteration index k increases, the algorithm becomes increasingly reluctant
to move to a worse point. The intuitive reason for this behavior is that initially
we wish to actively explore the feasible set, but with time we would like to
be less active in exploration so that we spend more time in a region around
a global minimizer. In other words, the desired behavior is this: Initially, the
algorithm jumps around and is more likely to climb out of regions around
local minimizers, but with time it settles down and is more likely to spend
time around a global minimizer.
The term annealing comes from the field of metallurgy, where it refers to
a technique for improving the property of metals. The basic procedure is to
heat up a piece of metal and then cool it down in a controlled fashion. When
the metal is first heated, the atoms in it become unstuck from their initial
positions (with some level of internal energy). Then, as cooling takes place,
the atoms gradually configure themselves in states of lower internal energy.
Provided that the cooling is sufficiently slow, the final internal energy is lower
282
GLOBAL SEARCH ALGORITHMS
than the initial internal energy, thereby refining the crystalline structure and
reducing defects.
In an analogous way, the temperature in simulated annealing must be
cooled in a controlled fashion. In particular, the cooling should be sufficiently
slow. In a seminal paper, Hajek [56] provides a rigorous analysis of the cooling
schedule for convergence of the algorithm to a global minimizer. Specifically,
he shows that an appropriate cooling schedule is
k
log(fc + 2)'
where 7 > 0 is a problem-dependent constant (large enough to allow the
algorithm to "climb out" of regions around local minimizers that are not
global minimizers). See also [57] for an analysis of a generalized version of
simulated annealing.
Simulated annealing is often also used in combinatorial optimization, where
the feasible set is finite (but typically large). An example of such a problem
is the celebrated traveling salesperson problem. In the most basic form of
this problem, we are given a number of cities and the cost of traveling from
any city to any other city. The optimization problem is to find the cheapest
round-trip route, starting from a given city, that visits every other city exactly
once. For a description of how to apply simulated annealing to the traveling
salesperson problem, see [67, p. 183].
14.4
Particle Swarm Optimization
Particle swarm optimization (PSO) is a randomized search technique presented by James Kennedy (a social psychologist) and Russell C. Eberhart
(an engineer) in 1995 [73]. This optimization method is inspired by social
interaction principles. The PSO algorithm differs from the randomized search
methods discussed in Section 14.3 in one key way: Instead of updating a single candidate solution x^ at each iteration, we update a population (set) of
candidate solutions, called a swarm. Each candidate solution in the swarm is
called a particle. We think of a swarm as an apparently disorganized population of moving individuals that tend to cluster together while each individual
seems to be moving in a random direction. (This description was adapted
from a presentation by R. C. Eberhart.) The PSO algorithm aims to mimic
the social behavior of animals and insects, such as a swarm of bees, a flock of
birds, or a herd of wildebeest.
Suppose that we wish to minimize an objective function over Rn. In the
PSO algorithm, we start with an initial randomly generated population of
points in R n . Associated with each point in the population is a velocity
vector. We think of each point as the position of a particle, moving with an
associated velocity. We then evaluate the objective function at each point
in the population. Based on this evaluation, we create a new population of
PARTICLE SWARM OPTIMIZATION
283
points together with a new set of velocities. The creation of points in the
new population, and their velocities, involve certain operations on points and
velocities of the particles in the preceding population, described later.
Each particle keeps track of its best-so-far position—this is the best position
it has visited so far (with respect to the value of the objective function). We
will call this particle-dependent best-so-far position a personal best (pbest).
In contrast, the overall best-so-far position (best among all the positions
encountered so far by the entire population) is called a global best (gbest).
The particles "interact" with each other by updating their velocities according to their individual personal best as well as the global best. In the
gbest version of the PSO algorithm, presented below, the velocity of each particle is changed, at each time step, toward a combination of its pbest and the
gbest locations. The velocity is weighted by a random term, with separate
random numbers being generated for velocities toward pbest and gbest locations. Thus, the particles are drawn both to their own personal best positions
as well as to the best position of the entire swarm. As usual, typical stopping
criteria of the algorithm consist of reaching a certain number of iterations, or
reaching a certain objective function value.
Basic PSO Algorithm
We now present a simple version of the gbest version of the PSO algorithm,
where at each time step the velocity of each particle is changed toward its
pbest and the gbest locations. Let / : W1 —> R be the objective function that
we wish to minimize. Let d be the population size, and index the particles in
the swarm by i = 1 , . . . , d. Denote the position of particle i by Xi G R n and
its velocity by vi G W1. Let p{ be the pbest of particle i and g the gbest.
It is convenient to introduce the Hadamard product (or Schur product)
operator, denoted by o: If A and B are matrices with the same dimension,
then AoB is a matrix of the same dimension as A (or B) resulting from entryby-entry multiplication of A and B. This operation is denoted in MATLAB by
".*" (the dot before an operator indicates entry-by-entry operations). Thus,
if A and B have the same dimension, then A. *B returns a matrix whose entries
are simply the products of the corresponding individual entries of A and B.
The PSO gbest algorithm uses three given constant real parameters, ω, c\,
and C2, which we discuss after presenting the algorithm.
PSO Gbest Algorithm
1. Set k := 0. For i = 1 , . . . ,d, generate initial random positions x\ ' and
velocities v) \ and set p) — x) \ Set g^ = arg min f (o>
(o), f(x).
284
GLOBAL SEARCH ALGORITHMS
2. For i = 1 , . . . , d, generate random n-vectors r\
nents uniformly in the interval (0,1), and set
= ωνΡ +
v\W
Clrf
> o ( p « - x(*>) +
}
C2sf
and s\
)
with compo-
> o foW - x « ) ,
χ<* +1 >=χ<*>+„<* +1 >.
3. For t = 1
d, if /(*i f c + 1 ) ) < f{p\k)),
set p ^ 1 ' = „ < * > .
then set p<fe+1) = x< fe+1) ; else,
4. If there exists i G { l , . . . , d } such that / ( i c f + 1 ) ) < f(g(k)),
0(*+i) = χ (*+ι). e l s e ? ^ ^(fc+i) = ^(fc).
then set
5. If stopping criterion satisfied, then stop.
6. Set k := A: + 1, go to step 2.
In the algorithm, the parameter ω is referred to as an inertial constant.
Recommended values are slightly less than 1. The parameters c\ and C2 are
constants that determine how much the particle is directed toward "good" positions. They represent a "cognitive" and a "social" component, respectively,
in that they affect how much the particle's personal best and the global best
influence its movement. Recommended values are ci,C2 ~ 2.
Variations
The PSO techniques have evolved since 1995. For example, recently Clerc [29]
proposed a constriction-factor version of the algorithm, where the velocity is
updated as
„(*+!) =
K
(„<*> +
Cirf)
o (p(*> - x « ) + <*.<*> o fo« - χ(*>)) ,
where the constriction coefficient κ is computed as
2
\ΐ-φ-
y/φ2 - 4φ\'
where φ = c\ -f C2 and φ > 4. For example, for ^ = 4.1, we have ^ = 0.729.
The role of the constriction coefficient is to speed up the convergence.
When using PSO in practice, one might wish to clamp the velocities to a
certain maximum amount, say, vmayi. In other words, we replace each component v of the velocity vector by
min {^max, max{-v m a x , v}} .
For an up-to-date literature survey and other modifications and heuristics,
we recommend the first part of the proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms, held in April 2007 in
GENETIC ALGORITHMS
285
Warsaw, Poland [5]. In these proceedings, one can find a number of papers
dealing with applications of PSO to multiobjective optimization problems,
versions of PSO for constrained optimization problems, as well as "niching"
versions designed to find multiple solutions, that is, applications of PSO to
multimodal optimization problems. For a mathematical analysis of the PSO
algorithm, see Clerc and Kennedy [30].
14.5
Genetic Algorithms
Basic Description
A genetic algorithm is a randomized, population-based search technique that
has its roots in the principles of genetics. The beginnings of genetic algorithms
is credited to John Holland, who developed the basic ideas in the late 1960s
and early 1970s. Since its conception, genetic algorithms have been used
widely as a tool in computer programming and artificial intelligence (e.g.,
[61], [79], and [94]), optimization (e.g., [36], [67], and [127]), neural network
training (e.g., [80]), and many other areas.
Suppose that we wish to solve an optimization problem of the form
maximize
subject to
f(x)
x GΩ
(notice that the problem is a maximization, which is more convenient for
describing genetic algorithms). The underlying idea of genetic algorithms applied to this problem is as follows. We start with an initial set of points in Ω,
denoted P(0), called the initial population. We then evaluate the objective
function at points in P(0). Based on this evaluation, we create a new set of
points P ( l ) . The creation of P ( l ) involves certain operations on points in
P(0), called crossover and mutation, discussed later. We repeat the procedure iteratively, generating populations P(2), P ( 3 ) , . . . , until an appropriate
stopping criterion is reached. The purpose of the crossover and mutation
operations is to create a new population with an average objective function
value that is higher than that of the previous population. To summarize, the
genetic algorithm iteratively performs the operations of crossover and mutation on each population to produce a new population until a chosen stopping
criterion is met.
The terminology used in describing genetic algorithms is adopted from
genetics. To proceed with describing the details of the algorithm, we need the
additional ideas and terms described below.
Chromosomes and Representation Schemes First, we point out that, in fact,
genetic algorithms do not work directly with points in the set Ω, but rather,
with an encoding of the points in Ω. Specifically, we need first to map Ω onto
a set consisting of strings of symbols, all of equal length. These strings are
286
GLOBAL SEARCH ALGORITHMS
called chromosomes. Each chromosome consists of elements from a chosen set
of symbols, called the alphabet. For example, a common alphabet is the set
{0,1}, in which case the chromosomes are simply binary strings. We denote by
L the length of chromosomes (i.e., the number of symbols in the strings). To
each chromosome there corresponds a value of the objective function, referred
to as the fitness of the chromosome. For each chromosome x, we write f(x) for
its fitness. Note that, for convenience, we use / to denote both the original
objective function and the fitness measure on the set of chromosomes. We
assume that / is a nonnegative function.
The choice of chromosome length, alphabet, and encoding (i.e., the mapping from Ω onto the set of chromosomes) is called the representation scheme
for the problem. Identification of an appropriate representation scheme is the
first step in using genetic algorithms to solve a given optimization problem.
Once a suitable representation scheme has been chosen, the next phase is to
initialize the first population P(0) of chromosomes. This is usually done by a
random selection of a set of chromosomes. After we form the initial population
of chromosomes, we then apply the operations of crossover and mutation on
the population. During each iteration k of the process, we evaluate the fitness
f(x^)
of each member x^ of the population P(k). After the fitness of the
entire population has been evaluated, we form a new population P(k + 1) in
two stages.
Selection and Evolution In the first stage we apply an operation called selection, where we form a set M(k) with the same number of elements as P(k).
This number is called the population size, which we denote by N. The set
M(k), called the mating pool, is formed from P(k) using a random procedure
as follows: Each point m^ in M(k) is equal to x^ in P(k) with probability
/(*<*>)
F(k)
where
'
F(k) = j2Mk))
and the sum is taken over the whole of P(k). In other words, we select
chromosomes into the mating pool with probabilities proportional to their
fitness.
The selection scheme above is also called the roulette-wheel scheme, for the
following reason. Imagine a roulette wheel in which each slot is assigned to
a chromosome in P(k); some chromosomes may be assigned multiple slots.
The number of slots associated with each chromosome is in proportion to its
fitness. We then spin the roulette wheel and select [for inclusion in M(k)] the
chromosome on whose slot the ball comes to rest. This procedure is repeated
TV times, so that the mating pool M(k) contains TV chromosomes.
An alternative selection scheme is the tournament scheme, which proceeds
as follows. First, we select a pair of chromosomes at random from P(k). We
GENETIC ALGORITHMS
Parent chromosomes
287
Offspring chromosomes
Crossing site
Figure 14.7
Illustration of basic crossover operation.
then compare the fitness values of these two chromosomes, and place the fitter
of the two into M(k). We repeat this operation until the mating pool M(k)
contains N chromosomes.
The second stage is called evolution: in this stage, we apply the crossover
and mutation operations. The crossover operation takes a pair of chromosomes, called the parents, and gives a pair of offspring chromosomes. The
operation involves exchanging substrings of the two parent chromosomes, described below. Pairs of parents for crossover are chosen from the mating pool
randomly, such that the probability that a chromosome is chosen for crossover
is pc. We assume that whether or not a given chromosome is chosen is independent of whether or not any other chromosome is chosen for crossover.
We can pick parents for crossover in several ways. For example, we may
randomly choose two chromosomes from the mating pool as parents. In this
case if TV is the number of chromosomes in the mating pool, then pc = 2/N.
Similarly, if we randomly pick 2k chromosomes from the mating pool (where
k < ΑΓ/2), forming k pairs of parents, we have pc = 2k/N. In the two examples
above, the number of pairs of parents is fixed and the value of pc is dependent
on this number. Yet another way of choosing parents is as follows: Given a
value of p c , we pick a random number of pairs of parents such that the average
number of pairs is pcN/2.
Once the parents for crossover have been determined, we apply the
crossover operation to the parents. There are many types of possible crossover
operations. The simplest crossover operation is the one-point crossover. In
this operation, we first choose a number randomly between 1 and L — 1 according to a uniform distribution, where L is the length of chromosomes. We
refer to this number as the crossing site. Crossover then involves exchanging substrings of the parents to the left of the crossing site, as illustrated in
Figure 14.7 and in the following example.
Example 14.1 Suppose that we have chromosomes of length L = 6 over
the binary alphabet {0,1}. Consider the pair of parents 000000 and 111111.
Suppose that the crossing site is 4. Then, the crossover operation applied to
the parent chromosomes yields the two offspring 000011 and 111100.
I
We can also have crossover operations with multiple crossing sites, as illustrated in Figure 14.8 and in the following example.
288
GLOBAL SEARCH ALGORITHMS
Parent chromosomes
Offspring chromosomes
Crossing sites
Figure 14.8
Illustration of two-point crossover operation.
Example 14.2 Consider two chromosomes, 000000000 and 111111111, of
length L = 9. Suppose that we have two crossing sites, at 3 and 7. Then, the
crossover operation applied to the parent chromosomes above yields the two
offspring 000111100 and 111000011.
I
After the crossover operation, we replace the parents in the mating pool by
their offspring. The mating pool has therefore been modified but maintains
the same number of elements.
Next, we apply the mutation operation, which takes each chromosome from
the mating pool and randomly changes each symbol of the chromosome with
a given probability pm. In the case of the binary alphabet, this change corresponds to complementing the corresponding bits; that is, we replace each
bit with probability pm from 0 to 1, or vice versa. If the alphabet contains
more than two symbols, then the change involves randomly substituting the
symbol with another symbol from the alphabet. Typically, the value of pm is
very small (e.g., 0.01), so that only a few chromosomes will undergo a change
due to mutation, and of those that are affected, only a few of the symbols are
modified. Therefore, the mutation operation plays only a minor role in the
genetic algorithm relative to the crossover operation.
After applying the crossover and mutation operations to the mating pool
M(fc), we obtain the new population P(k + 1). We then repeat the procedure
of evaluation, selection, and evolution, iteratively. We summarize the genetic
algorithm as follows.
Genetic Algorithm
1. Set k := 0. Generate an initial population P(0).
2. Evaluate P(k).
3. If the stopping criterion is satisfied, then stop.
4. Select M(k) from P(fc).
5. Evolve M(k) to form P(k + 1).
6. Set k := k + 1, go to step 2.
GENETIC ALGORITHMS
k:=0
Form P(0)
Evaluate P(k)
Yes
-C STOP)
Fitness
Selection
M(k)
Evolution
Crossover
Mutation
P(k+1)
k:=k+1
Figure 14.9
Flowchart for the genetic algorithm.
289
290
GLOBAL SEARCH ALGORITHMS
A flowchart illustrating this algorithm is shown in Figure 14.9.
During execution of the genetic algorithm, we keep track of the best-sofar chromosome, that is, the chromosome with the highest fitness of all the
chromosomes evaluated. After each iteration, the best-so-far chromosome
serves as the candidate for the solution to the original problem. Indeed,
we may even copy the best-so-far chromosome into each new population, a
practice referred to as elitism. The elitist strategy may result in domination
of the population by "superchromosomes." However, practical experience
suggests that elitism often improves the performance of the algorithm.
The stopping criterion can be implemented in a number of ways. For
example, a simple stopping criterion is to stop after a prespecified number of
iterations. Another possible criterion is to stop when the fitness for the bestso-far chromosome does not change significantly from iteration to iteration.
The genetic algorithm differs from the algorithms discussed in previous
chapters in several respects. First, it does not use derivatives of the objective
function (like the other methods in this chapter). Second, it uses operations
that are random within each iteration (like the other randomized search methods). Third, it searches from a set of points rather than a single point at each
iteration (like the PSO algorithm). Fourth, it works with an encoding of the
feasible set rather with than the set itself.
We illustrate an application of the genetic algorithm to an optimization
problem in the following example.
Example 14.3 Consider the MATLAB "peaks" function / : R 2 -> R given
by
f{x, y) = 3(1 - xf e-*9-<*+1>a - 10 ( | - x3 - y") e ^ ^
- ^
^
~"
(see also [67, pp. 178-180] for an example involving the same function). We
wish to maximize / over the set Ω = {[x,?/]T £ R 2 : — 3 < x, y < 3}. A plot
of the objective function / over the feasible set Ω is shown in Figure 14.10.
Using the MATLAB function fminunc (from the Optimization Toolbox), we
found the optimal point to be [—0.0093,1.5814]T, with objective function
value 8.1062.
To apply the genetic algorithm to solve the optimization problem above, we
use a simple binary representation scheme with length L = 32, where the first
16 bits of each chromosome encode the x component, whereas the remaining
16 bits encode the y component. Recall that x and y take values in the interval
[—3,3]. We first map the interval [—3,3] onto the interval [0, 2 16 — 1], via a
simple translation and scaling. The integers in the interval [0,2 16 — 1] are
then expressed as binary 16-bit strings. This defines the encoding of each
component x and y. The chromosome is obtained by juxtaposing the two
8-bit strings. For example, the point [x,2/]T = [—1,3]T is encoded as (see
GENETIC ALGORITHMS
Figure 14.10
291
Plot of / for Example 14.3.
Exercise 14.4 for a simple algorithm for converting from decimal into binary)
v01010101010101011111111111111111.
N
v
'
encoded x = — 1
v
'
encoded y = 3
Using a population size of 20, we apply 50 iterations of the genetic algorithm on the problem above. We used parameter values of pc = 0.75 and
pm = 0.0075. Figure 14.11 shows plots of the best, average, and worst objective function values in the population for every iteration (generation) of
the algorithm. The best-so-far solution obtained at the end of the 50 iterations is [0.0615,1.5827]T, with objective function value 8.1013. Note that this
solution and objective function value are very close to those obtained using
MATLAB.
■
Analysis of Genetic Algorithms
In this section we use heuristic arguments to describe why genetic algorithms
work. As pointed out before, the genetic algorithm was motivated by ideas
from natural genetics [61]. Specifically, the notion of "survival of the fittest"
plays a central role. The mechanisms used in the genetic algorithm mimic this
principle. We start with a population of chromosomes, and selectively pick
the fittest ones for reproduction. From these selected chromosomes, we form
the new generation by combining information encoded in them. In this way,
292
GLOBAL SEARCH ALGORITHMS
10
8
o Best
- * - Average
-*- Worst
6
<D
,3
<0
> 4
c
g
t3
§ 2
LL
<D
1°
o
-2
-4
"0
10
20
30
Generations
40
50
Figure 14.11
The best, average, and worst objective function values in the
population for every iteration (generation) of the genetic algorithm in Example 14.3.
the goal is to ensure that the fittest members of the population survive and
their information content is preserved and combined to produce even better
offspring.
To further analyze the genetic algorithm in a more quantitative fashion,
we need to define a few terms. For convenience, we only consider chromosomes over the binary alphabet. We introduce the notion of a schema (plural:
schemata) as a set of chromosomes with certain common features. Specifically, a schema is a set of chromosomes that contain Is and 0s in particular
locations. We represent a schema using a string notation over an extended
alphabet {0,1, *}. For example, the notation 1 * 01 represents the schema
1*01 = {1001,1101},
and the notation 0 * 101* represents the schema
0 * 101* = {001010,001011,011010,011011}.
In the schema notation, the numbers 0 and 1 denote the fixed binary values in
the chromosomes that belong to the schema. The symbol *, meaning "don't
care," matches either 0 or 1 at the positions it occupies. Thus, a schema
describes a set of chromosomes that have certain specified similarities. A
chromosome belongs to a particular schema if for all positions j = 1 , . . . , L
the symbol found in the jth position of the chromosome matches the symbol
found in the jth position of the schema, with the understanding that any
symbol matches *. Note that if a schema has r "don't care" symbols, then it
GENETIC ALGORITHMS
293
contains 2 r chromosomes. Moreover, any chromosome of length L belongs to
2 L schemata.
Given a schema that represents good solutions to our optimization problem,
we would like the number of matching chromosomes in the population P(k)
to grow as k increases. This growth is affected by several factors, which we
analyze in the following discussion. We assume throughout that we are using
the roulette-wheel selection method.
The first key idea in explaining why the genetic algorithm works is the observation that if a schema has chromosomes with better-than-average fitness,
then the expected (mean) number of chromosomes matching this schema in
the mating pool M(k) is larger than the number of chromosomes matching this
schema in the population P(k). To quantify this assertion, we need some additional notation. Let H be a given schema, and let e(if, k) be the number of
chromosomes in P(k) that match H; that is, e(H, k) is the number of elements
in the set P(k) Π H. Let f(H, k) be the average fitness of chromosomes in
P(k) that match schema H. This means that if HnP(k) — {x\,...,
xe(H,k)},
then
x
+ "' + f(xe(H,k))
f(rr M _ f( i)
t[H k)
'
~
eJWJ)
*
Let N be the number of chromosomes in the population and F(k) be the sum
of the fitness values of chromosomes in P(/c), as before. Denote by F(k) the
average fitness of chromosomes in the population; that is,
Finally, let m(üi, k) be the number of chromosomes in M(k) that match H,
in other words, the number of elements in the set M(k) Π H.
Lemma 14.1 Let H be a given schema and M(H,k)
of m(H,k) given P(k). Then,
M{H,k) =
be the expected value
if^e{H,k).
D
Proof. Let P(k) Π H = { x i , . . . ,xe(H,k)}- I n the remainder of the proof,
the term expected should be taken to mean "expected, given P(fc)." For each
element m^ G M(k) and each i = 1 , . . . , e(H, k), the probability that m^ =
Xi is given by f(xi)/F(k).
Thus, the expected number of chromosomes in
M(k) equal to X{ is
r/(«<)
N
F(k)
_
/ ( « * )
F(k)
294
GLOBAL SEARCH ALGORITHMS
Hence, the expected number of chromosomes in P(k) Π H that are selected
into M(k) is
^
F(k)~[
>
e(H,k)
F(k)~
F(k)
(
h
Because any chromosome in M{k) is also a chromosome in P(fc), the chromosomes in M(k) Π H are simply those in P(k) Π H that are selected into M(k).
Hence,
M(H,k) =
l^-e(H,k).
Lemma 14.1 quantifies our assertion that if a schema H has chromosomes
with better than average fitness [i.e., f(H,k)/F(k)
> 1], then the expected
number of chromosomes matching H in the mating pool M(k) is larger than
the number of chromosomes matching H in the population P(h).
We now analyze the effect of the evolution operations on the chromosomes
in the mating pool. For this, we need to introduce two parameters that are
useful in the characterization of a schema: order and length. The order o(S) of
a schema S is the number of fixed symbols (non* symbols) in its representation
(the notation o(S) is standard in the literature on genetic algorithms, and
should not be confused with the "little-oh" symbol defined in Section 5.6). If
the length of chromosomes in S is L, then o(S) is L minus the number of *
symbols in 5. For example,
o(l*01) = 4 - 1 - 3 ,
whereas
o(0* 1*01) = 6 - 2 = 4.
The length l(S) of a schema S is the distance between the first and last
fixed symbols (i.e., the difference between the positions of the rightmost fixed
symbol and the leftmost fixed symbol). For example,
Z(l*01)
3*101*)
/(* * 1*)
== 4 -- 1 : = 3,
== 5 -- 1 := 4,
:- 0 .
Note that for a schema S with chromosomes of length L, the order o(S) is a
number between 0 and L and the length l(S) is a number between 0 in L — 1.
The order of a schema with all * symbols is 0; its length is also 0. The order
of a schema containing only a single element (i.e., its representation has no *
symbols) is L [e.g., o(1011) = 4 — 0 = 4]. The length of a schema with fixed
symbols in its first and last positions is L — 1 [e.g., Z ( 0 * * l ) = 4 — 1 = 3].
GENETIC ALGORITHMS
295
We first consider the effect of the crossover operation on the mating pool.
The basic observation in the following lemma is that given a chromosome in
M(k)C\H, the probability that it leaves H after crossover is bounded above
by a quantity that is proportional to pc and 1(H).
Lemma 14.2 Given a chromosome in M(k) Π H, the probability that it is
chosen for crossover and neither of its offspring is in H is bounded above by
Pi
L-l
D
Proof Consider a given chromosome in M(k) Π H. The probability that it is
chosen for crossover is pc. If neither of its offspring is in H, then the crossover
point must be between the corresponding first and last fixed symbols of H.
The probability of this is 1(H)/(L — 1). Hence, the probability that the given
chromosome is chosen for crossover and neither of its offspring is in H is
bounded above by
Prom Lemma 14.2 we conclude that given a chromosome in M(k) Π H, the
probability either that it is not selected for crossover or that at least one of
its offspring is in H after the crossover operation, is bounded below by
Note that if a chromosome in H is chosen for crossover and the other parent
chromosome is also in i i , then both offspring are automatically in H (see
Exercise 14.5). Hence, for each chromosome in M(k) Π if, there is a certain
probability that it will result in an associated chromosome in H (either itself
or one of its offspring) after going through crossover (including selection for
crossover) and that probability is bounded below by the foregoing expression.
We next consider the effect of the mutation operation on the mating pool
M(k).
Lemma 14.3 Given a chromosome in M(k) Π H, the probability that it remains in H after the mutation operation is given by
D
Proof Given a chromosome in M(k) Π Ü", it remains in H after the mutation
operation if and only if none of the symbols in this chromosome that correspond to fixed symbols in H are changed by the mutation operation. The
probability of this event is (1 - pm)°^H\
I
296
GLOBAL SEARCH ALGORITHMS
Note that if p m is small, the expression (1— Pm)°^
equal to
l-pmo(ff).
above is approximately
The following theorem combines the results of the preceding lemmas.
Theorem 14.1 Let H be a given schema and S(H, k + 1) be the expected
value of e(H, k + 1) given P{k). Then,
8{H, k + 1) > ( l -
PcW^
(1 - Pm)°wf-^e(H,
k).
D
Proof. Consider a given chromosome in M(k) Π H. If, after the evolution
operations, it has a resulting chromosome that is in if, then that chromosome
is in P(k + 1) Π H. By Lemmas 14.2 and 14.3, the probability of this event is
bounded below by
{l~Pc^){l-PmY^.
Therefore, because each chromosome in M(k) Π H results in a chromosome in
P(k + 1)Γ\Η with a probability bounded below by the expression above, the
expected value of e(H, k + 1) given M(k) is bounded below by
(l-PcJ~)(l-Prn)°iH)rn(H,k).
Taking the expectation given P(fc), we get
S(H,k + l)>(l-Pcj^j)
(1
-pm)o{H)M(H,k).
Finally, using Lemma 14.1, we arrive at the desired result.
I
Theorem 14.1 indicates how the number of chromosomes in a given schema
changes from one population to the next. Three factors influence this change,
reflected by the three terms on the right-hand side of inequality in Theorem 14.1: l-pJ(H)/(L-l),
(1-pm)°(H\
and f(H,k)/F(k).
Note that the
larger the values of these terms, the higher the expected number of matches of
the schema H in the next population. The effect of each term is summarized
as follows:
■ The term /(if, k)/F(k) reflects the role of average fitness of the given
schema H—the higher the average fitness, the higher the expected number of matches in the next population.
■ The term 1 —pcl(H)/(L — 1) reflects the effect of crossover—the smaller
the term pcl(H)/(L — 1), the higher the expected number of matches in
the next population.
GENETIC ALGORITHMS
297
■ The term ( 1 — p m ) ° ^ reflects the effect of mutation—the larger the term,
the higher the expected number of matches in the next population.
In summary, we see that a schema that is short, low order, and has aboveaverage fitness will have on average an increasing number of its representatives
in the population from iteration to iteration. Observe that the encoding is
relevant to the performance of the algorithm. Specifically, a good encoding is
one that results in high-fitness schemata having small lengths and orders.
Real-Number Genetic Algorithms
The genetic algorithms described thus far operate on binary strings, representing elements of the feasible set Ω. (For this reason, genetic algorithms are also
suitably applied to combinatorial optimization problems, where Ω is not M.n
but some discrete set.) Binary encodings allow us to use the schema theory,
described in the preceding section, to analyze genetic algorithms. However,
there are some disadvantages to operating on binary strings. To see this,
let g : {0,1} L —> Ω represent the binary "decoding" function; that is, if x
is a binary chromosome, g(x) G Ω is the point in the feasible set Ω C Mn
whose encoding is x. Therefore, the objective function being maximized by
the genetic algorithm is not / itself but rather the composition of / and the
decoding function g. In other words, the optimization problem being solved
by the genetic algorithm is
maximize
subject to
f(g(x))
xe{y
e {0,1} L : g{y) G Ω}.
This optimization problem may be more complex than the original optimization problem. For example, it may have extra maximizers, making the search
for a global maximizer more difficult.
The above motivates a consideration of genetic algorithms that operate
directly on the original optimization problem. In other words, we wish to
implement a genetic algorithm that operates directly on W1. The steps of
this algorithm will be the same as before (see Figure 14.9), except that the
elements of the population are points in the feasible set Ω rather than binary
strings. We will need to define appropriate crossover and mutation operations
for this case.
For crossover, we have several options. The simplest is to use averaging:
For a pair of parents x and y, the offspring is z = (x + y)/2 (this type of
crossover operation is used, e.g., in [103]). This offspring can then replace
one of the parents. Alternatively, we may produce two offspring as follows:
Z\ = (x + y)/2 + w\ and z2 — (x + y)/2 + ttf2, where W\ and w^ are two
randomly generated vectors (with zero mean). If either offspring lies outside
Ω, we have to bring the offspring back into Ω, using, for example, a projection
(see Section 23.2). A third option for crossover is to take random convex
combinations of the parents. Specifically, given a pair of parents x and y,
298
GLOBAL SEARCH ALGORITHMS
we generate a random number a G (0,1) and then produce two offspring
z\ — ax + (1 — a)y and Z2 = (1 — a)x + ay. This method of crossover
ensures that the offspring are always in the feasible set, provided that the
feasible set is convex. A fourth option is to perturb the two points above by
some random amount: Z\ = ax + {\ — a)y + W\ and z<i = (1 — a)x + ay + W2,
where W\ and W2 are two randomly generated vectors (with zero mean). In
this case we have to check for feasibility of the offspring and use projections
if needed.
For mutation, a simple implementation is to add a random vector to the
chromosome. Specifically, given a chromosome x, we produce its mutation
as x' = x + tu, where w is a random vector with zero mean. This mutation
operation is also called a real number creep (see, e.g., [103]). As before, we
have to ensure that the mutated chromosome is feasible. If not, we may use
a projection. An alternative method for mutation is to replace the chosen
chromosome with a random convex combination of the chromosome with a
random point in the feasible set; that is, we generate a random number a G
(0,1) and a random point w G Ω, and set x' = ax + (1 — a)w. Provided that
the feasible set is convex, the mutated chromosom will always be feasible.
Example 14.4 Consider again the function / : R 2 —► R from Example 14.3.
We apply a real-number genetic algorithm to find a maximizer of / using a
crossover operation of the fourth type described above and a mutation operation of the second type above. With a population size of 20, we apply 50
iterations of the genetic algorithm. As before, we used parameter values of
pc = 0.75 and pm = 0.0075. Figure 14.12 shows plots of the best, average, and
worst objective function values in the population for every iteration (generation) of the algorithm. The best-so-far solution obtained at the end of the 50
iterations is [—0.0096,1.5845]T, with objective function value 8.1061, which
is close to the result of Example 14.3.
I
EXERCISES
14.1 Write a MATLAB program to implement the Nelder-Mead algorithm
applied to minimizing the function
f(xi,X2)
= {X2 - ^ l ) 4 + 12X1^2 - Xl + X 2 - 3
on Ω = {x G R 2 : ^1,^2 £ [— 1,1]}- Locate the iteration points on the
level sets of / . Connect the successive points with lines to show clearly the
progression of the optimization process. Test your program with two starting
points:
χ(0)=
0·55
0.7
and
*«»=
h0·9
-0.5
EXERCISES
i
I '
Γ
_ 6
I o Best
- * - Average
1 -*- Worst 1
_δΙ
'
0
10
'
>
20
30
Generations
'
40
299
J
1
1
50
Figure 14.12
The best, average, and worst objective function values in the
population for every iteration (generation) of the real-number genetic algorithm in
Example 14.4.
14.2 Write MATLAB programs to implement naive random search and simulated annealing. Use the neighborhood
N(x{k))
= {x : x[k) -a<Xi<
xf] + a } ,
where a > 0 is prespecified, and pick z^ to be uniformly distributed on
N(xW). Test both algorithms on maximizing the MATLAB "peaks" function
given in Example 14.3. Observe the effect of varying a.
14.3 Write a MATLAB program to implement a particle swarm optimization
algorithm. Test your implementation on maximizing the MATLAB "peaks"
function given in Example 14.3.
14.4 This problem has four parts and is related to binary encoding for genetic
algorithms.
a. Let (7)io be the decimal representation for a given integer, and let
a m a m _ i · · -ao be its binary representation; that is, each ai is either 0
or 1, and
(I)io - a m 2 m + a m _ i 2 m " 1 + · · · + a ^ 1 + a 0 2°.
Verify that the following is true:
GOio = (((· · · (((a m 2 + a m _!)2 + a m _ 2 )2 + a m _ 3 ) · · · )2 + ai)2 + a 0 ).
300
GLOBAL SEARCH ALGORITHMS
b . The second expression in part a suggests a simple algorithm for converting
from decimal representation to equivalent binary representation, as follows. Dividing both sides of the expression in part a by 2, the remainder
is CLQ. Subsequent divisions by 2 yield the remaining bits α,χ, α<ι,..., am
as remainders.
Use this algorithm to find the binary representation of the integer (i)io =
1995.
c. Let (-F)io be the decimal representation for a given number in [0,1], and
let 0.α_ια_2 · · · be its binary representation, that is,
(F)io = α _ ι 2 _ 1 + α_ 2 2" 2 + · · · .
If this expression is multiplied by 2, the integer part of the product is
α_ι. Subsequent multiplications yield the remaining bits a_2, a~3,
As in part b, the above gives a simple algorithm for converting from a
decimal fraction to its binary representations. Use this algorithm to find
the binary representation of (i^io = 0.7265625.
Note that we can combine the algorithms from parts b and c to convert
an arbitrary positive decimal representation into its equivalent binary
representation. Specifically, we apply the algorithms in parts b and c
separately to the integer and fraction parts of the given decimal number,
respectively.
d. The procedure in part c may yield an infinitely long binary representation.
If this is the case, then we need to determine the number of bits required
to keep at least the same accuracy as the given decimal number. If we
have a d-digit decimal fraction, then the number of bits b in the binary
representation must satisfy 2~b < 10~ d , which yields b > 3.32d Convert
19.95 to its equivalent binary representation with at least the same degree
of accuracy (i.e., to two decimal places).
14.5 Given two chromosomes in a schema H, suppose that we swap some
(or all) of the symbols between them at corresponding positions. Show that
the resulting two chromosomes are also in H. Prom this fact we conclude that
given two chromosomes in H, both offspring after the crossover operation are
also in H. In other words, the crossover operation preserves membership in
H.
14.6 Consider a two-point crossover scheme (see Example 14.2), described
as follows. Given a pair of binary chromosomes of length L, we independently
choose two random numbers, uniform over 1,...,L — 1. We call the two
numbers C\ and C2, where c\ < C2. If C\ — C2, we do not swap any symbols
(i.e., leave the two given parent chromosomes unchanged). If c\ < C2, we
interchange the (c\ + l)th through C2th bits in the given parent chromosomes.
EXERCISES
301
Prove the analog of Lemma 14.2 for this case, given below.
Lemma: Given a chromosome in M(k) Π H, the probability that it is chosen
for crossover and neither of its offspring is in H is bounded above by
D
Hint: Note that the two-point crossover operation is equivalent to a composition of two one-point crossover operations (i.e., doing two one-point crossover
operations in succession).
14.7 State and prove the analog of Lemma 14.2 for an n-point crossover
operation.
Hint: See Exercise 14.6.
14.8 Implement the roulette-wheel selection scheme using MATLAB.
Hint: Use the MATLAB functions sum, cumsum, and find.
14.9 Implement the crossover operation (one-point) using the MATLAB,
assuming that we are given two binary parent chromosomes.
14.10 Implement the mutation operation using the MATLAB function xor,
assuming that the chromosomes in the mating pool are binary vectors.
14.11 Write a MATLAB program to implement a genetic algorithm using
binary encoding. Test your implementation on the following functions:
a. f(x) = -15sin 2 (2x) - (x - 2) 2 + 160, |x| < 10.
b . /( a ? ,y) = 3 ( l - a ; ) 2 e - a 5 a - ^ + 1 ) a - 1 0 ( f - a : 3 - 2 / 5 )
W> M < 3 (considered in Example 14.3).
e " *
2
^
2
- ^ ^ ,
14.12 Write a MATLAB program to implement a real-number genetic algorithm. Test your implementation on the function f(x) = xisin(xi) +
X2 sin(5^2) with the constraint set Ω = {x : 0 < x\ < 10,4 < x2 < 6}.
PART III
LINEAR PROGRAMMING
CHAPTER 15
INTRODUCTION TO LINEAR
PROGRAMMING
15.1
Brief History of Linear Programming
The goal of linear programming is to determine the values of decision variables
that maximize or minimize a linear objective function, where the decision
variables are subject to linear constraints. A linear programming problem is
a special case of a general constrained optimization problem. In the general
setting, the goal is to find a point that minimizes the objective function and
at the same time satisfies the constraints. We refer to any point that satisfies
the constraints as a feasible point In a linear programming problem, the
objective function is linear, and the set of feasible points is determined by a
set of linear equations and/or inequalities.
In this part we study methods for solving linear programming problems.
Linear programming methods provide a way of choosing the best feasible point
among the many possible feasible points. In general, the number of feasible
points is infinitely large. However, as we shall see, the solution to a linear
programming problem can be found by searching through a particular finite
number of feasible points, known as basic feasible solutions. Therefore, in
principle, we can solve a linear programming problem simply by comparing
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
305
306
INTRODUCTION TO LINEAR PROGRAMMING
the finite number of basic feasible solutions and finding one that minimizes
or maximizes the objective function—we refer to this approach as the bruteforce approach. For most practical decision problems, even this finite number
of basic feasible solutions is so large that the method of choosing the best
solution by comparing them to each other is impractical. To get a feel for
the amount of computation needed in a brute-force approach, consider the
following example. Suppose that we have a small factory with 20 different
machines producing 20 different parts. Assume that any of the machines
can produce any part. We also assume that the time for producing each
part on each machine is known. The problem then is to assign a part to
each machine so that the overall production time is minimized. We see that
there are 20! (20 factorial) possible assignments. The brute-force approach to
solving this assignment problem would involve writing down all the possible
assignments and then choosing the best one by comparing them. Suppose
that we have at our disposal a computer that takes 1 μβ ( 1 0 - 6 second) to
determine each assignment. Then, to find the best (optimal) assignment this
computer would need 77,147 years (working 24 hours a day, 365 days a year)
to find the best solution. An alternative approach to solving this problem
is to use experienced planners to optimize this assignment problem. Such
an approach relies on heuristics. Heuristics come close, but give suboptimal
answers. Heuristics that do reasonably well, with an error of, say, 10%, may
still not be good enough. For example, in a business that operates on large
volumes and a small profit margin, a 10% error could mean the difference
between loss and profit.
Efficient methods for solving linear programming problems became available in the late 1930s. In 1939, Kantorovich presented a number of solutions
to some problems related to production and transportation planning. During
World War II, Koopmans contributed significantly to the solution of transportation problems. Kantorovich and Koopmans were awarded a Nobel Prize
in Economics in 1975 for their work on the theory of optimal allocation of resources. In 1947, Dantzig developed a new method for solving linear programs,
known today as the simplex method (see [34] for Dantzig's own treatment of
the algorithm). In the following chapters we discuss the simplex method in
detail. The simplex method is efficient and elegant and has been declared
one of the 10 algorithms with the greatest influence on the development and
practice of science and engineering in the twentieth century [40].
The simplex method has the undesirable property that in the worst case,
the number of steps (and hence total time) required to find a solution grows
exponentially with the number of variables. Thus, the simplex method is said
to have exponential worst-case complexity. This led to an interest in devising
algorithms for solving linear programs that have polynomial complexity—
algorithms that find a solution in an amount of time that is bounded by a
polynomial in the number of variables. Khachiyan, in 1979, was the first to devise such an algorithm. However, his algorithm gained more theoretical than
practical interest. Then, in 1984, Karmarkar proposed a new linear program-
SIMPLE EXAMPLES OF LINEAR PROGRAMS
307
ming algorithm that has polynomial complexity and appears to solve some
complicated real-world problems of scheduling, routing, and planning more efficiently than the simplex method. Karmarkar's work led to the development
of many other nonsimplex methods commonly referred to as interior-point
methods. This approach is currently still an active research area. For more
details on Karmarkar's and related algorithms, see [42], [55], [71], [119], and
[124]. Some basic ideas illustrating Khachiyan's and Karmarkar's algorithms
are presented in Chapter 18.
15.2
Simple Examples of Linear Programs
Formally, a linear program is an optimization problem of the form
minimize
subject to
cTx
Ax = b
x>0,
where c G Mn, b G M m , and A G R m x n . The vector inequality x > 0 means
that each component of x is nonnegative. Several variations of this problem
are possible; for example, instead of minimizing, we can maximize, or the
constraints may be in the form of inequalities, such as Ax > b or Ax < b.
We also refer to these variations as linear programs. In fact, as we shall
see later, these variations can all be rewritten into the standard form shown
above.
The purpose of this section is to give some simple examples of linear programming problems illustrating the importance and the various applications
of linear programming methods.
Example 15.1 This example is adapted from [123]. A manufacturer produces four different products: Χι, Χ2ι Χ3, and X4. There are three inputs to
this production process: labor in person-weeks, kilograms of raw material A,
and boxes of raw material B. Each product has different input requirements.
In determining each week's production schedule, the manufacturer cannot use
more than the available amounts of labor and the two raw materials. The
relevant information is presented in Table 15.1. Every production decision
must satisfy the restrictions on the availability of inputs. These constraints
can be written using the data in Table 15.1. In particular, we have
xi + 2x 2 + x 3 + 2x 4 < 20
6x1 + 5x 2 + 3x 3 + 2x 4 < 100
3xi + 4x 2 + 9x 3 + 12x4 < 75.
Because negative production levels are not meaningful, we must impose the
following nonnegativity constraints on the production levels:
Xi>0,
z = l,2,3,4.
308
INTRODUCTION TO LINEAR PROGRAMMING
Table 15.1
Data for Example 15.1
Product
Inputs
Person-weeks
Kilograms of material A
Boxes of material B
Production levels
XA
2
5
4
Xs
1
3
9
X2
X3
X4
Xl
X2
1
6
3
xi
2
2
12
Input
Availabilities
20
100
75
Now, suppose that one unit of product X\ sells for $6, and X2l X3, and Χ4 sell
for $4, $7, and $5, respectively. Then, the total revenue for any production
decision (#i,£2>£3>#4) is
/(xi,X 2 ,^3,^4) = 6X1 + 4:X2 + 7xs + 5^4.
The problem is then to maximize / subject to the given constraints (the three
inequalities and four nonnegativity constraints). Using vector notation with
X=
[X1,X 2 ,^3,^4] T ,
the problem can be written in the compact form
maximize
subject to
ex
Ax < b
x>0,
where
cT = [6,4,7,5],
1 2 1 2"
6 5 3 2 ,
3 4 9 12
"20]
b = 100
75 J
Another example that illustrates linear programming involves determining
the most economical diet that satisfies the basic minimum requirements for
good health.
Example 15.2 Diet Problem. This example is adapted from [88]. Assume
that n different food types are available. The jth food sells at a price Cj per
unit. In addition, there are m basic nutrients. To achieve a balanced diet, you
must receive at least bi units of the ith nutrient per day. Assume that each
SIMPLE EXAMPLES OF LINEAR PROGRAMS
309
unit of food j contains α^ units of the ith nutrient. Denote by Xj the number
of units of food j in the diet. The objective is to select the Xj to minimize
the total cost of the diet:
minimize
C\X\ + C2X2 + · · · + cnxn
subject to the nutritional constraints
anxi + (I12X2 H
l·
a\nxn
> 61
«21^1 + «22^2 + · · * + 0,2nXn > &2
dmlXl
+ «m2^2 H
l·
a m n X n > &m,
and the nonnegativity constraints
x\ > 0, #2 > 0, . . . , xn > 0.
In the more compact vector notation, this problem becomes
minimize cTx
subject to Ax > b
x>0,
where x = [χι,#2, · · · 5 #n] T is an n-dimensional column vector, c T is an ndimensional row vector, A is an m x n matrix, and b is an m-dimensional
column vector. We call this problem the diet problem and will return to it in
Chapter 17.
I
In the next example we consider a linear programming problem that arises
in manufacturing.
Example 15.3 A manufacturer produces two different products, X\ and X2,
using three machines: Mi, M2, and M3. Each machine can be used for only
a limited amount of time. Production times of each product on each machine
are given in Table 15.2. The objective is to maximize the combined time of
utilization of all three machines.
Every production decision must satisfy the constraints on the available
time. These restrictions can be written down using data from Table 15.2. In
particular, we have
xi + ^2 < 3,
x\ + 3^2 < 18,
2xi + Χ2 < 14,
where x\ and X2 denote the production levels. The combined production time
of all three machines is
f(xux2)
=4χι +5χ2·
310
INTRODUCTION TO LINEAR PROGRAMMING
Table 15.2
Data for Example 15.3
Production time (hours/unit)
Machine
Mi
M2
M3
Total
X2
Xl
1
1
2
4
1
3
1
5
Available time
(hours)
8
18
14
Thus, writing x = [χι,α^] 1 ", the problem in compact notation has the form
maximize
subject to
cTx
Ax < b
x > 0,
where
c T = [4,5],
Γι f
1
[2
3 ,
1
_
6 =
8~
18
14
■
In the following example we discuss an application of linear programming
in transportation.
Example 15.4 A manufacturing company has plants in cities A, B, and C.
The company produces and distributes its product to dealers in various cities.
On a particular day, the company has 30 units of its product in A, 40 in B,
and 30 in C. The company plans to ship 20 units to D, 20 to E, 25 to F,
and 35 to G, following orders received from dealers. The transportation costs
per unit of each product between the cities are given in Table 15.3. In the
table, the quantities supplied and demanded appear at the right and along
the bottom of the table. The quantities to be transported from the plants to
different destinations are represented by the decision variables.
SIMPLE EXAMPLES OF LINEAR PROGRAMS
Table 15.3
To
From
A
B
C
Demand
Data for Example 15.4
D
$7
$7
$5
20
311
$10
$11
$8
20
$14
$12
$15
25
$8
$6
$9
35
Supply
30
40
30
100
This problem can be stated in the form
minimize
subject to
lx\\ + 10a:i2 + 14xi3 + 8x14 + lx2\ + H#22 + 12#23
+ 6x24 + 5x 3 i + 8x32 + 15x 33 + 9x 34
x\\ + x\2 + £13 + £14 = 30
#21 + #22 + #23 + #24
#31 + #32 + #33 + #34
#11 +#21 +#31
#12 + #22 + #32
#13 + #23 + #33
#14 + #24 + #34
#11,#12,·· · ,#34
=
=
=
=
=
=
>
40
30
20
20
25
35
0.
In this problem one of the constraint equations is redundant because it can
be derived from the rest of the constraint equations. The mathematical formulation of the transportation problem is then in a linear programming form
with twelve (3x4) decision variables and six (3 + 4 — 1) linearly independent
constraint equations. Obviously, we also require nonnegativity of the decision
variables, since a negative shipment is impossible and does not have a valid
interpretation.
I
Next, we give an example of a linear programming problem arising in electrical engineering.
Example 15.5 This example is adapted from [100]. Figure 15.1 shows an
electric circuit that is designed to use a 30-V source to charge 10-V, 6-V, and
20-V batteries connected in parallel. Physical constraints limit the currents
i i , I2,13, ^4, and I5 to a maximum of 4 A, 3 A, 3 A, 2 A, and 2 A, respectively.
In addition, the batteries must not be discharged; that is, the currents 7χ, Ι2,
I3, J4, and 75 must not be negative. We wish to find the values of the currents
/ 1 , . . . , ^5 such that the total power transferred to the batteries is maximized.
The total power transferred to the batteries is the sum of the powers transferred to each battery and is given by IO/2 + 6/4 + 2Ο/5 W. Prom the circuit in
312
INTRODUCTION TO LINEAR PROGRAMMING
Volts
Figure 15.1
Battery charger circuit for Example 15.5.
Figure 15.1, we observe that the currents satisfy the constraints I\ = I2 + ^3
and J3 = I4 -f I5. Therefore, the problem can be posed as the following linear
program:
maximize
subject to
IO/2 + 6/4 + 2Ο/5
I\ = I2 + h
h =h +h
h <4
/2<3
/3<3
h <2
/i,/2,/3,/4,/5>0.
Finally, we present an example from wireless communications.
Example 15.6 Consider the wireless communication system shown in Figure 15.2. There are n "mobile" users. For each i = 1 , . . . ,n, user i transmits
a signal to the base station with power pi and an attenuation factor of hi
(i.e., the actual signal power received at the base station from user i is hiPi).
When the base station is receiving from user i, the total power received from
all other users is considered interference (i.e., the interference for user i is
Σΐ^ΐ hjPj)· For the communication with user i to be reliable, the signal-tointerference ratio must exceed a threshold 7^, where the "signal" is the power
received from user i.
We are interested in minimizing the total power transmitted by all users
subject to having reliable communications for all users. We can formulate the
SIMPLE EXAMPLES OF LINEAR PROGRAMS
313
Base
Station
r
User 1
User 2
F i g u r e 15.2
User 3
Wireless communication system in Example 15.6.
problem as a linear programming problem of the form
cTx
minimize
subject to
Ax
> b
x>0.
We proceed as follows. T h e total power transmitted is p i + · ■
signal-to-interference ratio for user i is
hiPi
Hence, t h e problem can be written as
minimize
subject to
pi +
\-pn
hiPi
>7t, i = l,...,n
p i , . . . , p n > 0.
We can write the above as the linear programming problem
minimize
subject to
pi + · · · + p n
hiPi — 7^ Y ^ hjPj > 0, i = 1 , . . . , n
ύφί
Ρ ΐ , · . . , Ρ η > 0.
+ pn. The
314
INTRODUCTION TO LINEAR PROGRAMMING
In matrix form, we have
c=[l,...,l]T
A
hi
I -72Λ1
-7i^2
h2
-l\hn
"72 K
-Inhl
b = 0.
~7n^2
hn
For more examples of linear programming and their applications in a variety
of engineering problems, we refer the reader to [1], [34], [35], [46], and [109].
For applications of linear programming to the design of control systems, see
[33]. Linear programming also provides the basis for theoretical applications,
as, for example, in matrix game theory (discussed in [18]).
15.3
Two-Dimensional Linear Programs
Many fundamental concepts of linear programming are easily illustrated in
two-dimensional space. Therefore, we consider linear problems in R 2 before
discussing general linear programming problems.
Consider the following linear program (adapted from [123]):
maximize
subject to
c x
Ax < b
x > 0,
where x = [xi,X2] T and
[1,5],
5
3
6
2
First, we note that the set of equations {cTx = Xi +5x2 = / , / G R} specifies
a family of straight lines in R 2 . Each member of this family can be obtained
by setting / equal to some real number. Thus, for example, x\ + 5x2 = — 5,
X\ + 5x2 = 0, and X\ + 5x2 = 3 are three parallel lines belonging to the family.
Now, suppose that we try to choose several values for x\ and X2 and observe
how large we can make / while still satisfying the constraints on x\ and X2.
We first try x\ — 1 and X2 = 3. This point satisfies the constraints. For this
point, / = 16. If we now select x\ — 0 and X2 = 5, then / = 25 and this
point yields a larger value for / than does x = [1,3] T . There are infinitely
many points [xi,X2] T satisfying the constraints. Therefore, we need a better
TWO-DIMENSIONAL LINEAR PROGRAMS
8
Figure 15.3
315
Xi
Geometric solution of a linear program in '.
method than trial and error to solve the problem. In the following sections
we develop a systematic approach that simplifies considerably the process of
solving linear programming problems.
For the example above we can easily solve the problem using geometric
arguments. First let us sketch the constraints in R 2 . The region of feasible
points (the set of points x satisfying the constraints Ax < 6, x > 0) is
depicted by the shaded region in Figure 15.3.
Geometrically, maximizing cTx = x\-}-5x2 subject to the constraints can be
thought of as finding the straight line f = x\+ 5x2 that intersects the shaded
region and has the largest / . The coordinates of the point of intersection will
then yield a maximum value of cTx. In our example, the point [0,5] T is the
solution (see Figure 15.3).
Example 15.7 Suppose that you are given two different types of concrete.
The first type contains 30% cement, 40% gravel, and 30% sand (all percentages
of weight). The second type contains 10% cement, 20% gravel, and 70% sand.
The first type of concrete costs $5 per pound and the second type costs $1 per
pound. How many pounds of each type of concrete should you buy and mix
together so that your cost is minimized but you get a concrete mixture that
has at least a total of 5 pounds of cement, 3 pounds of gravel, and 4 pounds
of sand?
316
INTRODUCTION TO LINEAR PROGRAMMING
The problem can be represented as
minimize
subject to
cTx
Ax > b
x>0,
where
c T = [5,l],
Γ0.3 0.1"
0.4 0.2 ,
[o.3 0.7
"5"
b= 3
4
Using the graphical method described above, we get a solution of [0,50] T ,
which means that we should purchase 50 pounds of the second type of concrete. (For a variation of this problem solved using a different method, see
Example 12.1.)
I
In some cases, when using the graphical method, there may be more than
one point of intersection of the optimal straight line f = cTx with the boundary of the feasible region. In this case all of the intersection points will yield
the same value for the objective function cTx, and therefore any one of them
is a solution.
15.4
Convex Polyhedra and Linear Programming
The goal of linear programming is to minimize (or maximize) a linear objective
function
CTX
= C\X\
+ C2X2 H
h CnXn
subject to constraints that are represented by linear equalities and/or inequalities. For the time being, let us consider only constraints of the form Ax < 6,
x > 0. In this section we discuss linear programs from a geometric point of
view (for a review of geometric concepts used in the section, see Chapter 4).
The set of points satisfying these constraints can be represented as the intersection of a finite number of closed half-spaces. Thus, the constraints define
a convex poly tope. We assume, for simplicity, that this poly tope is nonempty
and bounded. In other words, the equations of constraints define a polyhedron
M in R n . Let if be a hyperplane of support of this polyhedron. If the dimension of M is less than n, then the set of all points common to the hyperplane
H and the polyhedron M coincides with M. If the dimension of M is equal to
n, then the set of all points common to the hyperplane H and the polyhedron
M is a face of the polyhedron. If this face is (n — 1)-dimensional, then there
exists only one hyperplane of support, namely, the carrier of this face. If the
CONVEX POLYHEDRA AND LINEAR PROGRAMMING
317
0-dimensional
face
1-dimensional
!— face
Figure 15.4
M.
Hyperplanes of support at different boundary points of the polyhedron
dimension of the face is less than n — 1, then there exist an infinite number
of hyperplanes of support whose intersection with this polyhedron yields this
face (see Figure 15.4).
The goal of our linear programming problem is to maximize a linear objective function f(x) = cTx = c\X\ + · · · + cnxn on the convex polyhedron M.
Next, let H be the hyperplane defined by the equation
cTx
= 0.
Draw a hyperplane of support H to the polyhedron M, which is parallel to H
and positioned such that the vector c points in the direction of the half-space
that does not contain M (see Figure 15.5). The equation of the hyperplane
H has the form
cTx = /?,
and for all x E M we have cTx < ß. Denote by M the convex polyhedron
that is the intersection of the hyperplane of support H with the polyhedron
M. We now show that / is constant on M and that M is the set of all points
in M for which / attains its maximum value. To this end, let y and z be two
arbitrary points in M. This implies that both y and z belong to H. Hence,
f(y) = cTy = ß = cTz =
f(z),
which means that / is constant on M.
Let y be a point of M, and let x be a point of M \ M; that is, x is a point
of M that does not belong to M (see Figure 15.5). Then,
cTx < ß — cTy,
which implies that
318
INTRODUCTION TO LINEAR PROGRAMMING
Figure 15.5
Maximization of a linear function on the polyhedron M.
Thus, the values of / at the points of M that do not belong to M are smaller
than the values at points of M. Hence, / achieves its maximum on M at
points in M.
It may happen that M contains only a single point, in which case / achieves
its maximum at a unique point. This occurs when the the hyperplane of
support passes through an extreme point of M (see Figure 15.6).
15.5
Standard Form Linear Programs
We refer to a linear program of the form
minimize
subject to
cTx
Ax — b
x>0
as a linear program in standard form. Here A is an m x n matrix composed of
real entries, m < n, rank A = m. Without loss of generality, we assume that
Figure 15.6
Unique maximum point of / on the polyhedron M.
STANDARD FORM LINEAR PROGRAMS
319
b > 0. If a component of b is negative, say the ith component, we multiply
the ith constraint by —1 to obtain a positive right-hand side.
Theorems and solution techniques for linear programs are usually stated for
problems in standard form. Other forms of linear programs can be converted
to the standard form, as we now show. If a linear program is in the form
minimize
subject to
cTx
Ax > b
x>0,
then by introducing surplus variables yi, we can convert the original problem
into the standard form
minimize
subject to
c x
anXi + ai2X2 H l· ainxn -y{
Xl > 0, X2 > 0, . . . , Xn > 0
= b{, i = 1 , . . . , m
2/1 > 0,2/2 > 0 , . . . , 2/m > 0.
In more compact notation, the formulation above can be represented as
minimize
subject to
cTx
Ax - Imy
= [A, - I m ]
x > 0, y > 0,
where Im is the m x m identity matrix.
If, on the other hand, the constraints have the form
Ax <b
x > 0,
then we introduce slack variables yi to convert the constraints into the form
Ax + Imy = [A, I m ]
= b
x > 0, y > 0,
where y is the vector of slack variables. Note that neither surplus nor slack
variables contribute to the objective function cTx.
At first glance, it may appear that the two problems
minimize
subject to
cTx
Ax > b
x>0
320
INTRODUCTION TO LINEAR PROGRAMMING
and
minimize
subject to
ex
Ax — Imy
x>0
y>o
=b
are different, in that the first problem refers to the intersection of half-spaces in
the n-dimensional space, whereas the second problem refers to an intersection
of half-spaces and hyperplanes in the (n + m)-dimensional space. It turns
out that both formulations are algebraically equivalent in the sense that a
solution to one of the problems implies a solution to the other. To illustrate
this equivalence, we consider the following examples.
Example 15.8 Suppose that we are given the inequality constraint
x\ < 7.
We convert this to an equality constraint by introducing a slack variable x2 >
0 to obtain
X\+
X2 = 7
x2 > 0 .
Consider the sets C\ = {x\ : X\ < 7} and C2 = {x\ : X\+x2 = 7, x2 > 0}. Are
the sets C\ and C2 equal? It is clear that indeed they are; in this example,
we give a geometric interpretation for their equality. Consider a third set
Cs — {[χι,χ2]Ύ : x\ + x2 = 7,x2 > 0}. Prom Figure 15.7 we can see that
the set Cs consists of all points on the line to the left and above the point
of intersection of the line with the xi-axis. This set, being a subset of R 2 , is
of course not the same set as the set C\ (a subset of R). However, we can
project the set C3 onto the xi-axis (see Figure 15.7). We can associate with
each point X\ G C\ a point [#ι,0] τ on the orthogonal projection of C3 onto
the xi-axis, and vice versa. Note that C2 = {x\ : [xi,x2]T G Cs} = C\.
I
Example 15.9 Consider the inequality constraints
a\X\ + a2x2 < b
x\,x2
> 0,
where αι, a2, and b are positive numbers. Again, we introduce a slack variable
xs > 0 to get
a\X\ + a2x2 + xs = b
#1,^2, #3 > 0.
STANDARD FORM LINEAR PROGRAMS
321
Projection of C 3
onto χ-ι -axis
1 2 3 4 5 6 7\
χ
1
Projection of the set C3 onto the xi-axis.
Figure 15.7
Define the sets
C\ = {[xi,x2]T
C2 = {[#1,22]
: αιΧι + a2x2 < b, X\,x2 > 0},
: Q>iXi + a2x2 + %3 = b, # i , # 2 5 # 3 > 0 } ,
T
C 3 = {[xi,X2,x3]
: αιχι + a2x2 + %3 = &, ^1,^2,^3 > 0}.
We again see that C3 is not the same as C\. However, the orthogonal projection of C3 onto the (xi,x2)-plane allows us to associate the resulting set
with the set C\. We associate the points [xi,X2 5 0] T resulting from the orthogonal projection of C3 onto the (xi,x2)-pla,ne with the points in C\ (see
Figure 15.8). Note that C2 = {[xllx2]T : [xi,x2,xs]T G C 3 } = C\.
■
Example 15.10 Suppose that we wish to maximize
f{xi,x2)
= cixi
+c2x2
subject to the constraints
a\\X\ + a>\2x2 < b\
a2\X\ + a22x2 = b2
Z l , X 2 , > 0,
where, for simplicity, we assume that each α^ > 0 and 61, b2 > 0. The set of
feasible points is depicted in Figure 15.9. Let C\ C R 2 be the set of points
satisfying the constraints.
322
INTRODUCTION TO LINEAR PROGRAMMING
Projection of C 3 onto
the (x-| ,x2)-plane
Figure 15.8
Projection of the set C3 onto the (x 1,2:2)-plane.
Set of feasible points
a-j-jx-j+a^xa^b-j
Figure 15.9
The feasible set for Example 15.10.
STANDARD FORM LINEAR PROGRAMS
a
323
11x1+a12x2+x3=D1
Projection of C 2 onto
(x-,,x2)-plane
Figure 15.10
Projection of C2 onto the (xi,X2)-plane.
Introducing a slack variable, we convert the constraints into standard form:
ß l l ^ l + Ol2#2 + X3 = bi
&2\X\ + 022^2 = bi
%i > 0 , i = 1,2,3.
Let C2 C R 3 be the set of points satisfying the constraints. As illustrated
in Figure 15.10, this set is a line segment (in R 3 ). We now project C2 onto
the (xi,X2)-plane. The projected set consists of the points [xi,X2 ? 0] T , with
[#i,£ 2 >£3] T e C<i for some £3 > 0. In Figure 15.10 this set is marked by a
heavy line in the (#1, X2)-plane. We can associate the points on the projection
with the corresponding points in the set C\.
I
In the following example we convert an optimization problem into a standard form linear programming problem.
Example 15.11 Consider the following optimization problem
maximize
subject to
X2— x\
3#i = x<i — 5
1*2 I < 2
xi < 0 .
To convert the problem into a standard form linear programming problem,
we perform the following steps:
324
INTRODUCTION TO LINEAR PROGRAMMING
1. Change the objective function to: minimizex\ — x2.
2. Substitute x\ = —x[.
3. Write \x2\ < 2 as x2 < 2 and -x2 < 2.
4. Introduce slack variables x$ and £4, and convert the inequalities above
to x2 + #3 = 2 and — x2 + £4 = 2.
5. Write £2 = ^ — v, u,v > 0.
Hence, we obtain
minimize
subject to
— x[ — u + v
3x[ 4- u — v = 5
tz — i; + £3 = 2
t; — u + £4 = 2
# 1 , ^ , V,£3,#4 > 0.
15.6
Basic Solutions
We have seen in Section 15.5 that any linear programming problem involving
inequalities can be converted to standard form, that is, a problem involving
linear equations with nonnegative variables:
minimize
subject to
cTx
Ax = b
x > 0,
where c G R n , A e R m x n , b e M m , m < n, rank A = m, and 6 > 0.
In the following discussion we only consider linear programming problems in
standard form.
Consider the system of equalities
Ax = 6,
where rank A — m. In dealing with this system of equations, we frequently
need to consider a subset of columns of the matrix A. For convenience, we
often reorder the columns of A so that the columns we are interested in
appear first. Specifically, let B be a square matrix whose columns are m
linearly independent columns of A. If necessary, we reorder the columns of
A so that the columns in B appear first: A has the form A = [B,D], where
BASIC SOLUTIONS
325
D is an m x (n — m) matrix whose columns are the remaining columns of A.
The matrix B is nonsingular, and thus we can solve the equation
BxB
=b
for the m-vector XB- The solution is XB = B~lb. Let x be the n-vector
whose first m components are equal to XB and the remaining components are
equal to zero; that is, x = [ccj, 0 T ] T . Then, x is a solution to Ax = b.
Definition 15.1 We call [ # 5 , 0 T ] T a basic solution to Ax = b with respect
to the basis B. We refer to the components of the vector XB as basic variables
and the columns of B as basic columns.
If some of the basic variables of a basic solution are zero, then the basic
solution is said to be a degenerate basic solution.
A vector x satisfying Ax = 6, x > 0, is said to be a feasible solution.
A feasible solution that is also basic is called a basic feasible solution.
If the basic feasible solution is a degenerate basic solution, then it is called
a degenerate basic feasible solution.
I
Note that in any basic feasible solution, XB > 0.
Example 15.12 Consider Ihe equation -Ax — b ^ i t h
A = [αι,α 2 ,α 3 ,α 4 ] =
1 1 - 1 4
1 - 2 - 1 1
b =
where α^ denotes the ith column of the matrix A.
Then, x = [6,2,0,0] T is a basic feasible solution with respect to the basis
B = [αι,α2], x = [0,0,0,2] T is a degenerate basic feasible solution with respect to the basis B = [as, 0,4] (as well as [αι, 0,4} and [02,04]), x = [3,1,0,1] T
is a feasible solution that is not basic, and x = [0,2, — 6,0] T is a basic solution
with respect to the basis B = [02,03], but is not feasible.
I
Example 15.13 As another example, consider the system of linear equations
Ax = 6, where
2
3 - 1 - 1
A =
4
1 1 - 2
We now find all solutions of this system. Note that every solution x of Ax = b
has the form x = v + h, where v i s a particular solution of Ax = b and h is
a solution to Ax — 0.
We form the augmented matrix [A, b] of the system:
[A,b} =
2
4
3 - 1 - 1
-1
1 1 - 2 9
326
INTRODUCTION TO LINEAR PROGRAMMING
Using elementary row operations, we transform this matrix into the form (see
Chapter 16) given by
1 0 1
0 1 -
14
5
_11
5
I
2
The corresponding system of equations is given by
2
1
*1 + 5 Χ 3 " 2 * 4
14
=
Τ
3
11
Solving for the leading unknowns xi and X2, we obtain
14 2
= 15 " 5X3
11 3
Xl
X2 =
+
~T
1
2Xi
+
5* 3 '
·
where £3 and X4 are arbitrary real numbers. If [#ι,£2>#3>#4]lT is
a solution,
then we have
14
2
1
S +
5
2*
11 3
+
5S'
Xl
X2
X3
= s,
X4
= *,
where we have substituted s and t for £3 and £4, respectively, to indicate that
they are arbitrary real numbers.
Using vector notation, we may write the system of equations above as
Γ
xi
\X2
\xs
\X4
'
14 "
5
11
5
0
0
*
3
5
+5
2"
5
1
0
"1"
2
+f
0
0
1
Note that we have infinitely many solutions, parameterized by s, t G R. For
the choice s = t = 0 we obtain a particular solution to Ax = 6, given by
5
_!!
v =
5
0
0
PROPERTIES OF BASIC SOLUTIONS
327
Any other solution has the form v + h, where
"
h
3
5
2"
5
1
_ 0_
"1"
2
0
+t 0
1_
The total number of possible basic solutions is at most
71!
m\(n-m)\
4!
2!(4 - 2)!
■6.
To find basic solutions that are feasible, we check each of the basic solutions
for feasibility.
Our first candidate for a basic feasible solution is obtained by setting x% =
X4 = 0, which corresponds to the basis B = [01,02]. Solving BXB = b, we
obtain Xß = [14/5,-11/5]' , and hence x = [14/5,-11/5,0,0]' is a basic
solution that is not feasible.
For our second candidate basic feasible solution, we set #2 = #4 = 0. We
have the basis B = [αι,α 3 ]. Solving BXB = b yields XB = [4/3,11/3] .
Hence, x = [4/3,0,11/3,0] is a basic feasible solution.
A third candidate basic feasible solution is obtained by setting x2 = x$ = 0.
However, the matrix
^
r
i
Γ2 - l l
B = [αι,α 4 ] =
is singular. Therefore, B cannot be a basis, and we do not have a basic
solution corresponding to B = [0,1,0,4],
We get our fourth candidate for a basic feasible solution by setting X\ =
X4 = 0. We have a basis B = [02,03], resulting i n x = [0,2,7,0] T , which is a
basic feasible solution.
Our fifth candidate for a basic feasible solution corresponds to setting x\ —
xs = 0, with the basis B = [a 2 ,04]. This results in x = [0, —11/5,0, —28/5] ,
which is a basic solution that is not feasible.
Finally, the sixth candidate for a basic feasible solution is obtained by
setting xx = x2 = 0. This results in the basis B = [03,04], and x =
[0,0,11/3, —8/3] , which is a basic solution but is not feasible.
I
15.7
Properties of Basic Solutions
In this section we discuss the importance of basic feasible solutions in solving
linear programming (LP) problems. We first prove the fundamental theorem
of LP, which states that when solving an LP problem, we need only consider
328
INTRODUCTION TO LINEAR PROGRAMMING
basic feasible solutions. This is because the optimal value (if it exists) is always
achieved at a basic feasible solution. We need the following definitions.
Definition 15.2 Any vector x that yields the minimum value of the objective
function cTx over the set of vectors satisfying the constraints Ax = b, x > 0,
is said to be an optimal feasible solution.
An optimal feasible solution that is basic is said to be an optimal basic
feasible solution.
I
Theorem 15.1 Fundamental
in standard form.
Theorem of LP. Consider a linear program
1. If there exists a feasible solution, then there exists a basic feasible solution.
2. If there exists an optimal feasible solution, then there exists an optimal
basic feasible solution.
□
Proof. We first prove part 1. Suppose that
a feasible
solution and it has p positive components. Without loss of generality, we can
assume that the first p components are positive, whereas the remaining components are zero. Then, in terms of the columns of A — [αχ,..., α ρ , . . . , a n ] ,
this solution satisfies
x\a\ + x2Q>2 H
+ XpUp = o.
There are now two cases to consider.
Case 1: If αι, a 2 , . . . , ap are linearly independent, then p < m. If p = m,
then the solution x is basic and the proof is done. If, on the other hand,
p < m, then, since rank A = m, we can find m — p columns of A from the
remaining n — p columns so that the resulting set of m columns forms a basis.
Hence, the solution a? is a (degenerate) basic feasible solution corresponding
to the basis above.
Case 2: Assume that ai,a2,...
,ap are linearly dependent. Then, there
exist numbers yi, i = 1 , . . . ,p, not all zero, such that
2/ifli + 2/2^2 + · · · + ypap = 0.
We can assume that there exists at least one yi that is positive, for if all the
yi are nonpositive, we can multiply the equation above by —1. Multiply the
equation by a scalar ε and subtract the resulting equation from X\a\ +X2CL2 +
1- xpap = b to obtain
{xi - eyi)ai + (x2 - ey2)a2 H
Let
h (xp - eyp)ap = b.
V = [2/i,...,2/ P ,0,...,0] T .
PROPERTIES OF BASIC SOLUTIONS
329
Then, for any e we can write
A[x — ey] = b.
Let e = mm{xi/yi : i = 1 , . . . ,p, y% > 0}. Then, the first p components of
x — ey are nonnegative, and at least one of these components is zero. We
then have a feasible solution with at most p — 1 positive components. We
can repeat this process until we get linearly independent columns of A, after
which we are back to case 1. Therefore, part 1 is proved.
We now prove part 2. Suppose that x = [ # i , . . . , xn]T is an optimal feasible
solution and only the first p variables are nonzero. Then, we have two cases
to consider. The first case is exactly the same as in part 1. The second case
follows the same arguments as in part 1, but in addition we must show that
x — ey is optimal for any ε. We do this by showing that cTy = 0. To this
end, assume that cTy φ 0. Note that for e of sufficiently small magnitude
(|ε| < m i n { | ^ / ^ | : i = 1 , . . . ,p, yi φ 0}), the vector x — ey is feasible. We
can choose e such that cTx > cTx — ecTy = cT(x — ey). This contradicts
the optimality of x. We can now use the procedure from part 1 to obtain an
optimal basic feasible solution from a given optimal feasible solution.
I
E x a m p l e 15.14 Consider the system of equations given in Example 15.13.
Find a nonbasic feasible solution to this system and use the method in the
proof of the fundamental theorem of LP to find a basic feasible solution.
Recall that solutions for the system given in Example 15.13 have the form
Γ
x =
14
11
5
0
0
"
"
3
+5
2'
5
5
1
0
"11
2
-hi
o
0
1
where s, t G R. Note that if s = 4 and t = 0, then
XQ —
6'
5
1
5
4
is a nonbasic feasible solution.
There are constants y^ i = 1,2,3, such that
2/1 a i +2/202+2/303 = 0.
For example, let
2
2/1 = ~ 7 '
2/2—,
2/3 = 1.
330
INTRODUCTION TO LINEAR PROGRAMMING
Note that
where
A(x0 - ey) = 6,
_2"
5
3
5
1
If e = 1/3, then
xi = x0 - ey
is a basic feasible solution.
I
Observe that the fundamental theorem of LP reduces the task of solving
a linear programming problem to that of searching over a finite number of
basic feasible solutions. That is, we need only check basic feasible solutions
for optimality. As mentioned before, the total number of basic solutions is at
most
n!
m\(n — m)V
Although this number is finite, it may be quite large. For example, if m — 5
and n = 50, then
'5θ\
= 2,118,760.
This is potentially the number of basic feasible solutions to be checked for
optimality. Therefore, a more efficient method of solving linear programs is
needed. To this end, in the next section we analyze a geometric interpretation
of the fundamental theorem of LP. This leads us to the simplex method for
solving linear programs, which we discuss in Chapter 16.
15.8
Geometric View of Linear Programs
Recall that a set Θ C W1 is said to be convex if, for every cc, y G Θ and every
real number a, 0 < a < 1, the point ax + (1 — a)y £ Θ. In other words, a
set is convex if given two points in the set, every point on the line segment
joining these two points is also a member of the set.
Note that the set of points satisfying the constraints
Ax = b, x > 0
GEOMETRIC VIEW OF LINEAR PROGRAMS
331
is convex. To see this, let X\ and x2 satisfy the constraints, that is, Axi = 6,
x% > 0, i = 1,2. Then, for all a G (0,1), A(ax1 + (1 - a)x2) = α-Αχι + (1 a)Ax2 = b. Also, for a G (0,1), we have ax\ -f (1 — OL)X2 > 0.
Recall that a point cc in a convex set Θ is said to be an extreme point of Θ if
there are no two distinct points X\ and x2 in Θ such that x = ax\ + (1 — a)x2
for some a G (0,1). In other words, an extreme point is a point that does
not lie strictly within the line segment connecting two other points of the
set. Therefore, if x is an extreme point, and x = αχχ + (1 — a)x2 for some
Xi,x2 £ Θ and a G (0,1), then x\ = x2. In the following theorem we
show that extreme points of the constraint set are equivalent to basic feasible
solutions.
T h e o r e m 15.2 Let Ω be the convex set consisting of all feasible solutions,
that is, all n-vectors x satisfying
Ax = 6,
x > 0,
where A G R m X n ; m < n. Then, x is an extreme point of Ω if and only if x
is a basic feasible solution to Ax — b, x > 0.
□
Proof. =>: Suppose that x satisfies Ax = 6, x > 0, and has p positive
components. As before, without loss of generality, we can assume that the
first p components are positive and the remaining components are zero. We
have
x\a\ + χ2α>2 + · · · + XpQ>p = b.
Let ?/i, i = 1 , . . . ,p, be numbers such that
2/i«i + 2/2^2 H
h ypap = 0.
We show that each yi = 0. To begin, multiply this equation by ε > 0, then
add and subtract the result from the equation Χχα,ι + x2a2 + · · · + xpa,p = b
to get
(xi + eyi)ai + (x2 + 62/2)02 H
(xi - £j/i)ai + (x2 - ey2)a2 Λ
h (z p + £2/p)op = 6,
h (xp - eyp)ap = b.
Because each Xi > 0, ε > 0 can be chosen such that each xi + εy^,Xi — εyi>0
(e.g., ε = min{\xi/yi\ : z = 1 , . . . ,p, ^ ^ 0}). For such a choice of ε, the
vectors
zi = [xi + ε 2 / ι , χ 2 + ε ? / 2 , . · . , ^ ρ + ε?/ρ,0, . . . , 0 ] T ,
^2 = [xi -eyi,X2
-εy2,...,Xp-εyp,0,...,0}τ
belong to Ω. Observe that x = \z\ + \z2. Because x is an extreme point, Z\ =
z2. Hence, each yi = 0, which implies that the ai are linearly independent.
332
INTRODUCTION TO LINEAR PROGRAMMING
<=: Let x G Ω be a basic feasible solution. Let y, z G Ω be such that
cc = ay + (1 — a ) z
for some a G (0,1). We show that y = z and conclude that x is an extreme
point. Because t/, z > 0, and the last n — m components of x are zero,
the last n — m components of y and z are zero as well. Furthermore, since
Ay = Az = 6,
2/i αι H
ht/mflm = 6
and
ζιαι H
(- zmam = b.
Combining these two equations yields
(yi - ζι)αι +
h (ym - zm)am
= 0.
Because the columns o i , . . . , a m are linearly independent, we have yi = ^ ,
z = 1 , . . . , ra. Therefore, y = z, and hence x is an extreme point of Ω.
I
From Theorem 15.2 it follows that the set of extreme points of the constraint set Ω = {x : Ax = 6, x > 0} is equal to the set of basic feasible solutions to Ax = 6, x > 0. Combining this observation with the fundamental
theorem of LP (Theorem 15.1), we can see that in solving linear programming
problems we need only examine the extreme points of the constraint set.
Example 15.15 Consider the following LP problem:
maximize 3x\ + 5^2
subject to xi + 5x2
2xi + x 2
xi + x 2
xi,x2
<
<
<
>
40
20
12
0.
We introduce slack variables x 3 , x 4 , x 5 to convert this LP problem into standard form:
minimize -3xi— 5x2
subject to xi + 5x2 + X3
2xi + X2 + #4
=40
=20
#1 + #2 +
X5 = 12
xi,...,x5 > 0.
In the remainder of the example we consider only the problem in standard
form. We can represent the constraints above as
xi
1
1
5
0
0
2 + X2 1 + X3 0 + X4 1 + £5 0 =
1
1
1
0
0
40
20
12
x i , . . . ,x 5 > 0,
GEOMETRIC VIEW OF LINEAR PROGRAMS
that is, x\a\ + X2CL2 + #303 +
X±O>A
333
+ #505 = 6, x > 0. Note that
x = [0,0,40,20,12] T
is a feasible solution. But for this x, the value of the objective function is
zero. We already know that the minimum of the objective function (if it
exists) is achieved at an extreme point of the constraint set Ω defined by
the constraints. The point [0,0,40,20,12] T is an extreme point of the set of
feasible solutions, but it turns out that it does not minimize the objective
function. Therefore, we need to seek the solution among the other extreme
points. To do this we move from one extreme point to an adjacent extreme
point such that the value of the objective function decreases. Here, we define
two extreme points to be adjacent if the corresponding basic columns differ
by only one vector. We begin with x = [0,0,40,20,12] T . We have
Οαι + 0a 2 + 40a 3 4- 20a 4 + 12a 5 = b.
To select an adjacent extreme point, let us choose to include a\ as a basic
column in the new basis. We need to remove either 03, CI4, or α§ from the
old basis. We proceed as follows. We first express a\ as a linear combination
of the old basic columns:
CL\
= las + 2a4 + la^.
Multiplying both sides of this equation by E\ > 0, we get
e\a\ = Eias + 2ειθ4 + εια§.
We now add this equation to the equation Οαι -\-Oa2 +40a3 + 20a4 + 12as = b.
Collecting terms yields
ελαλ + 0a 2 + (40 - ελ)α3 + (20 - 2ει)α 4 + (12 - εχ)α 5 = b.
We want to choose S\ in such a way that each of the coefficients above is
nonnegative and at the same time, one of the coefficients 03, 04, or a§ becomes
zero. Clearly, e\ = 10 does the job. The result is
ΙΟαι 4- 30a 3 + 2a 5 = 6.
The corresponding basic feasible solution (extreme point) is
[10,0,30,0,2] T .
For this solution, the objective function value is —30, which is an improvement
relative to the objective function value at the old extreme point.
We now apply the same procedure as above to move to another adjacent
extreme point, which hopefully further decreases the value of the objective
function. This time, we choose a 2 to enter the new basis. We have
1
9
1
334
INTRODUCTION TO LINEAR PROGRAMMING
X1
/ Γ101\ y. ^
X
\*1
Figure 15.11
Graphical solution to the LP problem in Example 15.15.
and
10--ε2]αι+ε2α2+ η θ - - ε
2
) α3 + ( 2
ε2
«5
Substituting ε 2 = 4, we obtain
8αι + 4α 2 + 12α 3 = 6.
The solution is [8,4,12,0,0] T and the corresponding value of the objective
function is —44, which is smaller than the value at the previous extreme point.
To complete the example we repeat the procedure once more. This time, we
select a± and express it as a combination of the vectors in the previous basis,
Oi, a 2 , and a 3 :
Ö4 = α>ι — a>2 + 4a 3 ,
and hence
(8 - ε 3 ) αι + (4 + ε 3 ) α 2 + (12 - 4ε 3 ) α 3 + ε 3 α 4 = b.
The largest permissible value for ε 3 is 3. The corresponding basic feasible
solution is [5,7,0,3,0] T , with an objective function value of —50. The solution
[5,7,0,3,0] T turns out to be an optimal solution to our problem in standard
form. Hence, the solution to the original problem is [5,7] T , which we can
easily obtain graphically (see Figure 15.11).
I
EXERCISES
335
The technique used in this example for moving from one extreme point to
an adjacent extreme point is also used in the simplex method for solving LP
problems. The simplex method is essentially a refined method of performing
these manipulations.
EXERCISES
15.1 Convert the following linear programming problem to standard form:
maximize
subject to
2^i -f x2
0 < x\ < 2
x\ + %2 < 3
x\ + 2x2 < 5
x2 > 0 .
15.2 Consider a discrete-time linear system Xk+i = axk+buk, where Uk is the
input at time k, Xk is the output at time &, and a, b G R are system parameters.
Given an initial condition XQ — 1, consider the problem of minimizing the
output X2 at time 2 subject to the constraint that \ui\ < 1, i = 0,1.
Formulate the problem as a linear programming problem, and convert it
into standard form.
15.3 Consider the optimization problem
minimize
subject to
Ci|xi| +c 2 |#2| H
Ax = 6,
Hc n |z n |
where c* Φ 0, i = 1 , . . . , n. Convert this problem into an equivalent standard
form linear programming problem.
Hint: Given any x G M, we can find unique numbers x + , x~ G R, x + , x~ > 0,
such that \x\ = x+ + x~ and x = x+ — x~.
15.4 Does every linear programming problem in standard form have a
nonempty feasible set? If "yes," provide a proof. If "no," give a specific
example.
Does every linear programming problem in standard form (assuming a
nonempty feasible set) have an optimal solution? If "yes," provide a proof. If
"no," give a specific example.
15.5 Suppose that a computer supplier has two warehouses, one located in
city A and another in city B. The supplier receives orders from two customers,
one in city C and another in city D. The customer in city C orders 50 units,
336
INTRODUCTION TO LINEAR PROGRAMMING
and the customer in city D orders 60 units. The number of units at the
warehouse in city A is 70, and the number of units at the warehouse in city
B is 80. The cost of shipping each unit from A to C is 1, from A to D is 2,
from B to C is 3, and from B to D is 4.
Formulate the problem of deciding how many units from each warehouse
should be shipped to each customer to minimize the total shipping cost (assuming that the values of units to be shipped are real numbers). Express the
problem as an equivalent standard form linear programming problem.
15.6 Consider a computer network consisting of six computers, A through F.
The computers are connected according to the following links, with maximum
data rates (in Mbps) shown: AC (10), BC (7), BF (3), CD (8), DE (12), DF
(4). For example, "AC (10)" means that computers A and C are connected
with a link that supports data rates up to 10 Mbps.
Suppose that A and B need to send data to E and F , respectively (no
other communication is taking place in the network). Any path through the
given links above may be used as long as the path has no loop. Also, multiple
paths (say from A to E) can be used simultaneously. Link bandwidth can
be shared as long as the total data rate through the link does not exceed its
maximum (the total data rate through a link is the sum of the data rates of
communication in both directions).
For every Mbps of data rate the network can support for transmission from
A to E, we receive 2 dollars. For every Mbps of data rate the network can
support for transmission from B to F, we receive 3 dollars. Formulate a linear
programming problem to represent the goal of maximizing the total revenue.
Then, convert this problem into standard form.
Hint: Draw a picture of the network, then label each link with the maximum data rate and the paths that share that link.
15.7 A cereal manufacturer wishes to produce 1000 pounds of a cereal that
contains exactly 10% fiber, 2% fat, and 5% sugar (by weight). The cereal is
to be produced by combining four items of raw food material in appropriate
proportions. These four items have certain combinations of fiber, fat, and
sugar content, and are available at various prices per pound:
Item
% fiber
%fat
% sugar
Price/lb
1
3
6
20
2
2
8
46
5
4
3
16
9
4
1
4
4
9
0
2
The manufacturer wishes to find the amounts of each item to be used to
produce the cereal in the least expensive way. Formulate the problem as a
linear programming problem. What can you say about the existence of a
solution to this problem?
EXERCISES
337
15.8 Suppose that a wireless broadcast system has n transmitters. Transmitter j broadcasts at a power of pj > 0. There are m locations where the
broadcast is to be received. The path gain from transmitter j to location i is
gij; that is, the power of the signal transmitted from transmitter j received
at location i is gijPj. The total power received at location i is the sum of the
powers received from all the transmitters. Formulate the problem of finding
the minimum sum of the powers transmitted subject to the requirement that
the power received at each location is at least P.
15.9 Consider the system of equations
[2 - 1
2 -1
3"
1 2
3
1
0
1 0 - 2 0 - 5
X2
X3
=
X4
" 14"
5
-10
L X 5.
Check if the system has basic solutions. If yes, find all basic solutions.
15.10 Solve the following linear program graphically:
maximize
2#i + 5x2
subject to
0 < x\ < 4
0 < x2 < 6
15.11 The optimization toolbox in MATLAB provides a function, linprog,
for solving linear programming problems. Use the function l i n p r o g to solve
the problem in Example 15.5. Use the initial condition 0.
CHAPTER 16
SIMPLEX METHOD
16.1
Solving Linear Equations Using Row Operations
The examples in previous chapters illustrate that solving linear programs
involves the solution of systems of linear simultaneous algebraic equations. In
this section we describe a method for solving a system of n linear equations in
n unknowns that we use in subsequent sections. The method uses elementary
row operations and corresponding elementary matrices. For a discussion of
numerical issues involved in solving a system of simultaneous linear algebraic
equations, we refer the reader to [41] and [53].
An elementary row operation on a given matrix is an algebraic manipulation of the matrix that corresponds to one of the following:
1. Interchanging any two rows of the matrix
2. Multiplying one of its rows by a real nonzero number
3. Adding a scalar multiple of one row to another row
An elementary row operation on a matrix is equivalent to premultiplying the
matrix by a corresponding elementary matrix, which we define next.
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
339
340
SIMPLEX METHOD
Definition 16.1 We call E an elementary matrix of the first kind if E is
obtained from the identity matrix I by interchanging any two of its rows. I
An elementary matrix of the first kind formed from I by interchanging the
ith and the j t h rows has the form
0
1
···
1
ith row
E
jth row
Note that E is invertible and E = E - 1
Definition 16.2 We call E an elementary matrix of the second kind if E is
obtained from the identity matrix I by multiplying one of its rows by a real
number a φ 0.
I
The elementary matrix of the second kind formed from I by multiplying
the zth row by a φ 0 has the form
"l
E.
a
ith row
SOLVING LINEAR EQUATIONS USING ROW OPERATIONS
341
Note that E is invertible and
"l
E~l =
1/a
zth row
Definition 16.3 We call E an elementary matrix of the third kind if E is
obtained from the identity matrix I by adding ß times one row to another
row of I.
I
An elementary matrix of the third kind obtained from I by adding ß times
the jth row to the ith. row has the form
ß
ith row
E =
j t h row
Observe that E is the identity matrix with an extra ß in the (i, j)th location.
Note that E is invertible and
1
-ß
ith row
1
E-
j t h row
Definition 16.4 An elementary row operation (of first, second, or third kind)
on a given matrix is a premultiplication of the given matrix by a corresponding
elementary matrix of the respective kind.
I
342
SIMPLEX METHOD
Because elementary matrices are invertible, we can define the corresponding
inverse elementary row operations.
Consider a system of n linear equations in n unknowns xi,X2,..-,xn
with
right-hand sides bi, &2> · · · > &n- I n matrix form this system may be written as
Ax = b
where
\xi
,
x
b =
~h]
Ae
bn
1 %n
If A is invertible, then
A~lb.
x =
Thus, the problem of solving the system of equations Ax = 6, with A G
R n x n invertible, is related to the problem of computing A~l. We now show
that A~l can be computed effectively using elementary row operations. In
particular, we prove the following theorem.
Theorem 16.1 Let A e RnXn be a given matrix. Then, A is nonsingular
(invertible) if and only if there exist elementary matrices Ei, i = l , . . . , t ,
such that
Ef''' E2E1A = I.
D
Proof =>: If A is nonsingular, then its first column must have at least one
nonzero element, say a^\ φ 0. Premultiplying A by an elementary matrix of
the first kind of the form
0
1
ΕΛ =
j t h row
1
brings the nonzero element aj\ to the location (1,1). Hence, in the matrix
Ei A, the element an φ 0. Note that since Ei is nonsingular, EiA is also
nonsingular.
SOLVING LINEAR EQUATIONS USING ROW OPERATIONS
343
Next, we premultiply E\ A by an elementary matrix of the second kind of
the form
1/an
1
The result of this operation is the matrix E2E1A with unity in the location
(1,1). We next apply a sequence of elementary row operations of the third
kind on the matrix E2E1A. Specifically, we premultiply Ε^ΕχΑ by n — 1
elementary matrices of the form
1
1
-G21
E —
E3 =
1
1
-0>nl
where r = 2-f-n—l = n + l. The result of these operations is the nonsingular
matrix
ErET—\ ·'' E^E\A
1
äi2
0
a22
[0
an2
—
·· ·
a\n
&2r\
'··
dnn I
Because the matrix Er · · · Εχ A is nonsingular, its submatrix
Ö22
*· ·
ä>2n
0"η2
' ' '
&η
must also be nonsingular. This implies that there is a nonzero element äj2,
where 2 < j < n. Using an elementary operation of the first kind, we bring
this element to the location (2,2). Thus, in the matrix
Er+\Er
'''
E\A
the (2,2)th element is nonzero. Premultiplying the matrix by an elementary
matrix of the second kind yields the matrix
r+2-^r+l * - £ i A ,
in which the element in the location (2,2) is unity. As before, we premultiply
this matrix by n — 1 elementary row operations of the third kind, to get a
344
SIMPLEX METHOD
matrix of the form
Es ''' Er -' · E\A
1 0 <2i3
0 1 «23
0 0 α33
0
0
'
än3
··
^ΐη
·'
&2n
··
«3η
Q"nn
where s = r + 2 + n— 1 = 2(n + l). This matrix is nonsingular. Hence, there
is a nonzero element äj3, 3 < j < n. Proceeding in a similar fashion as before,
we obtain
Et · - - Es · - · Er · · - E\ A = / ,
where t = n(n + 1).
<=: If there exist elementary matrices Ei,...,Et
Et'"E1A
such that
= IJ
then clearly A is invertible, with
A-1=Et-'E1.
Theorem 16.1 suggests the following procedure for finding A \ if it exists.
We first form the matrix
[A, I}.
We then apply elementary row operations to [A, I] so that A is transformed
into i"; that is, we obtain
^ . . . ^ [ Α , Ι ] = [/,£?].
It then follows that
B = Et
Ελ
=Α~\
Example 16.1 Let
2
1
-2
-1
Find A " 1 .
5
10
1 1 0
-10 -30
-2
-3
0
1
0
SOLVING LINEAR EQUATIONS USING ROW OPERATIONS
345
We form the matrix
[A,I] =
2
5
1
1
- 2 -10
- 1 - 2
10 0 1
1 0
0
-30 1 0
- 3 0 0
0 0
1 0
0 1
0 0
0
0
0
1
and perform row operations on this matrix. Applying row operations of the
first and third kinds yields
1 1
1 0
0 1 0
0
0 3
8
0
1 - 2 0 0
0 - 8 -28 1 0 2
1 0
0 - 1 - 2 0 0 1 0 1
We then interchange the second and fourth rows and apply elementary row
operations of the second and third kinds to get
1 0 - 1
0 1
2
0 0
2
0 0 -12
0 0 2 0 1
0
0 - 1 0 - 1
0 1 1 0
3
1 0 -6 1 -8
Now multiply the third row by 1/2 and then perform a sequence of elementary
operations of the third kind to obtain
"l 0
0 1
0 0
0 0
0 0U
0 0
1 0
U
0 1
I
5
2
-1
λ
2
6
Hence,
2
A~l
=
2
-1
-2
λ
λ
2
0
6
5 "
2
0
-2 0
λ
0
U
2
0 1
2
2
u
-4
2
2
10_
5 "
2
0 -4
3
0
2
u
1 10
We now return to the general problem of solving the system of equations
Ax = 6, A e R n x n . If A~l exists, then the solution is x — A~lb. However,
we do not need an explicit expression for A - 1 to find the solution. Indeed,
let A~ be expressed as a product of elementary matrices
A"1 =
EtEt.
• E-1 ·
346
SIMPLEX METHOD
Thus,
Ef-EiAx
and hence
x=
=
Ef>Eib
Ef-E1b.
The discussion above leads to the following procedure for solving the system
Ax = b. Form an augmented matrix
[A,b]Then, perform a sequence of row elementary operations on this augmented
matrix until we obtain
Prom the above we have that if a? is a solution to Ax = 6, then it is also
a solution to EAx = Eb, where E = Et · · · Ei represents a sequence of
elementary row operations. Because EA = J, and Eb = 6, it follows that
x = b is the solution to Ax = 6, A G R n x n invertible.
Suppose now that A G R m x n where m < n, and rank A = m. Then, A is
not a square matrix. Clearly, in this case the system of equations Ax = b has
infinitely many solutions. Without loss of generality, we can assume that the
first m columns of A are linearly independent. Then, if we perform a sequence
of elementary row operations on the augmented matrix [A, 6] as before, we
obtain
[I,D,b],
where D is an m x (n — m) matrix. Let x G R n be a solution to Ax = b and
write x = [xj^x])}1", where xB G R m , xD G R^" 771 ). Then, [I,D]x = 6,
which we can rewrite as XB + DXD = b, or XB = b — DXQ. Note that
for an arbitrary XD G R ^ n _ m \ if XB = b — DXD, then the resulting vector
x = [ x j , xJ)]T is a solution to Ax = b. In particular, [b , 0 T ] T is a solution
to Ax = b. We often refer to the basic solution [b , 0 T ] T as a particular
solution to Ax = b. Note that [— (DXD)T,χ~ρ]τ
is a solution to Ax = 0.
Any solution to Ax = b has the form
x =
+
-DxD
XD
for some xD G R ( n _ m ) .
16.2
The Canonical Augmented Matrix
Consider the system of simultaneous linear equations Ax = 6, rank A = m.
Using a sequence of elementary row operations and reordering the variables
THE CANONICAL AUGMENTED MATRIX
347
if necessary, we transform the system Ax = b into the following canonical
form:
Xl
#2
+ V\ m + l ^ m + l H
V VlnXn
+ 2/2m+l#m+l H
h 2/2n#n = 2/20
*^m i l/mm-j-l%m-\-l
= 2/10
ι * * * ~r 1/τηη%η = 2/m0·
This can be represented in matrix notation as
[ J m , Y m , n - m ] * = y0·
Formally, we define the canonical form as follows.
Definition 16.5 A system Ax = b is said to be in canonical form if among
the n variables there are m variables with the property that each appears in
only one equation, and its coefficient in that equation is unity.
I
A system is in canonical form if by some reordering of the equations and
the variables it takes the form [J m ,"K m?n _ m ]ic = y0. If a system of equations Ax = b is not in canonical form, we can transform the system into
canonical form by a sequence of elementary row operations. The system in
canonical form has the same solution as the original system Ax = b and is
called the canonical representation of the system with respect to the basis
O i , . . . , am. There are, in general, many canonical representations of a given
system, depending on which columns of A we transform into the columns of
Im (i.e., basic columns). We call the augmented matrix [ J m , l r m > n _ m , y 0 ]
of the canonical representation of a given system the canonical augmented
matrix of the system with respect to the basis α ι , . . . , a m . Of course, there
may be many canonical augmented matrices of a given system, depending on
which columns of A are chosen as basic columns.
The variables corresponding to basic columns in a canonical representation of a given system are the basic variables, whereas the other variables
are the nonbasic variables. In particular, in the canonical representation
]x = yQ of a given system, the variables X\,..., Xm are the
basic variables and the other variables are the nonbasic variables. Note that
in general the basic variables need not be the first m variables. However, in
the following discussion we assume, for convenience and without loss of generality, that the basic variables are indeed the first m variables in the system.
348
SIMPLEX METHOD
Having done so, the corresponding basic solution is
xi = 2/10,
2/m05
*£m+l
0,
Xn — U',
that is,
x =
I/o
0
Given a system of equations Ax = 6, consider the associated canonical
augmented matrix
1 0
0 1
·· •
.· •
0
0
2/1 771+1
•
2/ln
2/10
2/2 m + l
*
2/2n
2/20
[-*77l5 * 771,71 — 7715 2/oJ —
0 0
1
2/:771771+1
2/m0
Prom the arguments above we conclude that
b = 2/io«i + 2/20^2 H
l·
2/moam.
In other words, the entries in the last column of the canonical augmented matrix are the coordinates of the vector 6 with respect to the basis {a\,..., a m } .
The entries of all the other columns of the canonical augmented matrix have a
similar interpretation. Specifically, the entries of the j t h column of the canonical augmented matrix, j = 1 , . . . ,n, are the coordinates of a,j with respect
to the basis { o i , . . . , a m } · To see this, note that the first m columns of the
augmented matrix form a basis (the standard basis). Every other vector in
the augmented matrix can be expressed as a linear combination of these basis
vectors by reading the coefficients down the corresponding column. Specifically, let a£, i = 1 , . . . ,n + 1, be the ith column in the augmented matrix
above. Clearly, since a[,..., afm form the standard basis, then for m < j < n,
a
'j = 2 / l j ° l + 2/2j«2 H
1" 2 / m j a m ·
Let a*, i = 1 , . . . ,n, be the zth column of A, and α η + χ = 6. Now, a\ =
Ea,i, z = l , . . . , n + l , where E is a nonsingular matrix that represents the
elementary row operations needed to transform [A, 6] into [ Z m , y m > n _ m , j / 0 ] .
Therefore, for m < j < n, we also have
dj = 2/ijOi + 2/2j«2 H
h 2/mjOm.
UPDATING THE AUGMENTED MATRIX
16.3
349
Updating the Augmented Matrix
To summarize Section 16.2, the canonical augmented matrix of a given system
Ax = b specifies the representations of the columns a,j, m < j < n, in terms
of the basic columns α ι , . . . , a m . Thus, the elements of the jth column of the
canonical augmented matrix are the coordinates of the vector aj with respect
to the basis c t i , . . . , o m . The coordinates of b are given in the last column.
Suppose that we are given the canonical representation of a system Ax = b.
We now consider the following question: If we replace a basic variable by a
nonbasic variable, what is the new canonical representation corresponding to
the new set of basic variables? Specifically, suppose that we wish to replace
the basis vector a p , 1 < p < m, by the vector aq, m < q < n. Provided
that the first m vectors with ap replaced by aq are linearly independent,
these vectors constitute a basis and every vector can be expressed as a linear
combination of the new basic columns.
Let us now find the coordinates of the vectors a\,..., an with respect to
the new basis. These coordinates form the entries of the canonical augmented
matrix of the system with respect to the new basis. In terms of the old basis,
we can express aq as
771
771
ΐφρ
Note that the set of vectors { α ι , . . . , a p _ i , a g , a p + i , . . . , a m } is linearly independent if and only if ypq φ 0. Solving the equation above for a p , we get
771
Λ
ap = — a q — 2^ — a i Vpq
2=1
Vpq
ϊφρ
Recall that in terms of the old augmented matrix, any vector α^, m < j < n,
can be expressed as
aj = yijdi
+ y2jCL2 H
l·
ymjOm·
Combining the last two equations yields
3 = Σ (y* ~ ? ^ ) α ί + !r"a«·
a
ΐφρ
Denoting the entries of the new augmented matrix by y[j, we obtain
/
Vij =yiJ
tfpj
Vpq
Vpj
-—Viq*
Vpq
·
l
j
^P>
350
SIMPLEX METHOD
Therefore, the entries of the new canonical augmented matrix can be obtained
from the entries of the old canonical augmented matrix via the formulas above.
These equations are often called the pivot equations, and ypq, the pivot element
We refer to the operation on a given matrix by the formulas above as
pivoting about the (p,q)th element Note that pivoting about the (p,q)ih
element results in a matrix whose qih column has all zero entries, except the
(p, <7)th entry, which is unity. The pivoting operation can be accomplished
via a sequence of elementary row operations, as was done in the proof of
Theorem 16.1.
16.4
The Simplex Algorithm
The essence of the simplex algorithm is to move from one basic feasible solution to another until an optimal basic feasible solution is found. The canonical
augmented matrix discussed in Section 16.3 plays a central role in the simplex
algorithm.
Suppose that we are given the basic feasible solution
x = [xi,...,xm,0, ...,0]T,
or equivalently
x\CL\ H
Xi > 0, i = l , . . . , r a
h x m o m — b.
In Section 16.3 we saw how to update the canonical augmented matrix if we
wish to replace a basic column by a nonbasic column, that is, if we wish to
change from one basis to another by replacing a single basic column. The
values of the basic variables in a basic solution corresponding to a given basis
are given in the last column of the canonical augmented matrix with respect
to that basis; that is, Xi = yio, i = l , . . . , r a . Basic solutions are not necessarily feasible—the values of the basic variables may be negative. In the
simplex method we want to move from one basic feasible solution to another.
This means that we want to change basic columns in such a way that the
last column of the canonical augmented matrix remains nonnegative. In this
section we discuss a systematic method for doing this.
In the remainder of this chapter we assume that every basic feasible solution
of
Ax = 6,
x >0
is a nondegenerate basic feasible solution. We make this assumption primarily
for convenience—all arguments can be extended to include degeneracy.
Let us start with the basic columns α ι , . . . , a m , and assume that the corresponding basic solution x = [?/io,..., ymo> 0 , . . . , 0] T is feasible; that is, the
THE SIMPLEX ALGORITHM
351
entries y^, i = 1 , . . . , ra, in the last column of the canonical augmented matrix
are positive. Suppose that we now decide to make the vector aq, q > ra, a
basic column. We first represent aq in terms of the current basis as
Q>q = VlqCil + y2q<*>2 H
V Vrnq^m-
Multiplying the above by ε > 0 yields
eaq = eyiqdi + ey2qa2 H
l·
eymqam.
We combine this equation with
2/10Ö1 H
H UmO^m = &
to get
(2/10 - £2/ΐς)αι + (?/2o ~ £y2q)a2 H
Note that the vector
h (ymo ~ e y m g ) a m -l· ε ο ς = 6.
Γ Vio ~ zy\q 1
2/77iO
£ymq
0
ε
[
0
J
where ε appears in the ^th position, is a solution to Ax = b. If ε = 0, then
we obtain the old basic feasible solution. As ε is increased from zero, the
qth component of the vector above increases. All other entries of this vector
will increase or decrease linearly as ε is increased, depending on whether
the corresponding yiq is negative or positive. For small enough ε, we have
a feasible but nonbasic solution. If any of the components decreases as ε
increases, we choose ε to be the smallest value where one (or more) of the
components vanishes. That is,
ε = mm{yio/yiq : yiq > 0}.
With this choice of ε we have a new basic feasible solution, with the
vector aq replacing a p , where p corresponds to the minimizing index
p = sigmin^yio/yiq
: yiq > 0}.
So, we now have a new basis
α ι , . . . , a p _ i , a p + i , . . . , a m , aq. As we can see, ap was replaced by aq in the
new basis. We say that aq enters the basis and ap leaves the basis. If the
minimum in mmi{yio/yiq : yiq > 0} is achieved by more than a single index, then the new solution is degenerate and any of the zero components can
352
SIMPLEX METHOD
be regarded as the component corresponding to the basic column that leaves
the basis. If none of the yiq are positive, then all components in the vector [?/io — eyiq,..., y-mo — eymq, 0 , . . . , ε , . . . , 0] T increase (or remain constant)
as ε is increased, and no new basic feasible solution is obtained, no matter
how large we make e. In this case there are feasible solutions having arbitrarily large components, which means that the set Ω of feasible solutions is
unbounded.
So far, we have discussed how to change from one basis to another, while
preserving feasibility of the corresponding basic solution, assuming that we
have already chosen a nonbasic column to enter the basis. To complete our
development of the simplex method, we need to consider two more issues. The
first issue concerns the choice of which nonbasic column should enter the basis.
The second issue is to find a stopping criterion, that is, a way to determine if a
basic feasible solution is optimal or is not. To this end, suppose that we have
found a basic feasible solution. The main idea of the simplex method is to
move from one basic feasible solution (extreme point of the set Ω) to another
basic feasible solution at which the value of the objective function is smaller.
Because there is only a finite number of extreme points of the feasible set, the
optimal point will be reached after a finite number of steps.
We already know how to move from one extreme point of the set Ω to a
neighboring one by updating the canonical augmented matrix. To see which
neighboring solution we should move to and when to stop moving, consider
the following basic feasible solution:
[ x 5 , 0 T ] T = [2/ 10 ,...,2/mO,0,...,0] T
together with the corresponding canonical augmented matrix, having an identity matrix appearing in the first m columns. The value of the objective
function for any solution x is
z = C\X\ + C2X2 H
h
cnxn.
For our basic solution, the value of the objective function is
z = z0 = cBxB
where
= C12/10 H
h c m 2/ m0 ,
c j = [ci,c2,...,cm].
To see how the value of the objective function changes when we move from
one basic feasible solution to another, suppose that we choose the qth column,
m < q < n, to enter the basis. To update the canonical augmented matrix,
let p = diTgrnm^yio/yiq : yiq > 0} and e = ypo/ypq. The new basic feasible
THE SIMPLEX ALGORITHM
solution is
353
Γ 2/10 ~ eyiq 1
UrnO
^Vmq
0
ε
° J
I
Note that the single ε appears in the qth component, whereas the pth component is zero. Observe that we could have arrived at the basic feasible solution
above simply by updating the canonical augmented matrix using the pivot
equations from the previous Section 16.3:
y%j - —yiq,
ίφν,
Upq
y'pj =
Vpq
where the qth column enters the basis and the pth column leaves [i.e., we
pivot about the (p, q)th element]. The values of the basic variables are entries
in the last column of the updated canonical augmented matrix.
The cost for this new basic feasible solution is
z = ci(2/io - yiqs) H
h c m (2/ m0 - ymqe) + cqe
= ZQ + [cq - {ciyiq H
where z0 = ayio H
h cmym0.
h βτηι/της)]ε,
Let
Zq — Ciyiq
~T ' ' ' ~T~ C-mymq'
Then,
Z = Z0 -h (Cq -
Zq)e.
Thus, if
Z - Z0 = (Cq ~ Zq)e < 0,
then the objective function value at the new basic feasible solution above is
smaller than the objective function value at the original solution (i.e., z < ZQ).
Therefore, if cq — zq < 0, then the new basic feasible solution with aq entering
the basis has a lower objective function value.
On the other hand, if the given basic feasible solution is such that for all
q = ra + 1,.. . , n ,
Cq ~ Zq > 0,
354
SIMPLEX METHOD
then we can show that this solution is in fact an optimal solution. To show
this, recall from Section 16.1 that any solution to Ax — b can be represented
as
* m,n—m*^D \
Vo
X =
0 +
XD
for some xp = [ x m + i , . . . , x n ] T € ^
the above, we obtain
m
n
^· Using manipulations similar to
n
cTx = z0 + ] P
{ci-Zi)xi,
where Zi = c\yu + · · · + c m y m i, i = m + l , . . . , n . For a feasible solution we
have Xi > 0, i = 1 , . . . , n. Therefore, if c* — z» > 0 for alH = ra + 1 , . . . , n,
then any feasible solution x will have objective function value cTx no smaller
than zo·
Let ri = 0 for z = 1 , . . . , m and r^ = c» — z» for i = m + 1 , . . . , n. We call
ri the zth reduced cost coefficient or relative cost coefficient. Note that the
reduced cost coefficients corresponding to basic variables are zero.
We summarize the discussion above with the following result.
Theorem 16.2 A basic feasible solution is optimal if and only if the corresponding reduced cost coefficients are all nonnegative.
ü
At this point we have all the necessary steps for the simplex algorithm.
Simplex Algorithm
1. Form a canonical augmented matrix corresponding to an initial basic
feasible solution.
2. Calculate the reduced cost coefficients corresponding to the nonbasic variables.
3. If rj > 0 for all j , stop—the current basic feasible solution is optimal.
4. Select a q such that rq < 0.
5. If no yiq > 0, stop—the problem is unbounded; else, calculate p =
aigmin^yio/yiq : yiq > 0}. (If more than one index i minimizes yio/yiq,
we let p be the smallest such index.)
6. Update the canonical augmented matrix by pivoting about the (p, q)ih
element.
7. Go to step 2.
We state the following result for the simplex algorithm, which we have
already proved in the foregoing discussion.
THE SIMPLEX ALGORITHM
355
Theorem 16.3 Suppose that we have an LP problem in standard form that
has an optimal feasible solution. If the simplex method applied to this problem
terminates and the reduced cost coefficients in the last step are all nonnegative,
then the resulting basic feasible solution is optimal
G
Example 16.2 Consider the following linear program (see also Exercise 15.10):
maximize
subject to
2x\ + 5^2
x\ < 4
£2 < 6
xi + #2 < 8
Xl,X2 > 0.
We solve this problem using the simplex method.
Introducing slack variables, we transform the problem into standard form:
minimize
subject to
— 2#i
x\
—5#2
— 0^3
+£3
+#4
X2
X\
£l,
—OX4 —OX5
+X2
#2,
#3?
#4j
=
4
=
6
+X*>
=
8
#5
>
0.
The starting canonical augmented matrix for this problem is
<2i
CL2
a
3
a
4
°5
&
1 0
1 0
0 4
0
1 0
1 0
6
1 1 0
0
1 8
Observe that the columns forming the identity matrix in the canonical augmented matrix above do not appear at the beginning. We could rearrange the
augmented matrix so that the identity matrix would appear first. However,
this is not essential from the computational point of view.
The starting basic feasible solution to the problem in standard form is
x = [0,0,4,6,8] T .
The columns 03, 04, and a§ corresponding to £3, £4, and £5 are basic, and
they form the identity matrix. The basis matrix is B = [03,04,0,5] = 13.
The value of the objective function corresponding to this basic feasible
solution is z = 0. We next compute the reduced cost coefficients corresponding
to the nonbasic variables x\ and £2· They are
n=ci-
z1=ci-
r2 = C2-z2
(c3j/ii + c4i/2i + C52/31) = - 2 ,
= C2- (c3yi2 + Q2/22 + c5y32) = - 5 .
356
SIMPLEX METHOD
We would like now to move to an adjacent basic feasible solution for which
the objective function value is lower. Naturally, if there is more than one such
solution, it is desirable to move to the adjacent basic feasible solution with
the lowest objective value. A common practice is to select the most negative
value of rj and then to bring the corresponding column into the basis (see
Exercise 16.18 for an alternative rule for choosing the column to bring into the
basis). In our example, we bring a<i into the basis; that is, we choose a<i as the
new basic column. We then compute p = argminji/io/to · V%2 > 0} = 2. We
now update the canonical augmented matrix by pivoting about the (2,2)th
entry using the pivot equations:
V'ij = Vij ~ — 2/<2, i φ 2,
J
2/22
y'2j =
3
&.
2/22
The resulting updated canonical augmented matrix is
CL\
a,<i α>3 α4
α^
b
1 0
1 0
0 4
0
1 0
1 0
6
1 0
0 - 1 1 2
Note that 02 entered the basis and a± left the basis. The corresponding basic
feasible solution is x = [0,6,4,0,2] T . We now compute the reduced cost
coefficients for the nonbasic columns:
n=ci-zi
= -2,
Γ4 = C4 — Z4 =
5.
Because r\ = — 2 < 0, the current solution is not optimal, and a lower objective function value can be obtained by bringing a\ into the basis. Proceeding
to update the canonical augmented matrix by pivoting about the (3, l)th element, we obtain
CL\
Q.2
Ö3
Ö4
Ö5
b
0
0
1
1 - 1 2
0
1 0
1 0
6
1 0
0 - 1 1 2
The corresponding basic feasible solution is x = [2,6,2,0,0] T . The reduced
cost coefficients are
Γ4 = C4 — Z4 =
3,
r*5 = c 5 - 25 = 2.
Because no reduced cost coefficient is negative, the current basic feasible solution x = [2,6,2,0,0] T is optimal. The solution to the original problem is
therefore X\ = 2, x<i = 6, and the objective function value is 34.
|
MATRIX FORM OF THE SIMPLEX METHOD
357
We can see from Example 16.2 that we can solve a linear programming
problem of any size using the simplex algorithm. To make the calculations
in the algorithm more efficient, we discuss the matrix form of the simplex
method in the next section.
16.5
Matrix Form of the Simplex Method
Consider a linear programming problem in standard form:
minimize
subject to
cTx
Ax = b
x>0.
Let the first m columns of A be the basic columns. The columns form a square
mxm nonsingular matrix B. The nonbasic columns of A form a n m x ( n - m )
matrix D. We partition the cost vector correspondingly as c T = [Cß,cJ)].
Then, the original linear program can be represented as follows:
minimize
subject to
CßXB + CpXp
[£?, D]
> 0,
XB
XB
=
XD
XD
+
BXB
DXD
= b
> 0.
If XD = 0, then the solution x = [Xß, x~b]T = [##> 0 T ] T is the basic feasible
solution corresponding to the basis B. It is clear that for this to be a solution,
we need XB = ß _ 1 b ; that is, the basic feasible solution is
x =
B-'b
0
The corresponding objective function value is
z0 =
cTBB~lb.
If, on the other hand, xr> φ 0, then the solution x =
In this case XB is given by
XB =
[X~B,XJ)]T
B~1b-B"1DxD,
and the corresponding objective function value is
z = CBXB +
= cJ(B
_1
CDXD
6 - ΒλΌχΌ)
= cTBB~lb + ( c j -
+ cTDxD
cTBB-lD)xD.
is not basic.
358
SIMPLEX METHOD
Defining
we obtain
z = ZQ + rJ)XDThe elements of the vector TD are the reduced cost coefficients corresponding
to the nonbasic variables.
If TD > 0, then the basic feasible solution corresponding to the basis B
is optimal. If, on the other hand, a component of rp is negative, then the
value of the objective function can be reduced by increasing a corresponding
component of #£>, that is, by changing the basis.
We now use the foregoing observations to develop a matrix form of the
simplex method. To this end we first add the cost coefficient vector c T to the
bottom of the augmented matrix [A, b] as follows:
A
cT
b
0
B
D
-D
b
0
We refer to this matrix as the tableau of the given LP problem. The tableau
contains all relevant information about the linear program.
Suppose that we now apply elementary row operations to the tableau such
that the top part of the tableau corresponding to the augmented matrix [A, b]
is transformed into canonical form. This corresponds to premultiplying the
tableau by the matrix
Γ Β _ 1 O|
o1 1
The result of this operation is
B 1
0T
0
1
B
D
cl
cl
BXD
b
0
-D
B~lb
0
We now apply elementary row operations to the tableau above so that the entries of the last row corresponding to the basic columns become zero. Specifically, this corresponds to premultiplication of the tableau by the matrix
The result is
BlD
-c
-D
B~lb
0
T
o
Β~λΌ
-clB^D
B-'b
-clB^b
We refer to the resulting tableau as the canonical tableau corresponding to
the basis B. Note that the first m entries of the last column of the canonical
MATRIX FORM OF THE SIMPLEX METHOD
359
tableau, B~ b, are the values of the basic variables corresponding to the
basis B. The entries of c J — c^B~lD in the last row are the reduced cost
coefficients. The last element in the last row of the tableau, —c]3B~1b, is
the negative of the value of the objective function corresponding to the basic
feasible solution.
Given an LP problem, we can in general construct many different canonical
tableaus, depending on which columns are basic. Suppose that we have a
canonical tableau corresponding to a particular basis. Consider the task of
computing the tableau corresponding to another basis that differs from the
previous basis by a single vector. This can be accomplished by applying
elementary row operations to the tableau in a similar fashion as discussed
above. We refer to this operation as updating the canonical tableau. Note
that updating of the tableau involves using exactly the same update equations
as we used before in updating the canonical augmented matrix, namely, for
i = l , . . . , r a + 1,
Vpq
itpj
Vpq
5
where yij and ^ are the (i,j)th entries of the original and updated canonical
tableaus, respectively.
Working with the tableau is a convenient way of implementing the simplex
algorithm, since updating the tableau immediately gives us the values of both
the basic variables and the reduced cost coefficients. In addition, the (negative
of the) value of the objective function can be found in the lower right-hand
corner of the tableau. We illustrate the use of the tableau in the following
example.
Example 16.3 Consider the following linear programming problem:
maximize
subject to
7x\ -f 6x2
2^i + X2 < 3
xi + 4x2 < 4
X\,X2 > 0.
We first transform the problem into standard form so that the simplex method
can be applied. To do this we change the maximization to minimization by
multiplying the objective function by —1. We then introduce two nonnegative
slack variables, x3 and x 4 , and construct the tableau for the problem:
a\
cT
a,2
a>3 0,4
b
2
1 1 0
3
1 4
0
1 4
-7 -6 0
0 0
360
SIMPLEX METHOD
Notice that this tableau is already in canonical form with respect to the basis
[03,04]. Hence, the last row contains the reduced cost coefficients, and the
rightmost column contains the values of the basic variables. Because r\ = — 7
is the most negative reduced cost coefficient, we bring a\ into the basis. We
then compute the ratios yio/yii = 3/2 and 2/20/2/21 = 4. Because 2/10/2/11 <
2/20/2/21, we get p = argmini{2/io/2/ii : Vn > 0} = 1. We pivot about the
(1, l)th element of the tableau to obtain
11
I
I
2
0
1
w
0
_i
2
0 -I
2
w
2
1
2
2
^
5
2
1 0 f
In the second tableau above, only r2 is negative. Therefore, q = 2 (i.e., we
bring a<i into the basis). Because
V™. = 3
2/12
2/20 _ 5
2/22
'
7
we have p = 2. We thus pivot about the (2,2)th element of the second tableau
to obtain the third tableau:
1
0
1
u
u
n
u
1
n
υ
0
*
_I
7
1 - 1 2
7
22
?
8
7
7
5
7
5
7
86
77
Because the last row of the third tableau above has no negative elements, we
conclude that the basic feasible solution corresponding to the third tableau is
optimal. Thus, x\ = 8/7, #2 = 5/7, £3 = 0, x± = 0 is the solution to our LP in
standard form, and the corresponding objective value is —86/7. The solution
to the original problem is simply xi = 8/7, #2 = 5/7, and the corresponding
objective value is 86/7.
I
Degenerate basic feasible solutions may arise in the course of applying
the simplex algorithm. In such a situation, the minimum ratio yio/yiq is 0.
Therefore, even though the basis changes after we pivot about the (p, q)th
element, the basic feasible solution does not (and remains degenerate). It is
possible that if we start with a basis corresponding to a degenerate solution,
several iterations of the simplex algorithm will involve the same degenerate
solution, and eventually the original basis will occur. The entire process will
then repeat indefinitely, leading to what is called cycling. Such a scenario,
although rare in practice, is clearly undesirable. Fortunately, there is a simple
rule for choosing q and p, due to Bland, that eliminates the cycling problem
(see Exercise 16.18):
q = min{z : r* < 0},
p = min{j : yj0/yjq
= min{yi0/yiq
: yiq > 0}}.
TWO-PHASE SIMPLEX METHOD
16.6
361
Two-Phase Simplex Method
The simplex method requires starting with a tableau for the problem in canonical form; that is, we need an initial basic feasible solution. A brute-force approach to finding a starting basic feasible solution is to choose m basic columns
arbitrarily and transform the tableau for the problem into canonical form. If
the rightmost column is positive, then we have a legitimate (initial) basic
feasible solution. Otherwise, we would have to pick another candidate basis.
Potentially, this brute-force procedure requires ( m ) tries, and is therefore not
practical.
Certain LP problems have obvious initial basic feasible solutions. For example, if we have constraints of the form Ax < b and we add m slack variables
2i > · · · j 2m j then the constraints in standard form become
[AJn
where z = [21,..., zm]T.
= b,
>0,
The obvious initial basic feasible solution is
and the basic variables are the slack variables. This was the case in the
example in Section 16.5.
Suppose that we are given a linear program in standard form:
minimize
subject to
cTx
Ax = b
x>0.
In general, an initial basic feasible solution is not always apparent. We therefore need a systematic method for finding an initial basic feasible solution
for general LP problems so that the simplex method can be initialized. For
this purpose, suppose that we are given an LP problem in standard form.
Consider the following associated artificial problem:
minimize
subject to
yi + y2 H
[A, I m ]
>0,
l·
ym
362
SIMPLEX METHOD
where y = [2/1,... ,2/m]T· We call y the vector of artificial variables. Note
that the artificial problem has an obvious initial basic feasible solution:
Γθΐ
[6J '
We can therefore solve this problem by the simplex method.
Proposition 16.1 The original LP problem has a basic feasible solution if
and only if the associated artificial problem has an optimal feasible solution
with objective function value zero.
O
Proof =>►: If the original problem has a basic feasible solution x, then the
vector [cc T ,0 T ] T is a basic feasible solution to the artificial problem. Clearly,
this solution has an objective function value of zero. This solution is therefore
optimal for the artificial problem, since there can be no feasible solution with
negative objective function value.
<=: Suppose that the artificial problem has an optimal feasible solution
with objective function value zero. Then, this solution must have the form
[ # τ , 0 T ] T , where x > 0. Hence, we have Ax = 6, and x is a feasible solution
to the original problem. By the fundamental theorem of LP, there also exists
a basic feasible solution.
I
Assume that the original LP problem has a basic feasible solution. Suppose that the simplex method applied to the associated artificial problem has
terminated with an objective function value of zero. Then, as indicated in
the proof above, the solution to the artificial problem will have all yi — 0,
i = 1 , . . . ,ra. Hence, assuming nondegeneracy, the basic variables are in the
first n components; that is, none of the artificial variables are basic. Therefore,
the first n components form a basic feasible solution to the original problem.
We can then use this basic feasible solution (resulting from the artificial problem) as the initial basic feasible solution for the original LP problem (after
deleting the components corresponding to artificial variables). Thus, using
artificial variables, we can attack a general linear programming problem by
applying the two-phase simplex method. In phase I we introduce artificial variables and the artificial objective function and find a basic feasible solution.
In phase II we use the basic feasible solution resulting from phase I to initialize the simplex algorithm to solve the original LP problem. The two-phase
simplex method is illustrated in Figure 16.1.
Example 16.4 Consider the following linear programming problem:
minimize
subject to
2x\ + 3#2
4 χ ι + 2^2 > 12
x\ + 4x 2 > 6
X\,X2 > 0.
TWO-PHASE SIMPLEX METHOD
363
I
PHASE I I
(Finding an
optimal solution)
Figure 16.1
!
PHASE I
W (Finding a basic
, feasible solution)
Illustration of the two-phase simplex method.
First, we express the problem in standard form by introducing surplus
variables:
minimize
subject to
2x\ + 3x2
4#i + 2x2 — X3 — 12
x\ + 4^2 — #4 = 6
xi,... ,£4 > 0.
For the LP problem above there is no obvious basic feasible solution that we
can use to initialize the simplex method. Therefore, we use the two-phase
method.
Phase I. We introduce artificial variables χ^,χβ > 0, and an artificial objective function x 5 + x6. We form the corresponding tableau for the problem:
CL\
cT
a<i 03
2 -1
4
1 4
0
0
d\ 0,5 ae b
0
1
0 12
0 - 1 0 1 6
0
0
1
1 0
To initiate the simplex procedure, we must update the last row of this tableau
to transform it into canonical form. We obtain
a\ a,2 CI3 d\ 05 ae
4
2 -1
0
1
0
1 4
0 - 1 0 1
- 5 - 6
1
1
0
0
b
12
6
-18
364
SIMPLEX METHOD
The basic feasible solution corresponding to this tableau is not optimal.
Therefore, we proceed with the simplex method to obtain the next tableau:
7
2
1
4
7
2
-1
0
1
0
1
0
1
0
0
1
2
1
4
1
2
1
4
3
2
1
2
9
3
2
-9
We still have not yet reached an optimal basic feasible solution. Performing
another iteration, we get
2
7
1 0
0 1
0 0
1
7
1
14
0
2
7
2
7
1
14
0
2
7
1
1
7
18
7
6
7
1
0
Both of the artificial variables have been driven out of the basis, and the
current basic feasible solution is optimal. We now proceed to phase II.
Phase II. We start by deleting the columns corresponding to the artificial
variables in the last tableau in phase I and revert back to the original objective
function. We obtain
«I
Ö2
0
1
3
«3
2
7
1
14
0
a±
1
7
2
7
0
b
18
7
6
7
0
We transform the last row so that the zeros appear in the basis columns; that
is, we transform the tableau above into canonical form:
1 0
0 1
0 0
2
7
1
14
5
14
1
7
4
7
2
7
18
7
6
7
54
7
All the reduced cost coefficients are nonnegative. Hence, the optimal solution
is
'18 6
X
=
7'7<°'°
and the optimal cost is 54/7.
16.7
Revised Simplex Method
Consider an LP problem in standard form with a matrix A of size m x n.
Suppose that we use the simplex method to solve the problem. Experience
suggests that if m is much smaller than n, then, in most instances, pivots will
REVISED SIMPLEX METHOD
365
occur in only a small fraction of the columns of the matrix A. The operation of pivoting involves updating all the columns of the tableau. However,
if a particular column of A never enters any basis during the entire simplex procedure, then computations performed on this column are never used.
Therefore, if m is much smaller than n, the effort expended on performing
operations on many of the columns of A may be wasted. The revised simplex
method reduces the amount of computation leading to an optimal solution by
eliminating operations on columns of A that do not enter the bases.
To be specific, suppose that we are at a particular iteration in the simplex
algorithm. Let B be the matrix composed of the columns of A forming the
current basis, and let D be the matrix composed of the remaining columns of
A. The sequence of elementary row operations on the tableau leading to this
iteration (represented by matrices Ei,..., Ek) corresponds to premultiplying
JB, D, and 6 by B~l = Ek · · · E\. In particular, the vector of current values
of the basic variables is B~xb. Observe that computation of the current basic
feasible solution does not require computation of B~1D; all we need is the
matrix B~l. In the revised simplex method we do not compute B~ D. Instead, we only keep track of the basic variables and the revised tableau, which
is the tableau [JB -1 , B~lb}. Note that this tableau is only of size m x (ra + 1 )
[compared to the tableau in the original simplex method, which is m x (n+1)].
To see how to update the revised tableau, suppose that we choose the column aq to enter the basis. Let y = B aq, y0 = [yoi
, 2/0mJ = B 6,
and p = argmiUilyio/yiq : yiq > 0} (as in the original simplex method).
Then, to update the revised tableau, we form the augmented revised tableau
[B~1,yQ,yq] and pivot about the pth element of the last column. We claim
that the first ra + 1 columns of the resulting matrix comprise the updated
revised tableau (i.e., we simply remove the last column of the updated augmented revised tableau to obtain the updated revised tableau). To see this,
write B~l as B~l = Ek · · ■ -Ei, and let the matrix Ek+ι represent the pivoting operation above (i.e., Ek+iyq = e p , the pth column of the mxm identity
matrix). The matrix Ek+ι is given by
1
E fc+l
-Vlq/Vl pq
0
l/2/j pq
0
VmqlV\
pq
1
Then, the updated augmented tableau resulting from the pivoting operation
above is [Ek+\B ,#H-i2/o> e pl· L e t Bv
be the new basis. Then, we have
1
Bn^w = Ek+i · · · Ελ. But notice that Bnew
= Ek+iB~1, and the values
of the basic variables corresponding to Bnew are given by y0new = 22/c+i2/o·
Hence, the updated tableau is indeed [i?new>2/onew] = [^fc+i-B_1,^fc+i2/o]·
We summarize the foregoing discussion in the following algorithm.
366
SIMPLEX METHOD
Revised Simplex Method
1. Form a revised tableau corresponding to an initial basic feasible solution
[B-\y0].
2. Calculate the current reduced cost coefficients vector via
rl = cTDwhere
\
T
=
XTD,
cTBB-x.
3. If Tj > 0 for all j , stop—the current basic feasible solution is optimal.
4. Select a q such that rq < 0 (e.g., the q corresponding to the most negative
rq), and compute
yq = B 1 ^ .
5. If no yiq > 0, stop—the problem is unbounded; else, compute p =
a r g m i n ^ o / ^ : yiq > 0}.
6. Form the augmented revised tableau [B~1,y0,yq],
and pivot about the
pth element of the last column. Form the updated revised tableau by
taking the first ra + 1 columns of the resulting augmented revised tableau
(i.e., remove the last column).
7. Go to step 2.
The reason for computing rp in two steps as indicated in step 2 is as
follows. We first note that rp = c j — c^B~lD.
To compute CgB~lD, we
1
can do the multiplication in the order either (c]3B~ )D or c]3(B~1D). The
former involves two vector-matrix multiplications, whereas the latter involves
a matrix-matrix multiplication followed by a vector-matrix multiplication.
Clearly, the former is more efficient.
As in the original simplex method, we can use the two-phase method to
solve a given LP problem using the revised simplex method. In particular,
we use the revised tableau from the final step of phase I as the initial revised
tableau in phase II. We illustrate the method in the following example.
Example 16.5 Consider solving the following LP problem using the revised
simplex method:
maximize
subject to
3x\ + 5x2
x\ + X2 < 4
5xi + 3x 2 > 8
Xl,X2 > 0.
REVISED SIMPLEX METHOD
367
First, we express the problem in standard form by introducing one slack and
one surplus variable, to obtain
minimize
subject to
— 3x\ — 6x2
x\ + X2 + X3 = 4
5#i + 3^2 — X4 = 8
# i , . . . ,£4 > 0.
There is no obvious basic feasible solution to this LP problem. Therefore, we
use the two-phase method.
Phase I. We introduce one artificial variable x$ and an artificial objective
function £5. The tableau for the artificial problem is
CL\
CL2
1
5
0
cT
Q>3
05
Q>A
b
1 1 0
0 4
3
0 - 1 1 8
0
0
0
1 0
We start with an initial basic feasible solution and corresponding 2? _ 1 , as
shown in the following revised tableau:
Β~λ
Variable
y0
T~Ö I
x~3
0
x5
1 8
We compute
A T = C 5 B " 1 = [0,1],
rl = cl-\TD
= [0,0,0] - [5,3,-1] = [-5,-3,1] =
[rur2,r4].
Because r\ is the most negative reduced cost coefficient, we bring a\ into the
basis. To do this, we first compute yx = B~la\.
In this case, yx = a\. We
get the augmented revised tableau:
B~l
y0
y1
xs
T~Ö
4
Γ
£5
0
Variable
1 8
5
We then compute p = argmin^^o/?/^ : Viq > 0} = 2 and pivot about the
second element of the last column to get the updated revised tableau:
B~l
Variable
X 3
x
*i
0
y0
I
5
5
§
368
SIMPLEX METHOD
We next compute
clB1
[0,0],
rl = c l - X D = [0,0,1] = [r2,r4,r5] > 0 T .
T
The reduced cost coefficients are all nonnegative. Hence, the solution to the
artificial problem is [8/5,0,12/5,0,0] . The initial basic feasible solution for
phase II is therefore [8/5,0,12/5,0] T .
Phase II. The tableau for the original problem (in standard form) is
a\
1
5
-3
cT
α<ι α^ &\ b
1 1 0
4
3
0 - 1 8
-5 0
0 0
As the initial revised tableau for phase II, we take the final revised tableau
from phase I. We then compute
λ τ = cTBB~l = [0, - 3 ]
1
-Γ
_o l
3"
5
"
°'-5.
5
3"
1
0
5
3
-1
rl = cTD-XTD = [-5,0]
,o]- 0,—
'
z=
~~5'
We bring a2 into the basis, and compute y2 = B~la2
B~l
Variable
1
5
Xl
5
8
y0
y2
5
5
3
5
5
16
3"
5
[^2,r4].
to get
In this case we get p — 2. We update this tableau by pivoting about the
second element of the last column to get
Variable
>-i
1 - i3
L
X2
U
3
I/o
*
3
3
We compute
λ τ = clß-1
= [0, -5]
rl = d - XTD = [-3,0]
_I
1
3
3
o-i
1
5
0
-1
16 _ 5
=
3 ' 3
[η,η].
EXERCISES
369
We now bring 0,4 into the basis:
Β~λ
Variable
1
0
x3
x2
y0
y4
_I
3
4
3
1
3
3
3
I
We update the tableau to obtain
Variable
x4
x2
8
_I
3
B~l
y0
3 - 1 4
1 0
4
We compute
TD-1
c^B
rTD=cTD-\TD
[0,-5]
3
1
-1
0
-5,0],
1 1
= [2,5] = [n,r 3 ].
5 0
= [-3,0] - [ - 5 , 0 ]
The reduced cost coefficients are all positive. Hence, [0,4,0,4] T is optimal.
The optimal solution to the original problem is [0,4] T .
I
EXERCISES
16.1 This question is concerned with elementary row operations and rank.
a. For the matrix
1 2
2 -1
3 1
1 2
-1
3
2
3
3
0
3
1
2
1
3
1
find its rank by first transforming the matrix using elementary row operations into an upper triangular form.
b . Find the rank of the following matrix for different values of the parameter
7 by first transforming the matrix using elementary row operations into
an upper triangular form:
A =
1
2
1
7
-1
10
-1
7
-6
2
5
1
370
SIMPLEX METHOD
16.2 Consider the following standard form LP problem:
minimize
subject to
2#i — X2 — £3
3xi + #2 + #4 = 4
6x1 + 2#2 + £3 + £4 = 5
#1,#2,£3J#4 >
0.
a. Write down the A, 6, and c matrices/vectors for the problem.
b . Consider the basis consisting of the third and fourth columns of A, ordered according to [04,03]. Compute the canonical tableau corresponding to this basis.
c. Write down the basic feasible solution corresponding to the basis above,
and its objective function value.
d. Write down the values of the reduced cost coefficients (for all the variables) corresponding to the basis.
e. Is the basic feasible solution in part c an optimal feasible solution? If yes,
explain why. If not, determine which element of the canonical tableau
to pivot about so that the new basic feasible solution will have a lower
objective function value.
f. Suppose that we apply the two-phase method to the problem, and at the
end of phase I, the tableau for the artificial problem is
0
1
0
0 - 1 1
3
0
3
0
2
υ
0
3
1
- 1 3
3
3
1 0
Does the original problem have a basic feasible solution? Explain.
g. Prom the final tableau for phase I in part f, find the initial canonical
tableau for phase II.
16.3 Use the simplex method to solve the following linear program:
maximize
X\ + χ<χ + 3a?3
subject to
xi + x3 = 1
X2 + £3 =
^1,^2,^3 >
2
0.
EXERCISES
371
16.4 Consider the linear program
maximize
subject to
2xi + x2
0 < xx < 5
0 < x2 < 7
x\ + x2 < 9.
Convert the problem to standard form and solve it using the simplex method.
16.5 Consider a standard form linear programming problem with
?
?
A—
?
?
0 1
1 0
τ
8
7
?
=
b =
6 '
?
where the "?" symbols signify unknowns to be determined. Suppose that the
canonical tableau corresponding to some basis is
[~0 1
1 0
1
3
2 ?1
4 ? .
[θ 0 - 1 1 ?J
a. Find all entries of A.
b . Find all entries of c.
c. Find the basic feasible solution corresponding to the canonical tableau
above.
d. Find all entries in the rightmost column of the tableau.
16.6 Consider the optimization problem
minimize
subject to
ci|xi| + c2\x2\ H
Ax = b.
l·
cn\xn\
We can convert this problem into an equivalent standard form linear programming problem by introducing the new variables
Xi = x\ — x~l where xf > 0, x~ > 0,
and
\xi\ = xf +x~,
i = 1,2,..., n
i = 1,2, . . . , n
(See also Exercise 15.3.) Then we can apply the simplex method to solve the
equivalent problem. Explain, in two or three sentences, why we will always
372
SIMPLEX METHOD
have that only either xf or x{ can be positive but never both xf and xi can
be positive. In other words, we will always have, x~\x~ = 0.
16.7 Suppose that we are given a linear programming problem in standard form (written in the usual notation) and are told that the vector
x = [1,0,2,3,0] T is a basic feasible solution with corresponding relative cost
coefficient vector r — [0,1,0,0, — 1] T and objective function value 6. We are
also told that the vector [—2,0,0,0,4] T lies in the nullspace of A.
a. Write down the canonical tableau corresponding to the given basic feasible solution above, filling in as many values of entries as possible (use
the symbol * for entries that cannot be determined from the information
given). Clearly indicate the dimensions of the tableau.
b . Find a feasible solution with an objective function value that is strictly
less than 6.
16.8 Consider a standard form linear programming problem (with the usual
A, 6, and c). Suppose that it has the following canonical tableau:
0 1 0
1 1 2
0
0 0 3
1 0 0 4 0
0 -
1
2
3
4
5
6
7
8
a. Find the basic feasible solution corresponding to this canonical tableau
and the corresponding value of the objective function.
b . Find all the reduced cost coefficient values associated with the tableau.
c. Does the given linear programming problem have feasible solutions with
arbitrarily negative objective function values?
d. Suppose that column a^ enters the basis. Find the canonical tableau for
the new basis.
e. Find a feasible solution with objective function value equal to —100.
f. Find a basis for the nullspace of A.
16.9 Consider the problem
maximize
subject to
— x\ — 2^2
x\ > 0
X2 > I-
EXERCISES
373
a. Convert the problem into a standard form linear programming problem.
b . Use the two-phase simplex method to compute the solution to this problem and the value of the objective function at the optimal solution of the
problem.
16.10 Consider the linear programming problem
minimize
subject to
— x\
x\ — X2 = 1
^1,^2 > 0.
a. Write down the basic feasible solution for x\ as a basic variable.
b . Compute the canonical augmented matrix corresponding to the basis in
part a.
c. If we apply the simplex algorithm to this problem, under what circumstance does it terminate? (In other words, which stopping criterion in
the simplex algorithm is satisfied?)
d. Show that in this problem, the objective function can take arbitrarily
negative values over the constraint set.
16.11 Find the solution and the value of the optimal cost for the following
problem using the revised simplex method:
minimize
subject to
x\ + x2
x\ + 2^2 > 3
2xi + x2 > 3
X\,X2 > 0.
Hint: Start with x\ and x2 as basic variables.
16.12 Solve the following linear programs using the revised simplex method:
a. Maximize — 4#i — Zx2 subject to
5^i + X2 > 11
- 2 x i - x2 < - 8
xi + 2x 2 > 7
χ\,χ2
> o.
374
SIMPLEX METHOD
b. Maximize 6x1 + 4x2 + 7x3 -f 5x4 subject to
xi + 2x2 + x3 + 2x4 < 20
6xi H- 5x2 + 3x3 + 2x4 < 100
3xi + 4x2 + 9x3 + 12x4 < 75
Xl,X 2 ,X3,#4 > 0.
16.13 Consider a standard form linear programming problem with
~7l
0 2 0 1
1 1 0
0
0 3
1 0
Suppose that we are told that the reduced cost coefficient vector corresponding
to some basis is r T = [0,1,0,0].
a. Find an optimal feasible solution to the problem.
b . Find c 2 .
16.14 Consider the linear programming problem
minimize
subject to
c\X\ -f c 2 x 2
2xi 4- x 2 = 2
#1,#2 :> 0,
where c\, c2 G R. Suppose that the problem has an optimal feasible solution
that is not basic.
a. Find all basic feasible solutions.
b . Find all possible values of c\ and c 2 .
c. At each basic feasible solution, compute the reduced cost coefficients for
all nonbasic variables.
16.15 Suppose that we apply the simplex method to a given linear programming problem and obtain the following canonical tableau:
0 / 3 0 1 4
1
7
0 0 5
0
-3
1 0 6
0 2-a
0 0 δ
EXERCISES
375
For each of the following conditions, find the set of all parameter values
a, /?, 7, δ that satisfy the condition.
a. The problem has no solution because the objective function values are
unbounded.
b . The current basic feasible solution is optimal, and the corresponding
objective function value is 7.
c. The current basic feasible solution is not optimal, and the objective function value strictly decreases if we remove the first column of A from the
basis.
16.16 You are given a linear programming problem in standard form. Suppose that you use the two-phase simplex method and arrive at the following
canonical tableau in phase I:
"? 0 1 1 ? ? 0 6"
? 0 0 ? ? ? 1 a
? 1 0 ? ? ? 0 5
7 0 0 δ ? ? β 0
The variables a, /?, 7, and δ are unknowns to be determined. Those entries
marked with "?" are unspecified. The only thing you are told is that the value
of 7 is either 2 or —1.
a. Determine the values of a, /?, 7, and δ.
b . Does the given linear programming problem have a feasible solution? If
yes, find it. If not, explain why.
16.17 Suppose we are given a matrix A e Wrnxn and a vector b e R m such
that b > 0. We are interested in an algorithm that, given this A and 6, is
guaranteed to produce one of following two outputs: (1) If there exists x such
that Ax > 6, then the algorithm produces one such x. (2) If no such x exists,
then the algorithm produces an output to declare so.
Describe in detail how to design this algorithm based on the simplex
method.
376
SIMPLEX METHOD
16.18 Consider the following linear programming problem (attributed to
Beale—see [42, p. 43]):
minimize
subject to
3
1
— -X4 + 2OX5 — -XQ + 6x7
4
2
x\ + -£4 — 8x5 — #6 + 9x7 = 0
%2 + ~#4 - 12^5 - ~#6 + 3X7 = 0
X3 + X6 = 1
x i , . . . ,£7 > 0.
a. Apply the simplex algorithm to the problem using the rule that q is the
index corresponding to the most negative rq. (As usual, if more than one
index i minimizes yio/yiq, let p be the smallest such index.) Start with
Xi, X2, and X3 as initial basic variables. Notice that cycling occurs.
b . Repeat part a using Bland's rule for choosing q and p:
q — min{i : Vi < 0},
p = min{j : yj0/yjq
= min{yi0/yiq
: yiq > 0}}.
Note that Bland's rule for choosing p corresponds to our usual rule that
if more than one index i minimizes yio/yiq, we let p be the smallest such
index.
16.19 Consider a standard form LP problem. Suppose that we start with
an initial basic feasible solution x^ and apply one iteration of the simplex
algorithm to obtain x^\
We can express x^ in terms of x^ as
where ao minimizes φ(α) = f(x(°)+aS0')
is feasible.
a. Show that d (0)
over all a > 0 such that
x^+ad^
βλί(Α).
b . As usual, assume that the initial basis is the first m columns of A, and the
first iteration involves inserting aq into the basis, where q > m. Let the
qth column of the canonical augmented matrix be yq = [yiq,..., y m g ] T .
Express £Τ0^ in terms of yq.
16.20 Write a simple MATLAB function that implements the simplex algorithm. The inputs are c, A, 6, and v, where v is the vector of indices of basic
EXERCISES
377
columns. Assume that the augmented matrix [A, b] is already in canonical
form; that is, the i^th column of A is [ 0 , . . . , 1 , . . . , 0] T , where 1 occurs in the
ith position. The function should output the final solution and the vector
of indices of basic columns. Test the MATLAB function on the problem in
Example 16.2.
16.21 Write a MATLAB routine that implements the two-phase simplex
method. It may be useful to use the MATLAB function of Exercise 16.20.
Test the routine on the problem in Example 16.5.
16.22 Write a simple MATLAB function that implements the revised simplex
algorithm. The inputs are c, A, 6, v, and B " 1 , where v is the vector of
indices of basic columns; that is, the zth column of B is the t^th column of A.
The function should output the final solution, the vector of indices of basic
columns, and the final Β~λ. Test the MATLAB function on the problem in
Example 16.2.
16.23 Write a MATLAB routine that implements the two-phase revised simplex method. It may be useful to use the MATLAB function of Exercise 16.22.
Test the routine on the problem in Example 16.5.
CHAPTER 17
DUALITY
17.1
Dual Linear Programs
Associated with every linear programming problem is a corresponding dual
linear programming problem. The dual problem is constructed from the cost
and constraints of the original, or primal, problem. Being an LP problem,
the dual can be solved using the simplex method. However, as we shall see,
the solution to the dual can also be obtained from the solution of the primal
problem, and vice versa. Solving an LP problem via its dual may be simpler
in certain cases, and also often provides further insight into the nature of the
problem. In this chapter we study basic properties of duality and provide
an interpretive example of duality. Duality can be used to improve the performance of the simplex algorithm (leading to the primal-dual algorithm), as
well as to develop nonsimplex algorithms for solving LP problems (such as
Khachiyan's algorithm and Karmarkar's algorithm). We do not discuss this
aspect of duality further in this chapter. For an in-depth discussion of the
primal-dual method, as well as other aspects of duality, see, for example, [88].
For a description of Khachiyan's algorithm and Karmarkar's algorithm, see
Chapter 18.
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
379
380
DUALITY
Suppose that we are given a linear programming problem of the form
minimize
subject to
cTx
Ax > b
x>0.
We refer to the above as the primal problem. We define the corresponding
dual problem as
maximize
λ b
subject to
λτ A < cT
\>0.
We refer to the variable λ G Mm as the dual vector. Note that the cost vector
c in the primal has moved to the constraints in the dual. The vector b on the
right-hand side of Ax > b becomes part of the cost in the dual. Thus, the
roles of b and c are reversed. The form of duality defined above is called the
symmetric form of duality.
To define the dual of an arbitrary linear programming problem, we use the
following procedure. First, we convert the given linear programming problem
into an equivalent problem of the primal form shown above. Then, using the
symmetric form of duality, we construct the dual to the equivalent problem.
We call the resulting problem the dual of the original problem.
Note that based on the definition of duality above, the dual of the dual
problem is the primal problem. To see this, we first represent the dual problem
in the form
minimize
subject to
λ (—b)
λ τ (—A) > —cT
λ>0.
Therefore, by the symmetric form of duality, the dual to the above is
maximize
subject to
(—cT)x
(—A)x < —b
x>0.
Upon rewriting, we get the original primal problem.
Now consider an LP problem in standard form. This form has equality
constraints Ax = b. To formulate the corresponding dual problem, we first
convert the equality constraints into equivalent inequality constraints. Specifically, observe that Ax = b is equivalent to
Ax >b
-Ax > -b.
DUAL LINEAR PROGRAMS
381
Thus, the original problem with the equality constraints can be written in the
form
minimize
c x
subject to
A
-A
x >
x>0.
The LP problem above is in the form of the primal problem in the symmetric
form of duality. The corresponding dual is therefore
maximize
\uT vT]
6
-b
subject to
[uT vT]
A
-A
<c'
tx, v > 0
After simple manipulation the dual above can be represented as
maximize
(u — v)Tb
subject to
(u — v)TA
< cT
u,v > 0.
Let λ = u — v. Then, the dual problem becomes
maximize
λ b
subject to
XT A < c T .
Note that since X = u — v and n, v > 0, the dual vector λ is not restricted to
be nonnegative. We have now derived the dual for a primal in standard form.
The form of duality above is referred to as the asymmetric form of duality.
We summarize the forms of duality in Tables 17.1 and 17.2. Note that in
the asymmetric form of duality, the dual of the dual is also the primal. We
can show this by reversing the arguments we used to arrive at the asymmetric
form of duality and using the symmetric form of duality.
Recall that at the beginning of this chapter we defined the dual of an arbitrary linear programming problem by first transforming the problem into an
equivalent problem of the form of the primal in the symmetric form of duality.
We then derived the asymmetric form of duality based on the symmetric form.
In both forms of duality the dual of the dual is the primal. Therefore, we now
have four forms of primal-dual linear programming pairs: Each of the four
linear programming problems in Tables 17.1 and 17.2 is a primal in these four
pairs. So, given an arbitrary linear programming problem, we can obtain its
382
DUALITY
dual by converting the problem into any of the four problems in Tables 17.1
and 17.2.
Example 17.1 Suppose that we are given the given linear programming
problem
minimize
subject to
ex
Ax < b.
This problem is already close to the form of the dual in Table 17.2. In particular, let us rewrite the above as
maximize
x T (—c)
subject to
xT A
<b.
Its associated dual is then given by the primal in Table 17.2, which has the
form
minimize
subject to
b X
Α τ λ = —c
λ>0,
which can be written in the equivalent form
maximize
—λ b
subject to
λ τ Α = —cT
λ>0.
If we change the sign of the dual variable, we can rewrite the above in a more
"natural" form:
maximize
λ b
subject to
λ τ Λ = cT
λ<0.
■
Example 17.2 This example is adapted from [88]. Recall the diet problem
(see Example 15.2). We have n different types of food. Our goal is to create
the most economical diet and at the same time meet or exceed nutritional
requirements. Specifically, let α^ be the amount of the ith nutrient per unit
of the jth food, bi the amount of the ith nutrient required, 1 < i < m, Cj the
DUAL LINEAR PROGRAMS
Table 17.1
Symmetric Form of Duality
Primal
minimize cTx
subject to Ax>b
x>0
Table 17.2
383
Dual
maximize λ τ 6
subject to λ τ Α < c T
λ>0
Asymmetric Form of Duality
Primal
minimize cTx
subject to Ax = b
x>0
Dual
maximize λ τ &
subject to λ τ Λ < c T
cost per unit of the j t h food, and Xi the number of units of food i in the diet.
Then, the diet problem can be stated as follows:
minimize
C\X\ + c^xi + · · · + cnxn
subject to
anXi + «12^2 +
l·
^21^1 + 022^2 H
a
mlxl
+ am2x2
Oj\nxn > b\
1" 02n^n > &2
+ * * ' + Q"mn%n ^ ^m
X\,
...,
XJI
^_ U.
Now, consider a health food store that sells nutrient pills (all m types of
nutrients are available). Let λ^ be the price of a unit of the zth nutrient
in the form of nutrient pills. Suppose that we purchase nutrient pills from
the health food store at this price such that we exactly meet our nutritional
requirements. Then, XTb is the total revenue to the store. Note that since
prices are nonnegative, we have λ > 0. Consider now the task of substituting
nutrient pills for natural food. The cost of buying pills to create the nutritional
equivalent of the ith food synthetically is simply XIÜU H
h A m a m i. Because
C{ is the cost per unit of the ith food, if
^lali
"l· ' ' * H" Xmami
^ Q?
then the cost of the unit of the zth food made synthetically from nutrient pills
is less than or equal to the market price of a unit of the real food. Therefore,
384
DUALITY
for the health food store to be competitive, the following must hold:
λιαιι 4-
h A m a m i < c\
^ι^ΐη + · · · + A m a m n < cn.
The problem facing the health food store is to choose the prices λ ι , . . . , Xm
such that its revenue is maximized. This problem can be stated as
maximize
λ b
subject to
λ τ Α < cT
λ>0.
Note that this is simply the dual of the diet problem.
I
E x a m p l e 17.3 Consider the following linear programming problem:
maximize
2xi + 5^2 + X3
subject to
2a: 1 — X2 + 7x3 < 6
x\ + 3x2 + 4^3 < 9
3xi + 6x2 + %3 < 3
^1,^2,^3 > 0.
Find the corresponding dual problem and solve it.
We first write the primal problem in standard form by introducing slack
variables X4,X5,X6- This primal problem in standard form is
[c T , 0 T ] x
[A, I]x = b
x>0,
minimize
subject to
where x — [ x i , . . . , x 6 ] T and
"2 - 1
1 3
L3 6
7"
4 ,
1
"-2I
"β"
6 =
9 ,
3
c =
The corresponding dual problem (asymmetric form) is
maximize
λ b
subject to
λ τ [ Λ , I] < [c T , 0 T ] .
-5
-1
DUAL LINEAR PROGRAMS
385
Note that the constraints in the dual can be written as
λ τ Α < cT
λ<0.
To solve the dual problem above, we use the simplex method. For this, we
need to express the problem in standard form. We substitute λ by — λ and
introduce surplus variables to get
minimize
subject to
6λι + 9λ 2 + 3λ 3
2λι + λ 2 4- 3λ 3 - λ 4
- λ ι + 3λ 2 + 6Α3
- λ5
7λι + 4λ 2 + λ 3
=2
=5
- λ6 = 1
λι,...,λ6>0.
There is no obvious basic feasible solution. Thus, we use the two-phase simplex
method to solve the problem.
Phase I. We introduce artificial variables \γ, Ag, A9 and the artificial objective function A 7 + As + X9. The tableau for the artificial problem is
Ai
Cost
2
-1
7
0
λ2
1
3
4
0
λ3
3
6
1
0
λ4
-1
0
0
0
λ5
0
-1
0
0
λ6
0
0
-1
0
λ7
1
0
0
1
λ8
0
1
0
1
λ9
0
0
1
1
C
2
5
1
0
We start with an initial feasible solution and corresponding B~
Variable
λ7
λ8
λ9
B~l
1 0 0
0 1 0
0 0 1
y0
2
5
1
compute
rl = [0,0,0,0,0,0]- [8,8, 10, - 1 . ,-ι 5 1] = [ - 8 , - 8 , - 1 0 , 1 , 1 , 1 ]
=
[ri,r2,r3,r4,r5 ,re}.
Because r 3 is the most negative reduced cost coefficient, we bring the third
column into the basis. In this case, y3 = [3,6,1] T . We have
Variable
A7
A8
A9
B λ
y0 y3
1 0 0 2
3
0 1 0
5
6
0 0
1 1 1
386
DUALITY
By inspection, p = 1, so we pivot about the first element of the last column.
The updated tableau is
Variable
λ3
λ8
λ9
I/o
1
3
2
3
0
-2
_ι
3
0
1
i
I
We compute
D
_ _7
10
14
3 ' 3' ' ' 3
4
3'
[n,r2,r4,r5,r6,r7],
We bring the second column into the basis to get
Variable
1
B
1
3
0 0
-2 1 0
1
3 0 1
λ3
λ8
λ9
I/o
2
3
2/2
1
3
1
3
11
3
1
1
We update the tableau to get
Variable
4
11
21
11
1
11
λ3
λ8
λ2
1
11
3
11
3
11
0
1
0
2/o
7
11
10
11
1
11
We compute
D
74 _ 2 1
ll'"ll'
'
__3_ 32 14
=
11' 11' 11
[ri,U,r5,r6,r7,r9].
We bring the fourth column into the basis:
Variable
λ3
λ8
λ2
4
11
21
11
1
11
0
1
0
1
11
3
11
3
11
2/o
7
11
10
11
1
11
2/4
4
11
21
11
1
11
The updated tableau becomes
Variable
B
λ
y0
PROPERTIES OF DUAL PROBLEMS
We compute
387
r j = [0,0,0,1,1,1] = [ r i , r 5 , r 6 , r 7 , r 8 , r 9 ] .
Because all the reduced cost coefficients are nonnegative, we terminate phase I.
Phase II. We use the last tableau in phase I (where none of the artificial
variables are basic) as the initial tableau in phase II. Note that we now revert
back to the original cost of the dual problem in standard form. We compute
62 1 15
[ri,rb,r6]
"T'7'T
r
D
We bring the first column into the basis to obtain the augmented revised
tableau
Variable
B~l
y0
yl
λ3
4
oi
21
11
21
1
1_
21
oi
U
λ4
λ2
We update the tableau to get
p|
^
Variable
λ3
λ4
λι
We compute
D
3
oi
21
3
21
6
_2_
21
oi
B
0
w
1
25
oi
21
74
21
43
45
21
oi
Vo
43
19
43
^
17
oi
21
10
21
1
J^
21
oi
43
15
43
6_
J43
ΛΟ
ΛΟ
Art
- J43/IQ
36
AQ
43
24
43
J_
-L
43
ΛΟ
186 15 39
= fc2,r5,r6].
43 ' 4 3 ' 4 3
Because all the reduced cost coefficients are nonnegative, the current basic
feasible solution is optimal for the dual in standard form. Thus, an optimal
solution to the original dual problem is
1
0,
43
17.2
36 Ί τ
43
Properties of Dual Problems
In this section we present some basic results on dual linear programs, We
begin with the weak duality lemma.
Lemma 17.1 Weak Duality Lemma. Suppose that x and X are feasible
solutions to primal and dual LP problems, respectively (either in the symmetric
or asymmetric form). Then, cTx > X b.
□
388
DUALITY
Proof. We prove this lemma only for the asymmetric form of duality. The
proof for the symmetric form involves only a slight modification (see Exercise 17.1).
Because x and λ are feasible, we have Ax = 6, x > 0, and λ A < c T .
Postmultiplying both sides of the inequality λ A < c T by x > 0 yields
λ τ A x < cTx. But Ax = 6, hence λ τ 6 < cTx.
I
The weak duality lemma states that a feasible solution to either problem
yields a bound on the optimal cost of the other problem. The cost in the
dual is never above the cost in the primal. In particular, the optimal cost
of the dual is less than or equal to the optimal cost of the primal, that is,
"maximum < minimum." Hence, if the cost of one of the problems is unbounded, then the other problem has no feasible solution. In other words, if
"minimum = - c o " or "maximum = +oo," then the feasible set in the other
problem must be empty.
Example 17.4 Consider the problem
minimize x
subject to x < 1,
which is clearly unbounded. By Example 17.1, the dual is
maximize λ
subject to λ = 1
λ<0,
which is clearly infeasible.
I
It follows from the weak duality lemma that if we are given feasible primal
and dual solutions with equal cost, then these solutions must be optimal in
their respective problems.
Theorem 17.1 Suppose that XQ and XQ are feasible solutions to the primal
and dual, respectively (either in symmetric or asymmetric form). If cTXo =
X0 b, then XQ and XQ are optimal solutions to their respective problems.
□
Proof. Let x be an arbitrary feasible solution to the primal problem. Because
λο is a feasible solution to the dual, by the weak duality lemma, cTx > X0 b.
So, if cTXo = λ^&, then cTXo = X0b < cTx. Hence, Xo is optimal for the
primal.
On the other hand, let λ be an arbitrary feasible solution to the dual
problem. Because Xo is a feasible solution to the primal, by the weak duality
lemma, CTXQ > XTb. Therefore, if cTxo = λ^&, then λ τ 6 < cTxo = λ ^ 6 .
Hence, λο is optimal for the dual.
I
We can interpret Theorem 17.1 as follows. The primal seeks to minimize
its cost, and the dual seeks to maximize its cost. Because the weak duality
PROPERTIES OF DUAL PROBLEMS
389
lemma states that "maximum < minimum," each problem "seeks to reach
the other." When their costs are equal for a pair of feasible solutions, both
solutions are optimal, and we have "maximum = minimum."
It turns out that the converse of Theorem 17.1 is also true, that is, "maximum = minimum" always holds. In fact, we can prove an even stronger
result, known as the duality theorem.
Theorem 17.2 Duality Theorem. If the primal problem (either in symmetric or asymmetric form) has an optimal solution, then so does the dual,
and the optimal values of their respective objective functions are equal.
□
Proof We first prove the result for the asymmetric form of duality. Assume
that the primal has an optimal solution. Then, by the fundamental theorem
of LP, there exists an optimal basic feasible solution. As is our usual notation,
let B be the matrix of the corresponding m basic columns, D the matrix of the
n — m nonbasic columns, cB the vector of elements of c corresponding to basic
variables, CD the vector of elements of c corresponding to nonbasic variables,
and VD the vector of reduced cost coefficients. Then, by Theorem 16.2,
=cJ)-clB~1D>0T.
rl
Hence,
Define
Then,
cTBBlD
< cj.
\T =
clB-\
cTBB~lD
= \TD
< cj.
We claim that λ is a feasible solution to the dual. To see this, assume for
convenience (and without loss of generality) that the basic columns are the
first m columns of A. Then,
XTA
= XT[B,D]
= [cl,XTD]
< [cTB,cl\ = c T .
Hence, XT A < cT and thus λ τ = CßB'1 is feasible.
We claim that λ is also an optimal feasible solution to the dual. To see
this, note that
XTb = clB~1b = clxB.
Thus, by Theorem 17.1, λ is optimal.
We now prove the symmetric case. First, we convert the primal problem
for the symmetric form into the equivalent standard form by adding surplus
390
DUALITY
variables:
minimize
[c T ,0 T ]
subject to
[A, — I]
= b
>0.
Note that x is optimal for the original primal problem if and only if [x T , (Ax—
6 ) T ] T is optimal for the primal in standard form. The dual to the primal in
standard form is equivalent to the dual to the original primal in symmetric
form. Therefore, the result above for the asymmetric case applies also to the
symmetric case.
This completes the proof.
I
Example 17.5 Recall Example 17.2, where we formulated the dual of the
diet problem. Prom the duality theorem, the maximum revenue for the health
food store is the same as the minimum cost of a diet that satisfies all of the
nutritional requirements; that is, cTx = XTb.
I
Consider a primal-dual pair in asymmetric form. Suppose that we solve the
primal problem using the simplex method. The proof of the duality theorem
suggests a way of obtaining an optimal solution to the dual by using the last
row of the final simplex tableau for the primal. First, we write the tableau
for the primal problem:
A
b
cT
0
B
D
-D
Suppose that the matrix B is the basis for an optimal basic feasible solution.
Then, the final simplex tableau is
I
0'
BlD
D
B-'b
-cTBB-lb
l
where r ^D — c
= b ~ c 3B-1 D. In the proof of the duality theorem we have
shown that λ = CQB
is an optimal solution to the dual. The vector λ
can be obtained from the final tableau above. Specifically, if rankZ) = m,
then we can solve for λ using the vector r ^ , via the equation
XTD =
cl-rl.
PROPERTIES OF DUAL PROBLEMS
391
Of course, it may turn out that rank D < m. In this case as we now show,
we have additional linear equations that allow us to solve for λ. To this end,
recall that λ B = c j . Therefore, if we define rT = [0 T , r J ] , then combining
the equations XT D = cj
rj) and \T B
yields
The vector λ may be easy to obtain from the equation λ D = c]D — r]D
if D takes certain special forms. In particular, this is the case if D has an
mxm identity matrix embedded in it; that is, by rearranging the positions of
the columns of D, if necessary, D has the form D = [J m , G], where G is an
rax (n — 2ra) matrix. In this case we can write the equation λ D = Cp—rJ)
as
[XT,XTG] =
{cJ,cl}-[rJ,rl].
Hence, λ is given by
In other words, the solution to the dual is obtained by subtracting the reduced costs coefficients corresponding to the identity matrix in D from the
corresponding elements in the vector c (i.e., c/).
For example, if we have a problem where we introduced slack variables, and
the basic variables for the optimal basic feasible solution do not include any
of the slack variables, then the matrix D has an identity matrix embedded in
it. In addition, in this case we have c/ = 0. Therefore, λ = — τι is an optimal
solution to the dual.
E x a m p l e 17.6 In Example 17.3, the tableau for the primal in standard form
IS
Oi
ei2
15
43
74
43
19
43
24
7?
43
0
0
1
0
03
05
04
Ct6
b
2 - 1 7 1
0
0 6
1
1 3
4
0
0 9
0
3
6
1 0
1 3
0
cT - 2 - 5 - 1 0
0 0
If we now solve the problem using the simplex method, we get the following
final simplex tableau:
r
1
0
0
0
6
43
21
43
1
43
1
43
0
1
0
0
1
43
25
43
7
43
36
43
39
43
186
43
15
43
114
43
We can now find the solution of the dual from the above simplex tableau using
the equation XT D = c j — rT .
[λι,λ2,λ3]
"2 i οΊ
24
1 0 0 = [-2,0,0]43' 43'43
3 0 1
392
DUALITY
Solving the above, we get
43
36
43
,0,
which agrees with our solution in Example 17.3.
I
We now summarize our discussion relating the solutions of the primal and
dual problems. If one has unbounded objective function values, then the
other has no feasible solution. If one has an optimal feasible solution, then so
does the other (and their objective function values are equal). One final case
remains: What can we say if one (the primal, say) has no feasible solution?
In this case clearly the other (the dual, say) cannot have an optimal solution.
However, is it necessarily the case that the dual is unbounded? The answer is
no: If one of the problems has no feasible solution, then the other may or may
not have a feasible solution. The following example shows that there exists a
primal-dual pair of problems for which both have no feasible solution.
Example 17.7 Consider the primal problem
minimize
[1, — 2]x
subject to
1
-1
-1
1
x>
' 2"
-1
x > 0.
The problem has no feasible solution, because the constraints require that
#i — #2 > 2 and X\ — x 2 < 1. Based on symmetric duality, the dual is
maximize
λ
"2 "
-1
subject to
λ
1
-1
-l"
1 <[l,-2]
λ>0.
The dual also has no feasible solution, because the constraints require that
λι - λ 2 < 1 and λχ - λ 2 > 2.
I
We end this chapter by presenting the following theorem, which describes
an alternative form of the relationship between the optimal solutions to the
primal and dual problems.
Theorem 17.3 Complementary
Slackness Condition.
The feasible solutions x and X to a dual pair of problems (either in symmetric or asymmetric
form) are optimal if and only if:
PROPERTIES OF DUAL PROBLEMS
1. (c T - XTA)x
393
= 0.
T
2. \ {Ax-b)=0.
D
Proof. We first prove the result for the asymmetric case. Note that condition 2
holds trivially for this case. Therefore, we consider only condition 1.
=>: If the two solutions are optimal, then by Theorem 17.2, cTx = X b.
Because Ax = 6, we also have (c T — λ Α)χ = 0.
<=: If (c T - XTA)x = 0, then cTx = XTAx = XTb. Therefore, by
Theorem 17.1, x and λ are optimal.
We now prove the result for the symmetric case.
=>: We first show condition 1. If the two solutions are optimal, then by
Theorem 17.2, cTx = X b. Because Ax > b and λ > 0, we have
(c T - XTA)x
= cTx - XTAx = XTb - XT Ax = XT (b - Ax) < 0.
On the other hand, since X A < cT and x > 0, we have (c T — λ Α)χ > 0.
Hence, (c T — λ A)x — 0. To show condition 2, note that since Ax > b and
λ > 0, we have λ (Ax — b) > 0. On the other hand, since XTA < cT and
x > 0, we have XT{Ax - b) = ( λ τ A - cT)x < 0.
<=: Combining conditions 1 and 2, we get cTx = X Ax = X b. Hence,
by Theorem 17.1, x and λ are optimal.
I
Note that if x and λ are feasible solutions for the dual pair of problems,
we can write condition 1, that is, (c T — λ A)x = 0, as UX{ > 0 implies that
λ di = a, i = 1 , . . . ,n," that is, for any component of x that is positive,
the corresponding constraint for the dual must be an equality at λ. Also,
observe that the statement uXi > 0 implies that λ τ α * = Q " is equivalent to
"λ a,i < Ci implies that Xi = 0." A similar representation can be written for
condition 2.
Consider the asymmetric form of duality. Recall that for the case of an
optimal basic feasible solution x, r T = c T — λ τ A is the vector of reduced cost
coefficients. Therefore, in this case, the complementary slackness condition
can be written as rTx = 0.
Example 17.8 Suppose that you have 26 dollars and you wish to purchase
some gold. You have a choice of four vendors, with prices (in dollars per
ounce) of 1/2, 1, 1/7, and 1/4, respectively. You wish to spend your entire
26 dollars by purchasing gold from these four vendors, where xi is the dollars
you spend on vendor i, i = 1,2,3,4.
a. Formulate the linear programming problem (in standard form) that reflects your desire to obtain the maximum weight in gold.
b . Write down the dual of the linear programming problem in part a, and
find the solution to the dual.
394
DUALITY
c. Use the complementary slackness condition together with part b to find
the optimal values of xi,..., #4.
Solution:
a. The corresponding linear programming problem is
minimize
subject to
— (2#i + #2 + 7#3 + ^4)
x\ + X2 + £3 -f £4 = 26
>
X\,X2,XZ,X4
0.
b . The dual problem is
maximize
subject to
26λ
λ < —2
λ<-1
λ<-7
λ<-4.
The solution is clearly λ = — 7. (Note: It is equally valid to have a dual
problem with variable λ' = — λ.)
c. By the complementary slackness condition, we know that if we can find
a vector x that is feasible in the primal and satisfies (—[2,1,7,4] —
(—7)[l,l,l,l])x = 0, then this x is optimal in the primal (original) problem. We can rewrite the conditions above as
[1,1,1,1]« = 26,
x>0,
[5,6,0,3]® = 0.
By x > 0 and [5,6,0,3]a? = 0, we conclude that x\ = X2 = XA = 0, and
by [1,1,1, l]x = 26 we then conclude that x = [0,0,26,0] T .
|
EXERCISES
17.1 Prove the weak duality lemma for the symmetric form of duality.
17.2 Find the dual of the optimization problem in Exercise 15.8.
17.3 Consider the following linear program:
maximize
subject to
2#i + 3α?2
x\+ 2x2 < 4
2a: 1 + X2 < 5
X\,X2 > 0.
EXERCISES
395
a. Use the simplex method to solve the problem.
b . Write down the dual of the linear program and solve the dual.
17.4 Consider the linear program
minimize
4χχ + 3x2
subject to
5xi -\- X2 > H
2xi + x2 > 8
xi + 2x 2 > 7
X\,X2 > 0.
Write down the corresponding dual problem and find the solution to the dual.
(Compare this problem with the one in Exercise 16.12, part a.)
17.5 Consider the following primal problem:
maximize
subject to
x\ + 2^2
— 2xi + x2 + X3
— X\ + 2x 2
=2
4- X4
=7
+ X5 = 3
x\
Xi > 0 ,
i = 1,2,3,4,5.
a. Construct the dual problem corresponding to the primal problem above.
b . It is known that the solution to the primal above is x* = [3,5,3,0,0] T .
Find the solution to the dual.
17.6 Consider the linear programming problem
minimize
subject to
cTx
Ax < b.
a. Find the dual to this problem.
b . Suppose that b = 0 and there exists a vector y > 0 such that yT A+cT =
0 . Does this problem have an optimal feasible solution? If yes, find it.
If no, explain why not. Give complete explanations.
17.7 Convert the following optimization problem into a linear programming
problem and solve it:
maximize
" Fil - \χ2\
subject to
1
0
1
-1
\χ3\
X\
X2
_X3_
—
2
1
L J
396
DUALITY
Then construct its dual program and solve it.
Hint: Introduce two sets of nonnegative variables: x^ > 0, x~ > 0. Then
represent the optimization problem using the variables above. Note that only
one xf and x^ can be nonzero at a time. If Xi > 0 then xf = Xi and x~ = 0.
On the other hand, if Xi < 0 then xf = 0 and Xi = —x~. See Exercise 16.6.
17.8 Consider the linear program
minimize
subject to
x\ Λ
h xn,
# i , . . . , xn G R
a\X\ H
h anxn = 1
a ? i , . . . , xn t> U,
where 0 < a\ < a,2 < · · · < an.
a. Write down the dual to the problem and find a solution to the dual in
terms of α ι , . . . , a n .
b . State the duality theorem and use it to find a solution to the primal
problem above.
c. Suppose that we apply the simplex algorithm to the primal problem.
Show that if we start at a nonoptimal initial basic feasible solution, the
algorithm terminates in one step if and only if we use the rule where the
next nonbasic column to enter the basis is the one with the most negative
reduced cost coefficient.
17.9 You are given the following linear programming problem:
maximize
subject to
c\X\ Λ
X\ Λ
h cnxn
V xn = 1
X\, . . . , Xn ^_ U,
where c i , . . . , cn G R are constants.
a. Write down the dual linear program for the primal problem.
b . Suppose you know that C4 > Q for all i φ 4. Use this information to
solve the dual.
c. Use part b to solve the linear programming problem.
17.10 Consider the linear programming problem
maximize
subject to
cTx
Ax < 0
x > 0,
EXERCISES
397
where c = [ 1 , 1 , . . . , 1] T . Assume that the problem has a solution.
a. Write down the dual of this problem.
b . Find the solution to the problem.
c. What can you say about the constraint set for the problem?
17.11 Consider a given linear programming problem in standard form (written in the usual notation).
a. Write down the associated artificial problem for the problem (used in the
two-phase method).
b . Write down the dual to the artificial problem from part a.
c. Prove that if the original linear programming problem has a feasible solution, then the dual problem in part b has an optimal feasible solution.
17.12 Consider a pair of primal and dual linear programming problems
(either in symmetric or asymmetric form). Identify which of the following
situations are possible (depending on the particular primal-dual pair) and
which are impossible (regardless of the primal-dual pair). In each case, justify
your answer (citing results such as the weak duality lemma and the duality
theorem whenever needed).
a. The primal has a feasible solution, and the dual has no feasible solution.
b. The primal has an optimal feasible solution, and the dual has no optimal
feasible solution.
c. The primal has a feasible solution but no optimal feasible solution, and
the dual has an optimal feasible solution.
17.13 Consider an LP problem in standard form. Suppose that x is a feasible
solution to the problem. Show that if there exist λ and μ such that
Ατ\
+μ =ο
μτχ
=0
μ>0,
then x is an optimal feasible solution to the problem and λ is an optimal
feasible solution to the dual. The conditions above, called the Karush-KuhnTucker optimality conditions for LP, are discussed in detail in Chapters 21
and 22.
398
DUALITY
17.14 Consider the linear program
cTx,
Ax < 6,
maximize
subject to
where c G Mn, b G R m , and A e Rmxn.
Use the symmetric form of duality
to derive the dual of this linear program and show that the constraint in the
dual involving A can be written as an equality constraint.
Hint: Write x — u — v^ with u, v > 0.
17.15 Consider the linear program
minimize
subject to
x\ + x2
x\ + 2x2 > 3
2#i + X2 > 3
£ l , £ 2 > 0.
The solution to the problem is [1,1] T (see Exercise 16.11). Write down the
dual to the problem, solve the dual, and verify that the duality theorem holds.
17.16 Consider the problem
minimize
subject to
cTx,
x > 0.
xGRn
For this problem we have the following theorem.
Theorem: A solution to the foregoing problem exists if and only if c > 0.
Moreover, if a solution exists, 0 is a solution.
Use the duality theorem to prove this theorem (see also Exercise 22.15).
17.17 Let A be a given matrix and b a given vector. Show that there exists a
vector x such that Ax > b and x > 0 if and only if for any vector y satisfying
ATy < 0 and y > 0, we have bTy < 0.
17.18 Let A be a given matrix and b a given vector. We wish to prove the
following result: There exists a vector x such that Ax = b and x > 0 if and
only if for any given vector y satisfying ATy < 0, we have b y < 0. This
result is known as Farkas's transposition theorem. Our argument is based on
duality theory, consisting of the following parts.
a. Consider the primal linear program
minimize
subject to
0Tx
Ax = b
x > 0,
EXERCISES
399
Write down the dual of this problem using the notation y for the dual
variable.
b . Show that the feasible set of the dual problem is guaranteed to be
nonempty.
Hint: Think about an obvious feasible point.
c. Suppose that for any y satisfying ATy < 0, we have b y < 0. In this
case what can you say about whether or not the dual has an optimal
feasible solution?
Hint: Think about the obvious feasible point in part b.
d. Suppose that for any y satisfying ATy < 0, we have b y < 0. Use parts
b and c to show that there exists x such that Ax = b and x > 0. (This
proves one direction of Farkas's transposition theorem.)
e. Suppose that x satisfies Ax = b and x > 0. Let y be an arbitrary
vector satisfying A y < 0. Show that b y < 0. (This proves the other
direction of Farkas's transposition theorem.)
17.19 Let A be a given matrix and b a given vector. Show that there exists
a vector x such that Ax < b if and only if for any given vector y satisfying
ATy = 0 and y > 0, we have b y > 0. This result is known as Gale's
transposition theorem.
17.20 Let A be a given matrix. Show that there exists a vector x such that
Ax < 0 if and only if for any given vector y satisfying ATy = 0 and y > 0,
we have y = 0 (i.e., y — 0 is the only vector satisfying ATy = 0 and y > 0).
This result is known as Gordan's transposition theorem.
17.21 Let P G R n x n be a matrix with the property that each element is in
the real interval [0,1], and the sum of the elements of each row is equal to 1;
call such a matrix a stochastic matrix. Now consider a vector x > 0 such that
xTe = 1, where e— [ 1 , . . . , 1] T ; call such a vector x a probability vector.
We wish to prove the following result: For any stochastic matrix P , there
exists a probability vector x such that xTP = xT. Although this is a key
result in probability theory (under the topic of Markov chains), our argument
is based on duality theory (for linear programming), consisting of the following
parts.
a. Consider the primal linear program:
maximize
subject to
xTe
xTP
x>0.
= xT
400
DUALITY
Write down the dual of this problem.
b . Show that the dual is not feasible (i.e., there does not exist a feasible
solution to the dual).
Hint: Derive a contradiction based on Py > y; think about the largest
element of y (call it yi).
c. Is the primal feasible? What can you deduce about whether or not the
primal is unbounded?
d. Use part c to deduce the desired result: that there exists a vector x > 0
such that xT P = xT and xTe = 1.
17.22 Suppose that you are presented with a "black box" that implements
a function φ defined as follows: Given positive integers m and n, a matrix
A G R m x n , and a vector b G R m , the value of </>(m, n, A, b) is a vector
x = </>(ra, n, A, b) that satisfies Ax > 6, if such a vector exists. In other
words, the black box solves a linear feasibility problem.
Now, given A G R m x n , b G R m , and c G R n , consider the linear programming problem
minimize
subject to
cTx
Ax > b
x>0.
Express a solution to this problem in terms of the function φ given above. In
other words, show how we can use the black box to solve this linear programming problem.
Hint: Find the appropriate inputs to the black box such that the output immediately gives a solution to the linear programming problem. You should
use the black box only once.
17.23 This exercise illustrates the use of duality to compute the sensitivity
of the optimal objective function value with respect to perturbations in the
constraint.
Consider a primal linear programming problem and its dual (in either symmetric or asymmetric form). Let us view the b vector in the primal as a
parameter that we can vary, and that we wish to calculate the change in the
optimal objective function value if we perturb 6 by a small perturbation Δ6
(i.e., replace b by b + Δ6).
a. To make the problem precise, let z(b) be the optimal value of the primal
objective function. Let λ denote the corresponding optimal dual vector.
Calculate the gradient of z at b: Vz(b). Write the answer in terms of
λ. You may assume that the optimal dual vector remains fixed in a
neighborhood of 6; but if you do, you must explain why this assumption
EXERCISES
401
is reasonable.
Hint: Use the duality theorem to see how z(b) depends on b.
b . Suppose that the first component of the optimal dual vector is λι = 3.
Now suppose that we increase b\ by a very small amount Ab\. Determine
the amount by which the optimal objective function value will change.
17.24 Consider the quadratic programming problem
minimize
subject to
-x x
2
Ax < 6,
where A G M m x n and b G M m . Call this problem the primal problem.
Consider the associated dual quadratic programming problem
maximize
—-yT(AAT)y
subject to
y > 0.
—b y
Let / i and fa be the objective functions of the primal and dual, respectively.
a. State and prove a weak duality lemma in this setting.
b . Show that if XQ and y0 are feasible points in the primal and dual, and
/i(sco) — Λίί/ο)? t n e n x0 and y0 are optimal solutions to the primal and
dual, respectively.
Hint: The techniques used in the linear programming duality results are applicable in this exercise.
CHAPTER 18
NONSIMPLEX METHODS
18.1
Introduction
In previous chapters we studied the simplex method and its variant, the revised simplex method, for solving linear programming problems. The method
remains widely used in practice for solving LP problems. However, the amount
of time required to compute a solution using the simplex method grows rapidly
as the number of components n of the variable x £ R n increases. Specifically,
it turns out that the relationship between the required amount of time for the
algorithm to find a solution and the size n of x is exponential in the worst
case. An example of an LP problem for which this relationship is evident
was devised by Klee and Minty in 1972 [76]. Below, we give a version of the
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
403
404
NONSIMPLEX METHODS
Klee-Minty example, taken from [9]. Let n be given. Let
c=[10n_1,10"-2,...,101,l]T,
& = [i,io2,io4,...,io2(n-1)]T,
1
2 x 101
2 x 102
0
1
2 x 101
0
0
1
2 x lO71"1
2 x 10 n " 2
···
0
0
0
2 x 101
1
Consider the following LP problem:
maximize
subject to
cTx
Ax < b
x>0.
The simplex algorithm applied to the LP problem above requires 2 n — 1 steps
to find the solution. Clearly, in this example the relationship between the
required amount of time for the simplex algorithm to find a solution and the
size n of the variable x is exponential. This relationship is also called the
complexity of the algorithm. The simplex algorithm is therefore said to have
exponential complexity. The complexity of the simplex algorithm is also often
written as 0 ( 2 n - 1).
Naturally, we would expect that any algorithm that solves LP problems
would have the property that the time required to arrive at a solution increases with the size n of the variable x. However, the issue at hand is the
rate at which this increase occurs. As we have seen above, the simplex algorithm has the property that this rate of increase is exponential. For a number
of years, computer scientists have distinguished between exponential complexity and polynomial complexity. If an algorithm for solving LP problems
has polynomial complexity, then the time required to obtain the solution is
bounded by a polynomial in n. Obviously, polynomial complexity is more desirable than exponential complexity. Therefore, the existence of an algorithm
for solving LP problems with polynomial complexity is an important issue.
This issue was partially resolved in 1979 by Khachiyan (also transliterated
as Hacijan) [74], who proposed an algorithm that has a complexity 0(n4L),
where, roughly speaking, L represents the number of bits used in the computations. The reason that we consider Khachiyan's algorithm (also called the
ellipsoid algorithm) as only a partial resolution of this issue is that the complexity depends on L, which implies that the time required to solve a given
LP problem increases with the required accuracy of the computations. The
existence of a method for solving LP problems with a polynomial complexity
bound based only on the size of the variable n (and possibly the number of
constraints) remains a difficult open problem [55]. In any case, computational
KHACHIYAN'S METHOD
405
experience with Khachiyan's algorithm has shown that it is not a practical
alternative to the simplex method [14]. The theoretical complexity advantage of Khachiyan's method relative to the simplex method remains to be
demonstrated in practice.
Another nonsimplex algorithm for solving LP problems was proposed in
1984 by Karmarkar [71]. Karmarkar's algorithm has a complexity of 0(n 3 , 5 L),
which is lower than that of Khachiyan's algorithm. The algorithm is superior
to the simplex algorithm from a complexity viewpoint, but has its drawbacks. Improved methods along similar lines, called interior-point methods,
have received considerable interest since Karmarkar's original paper. Wellimplemented versions of these methods are very efficient, especially when the
problem involves a large number of variables [55].
This chapter is devoted to a discussion of nonsimplex methods for solving LP problems. In the next section we discuss some ideas underlying
Khachiyan's algorithm. We then present Karmarkar's algorithm in the section
to follow.
18.2
Khachiyan's Method
Our description of the Khachiyan's algorithm is based on [8] and [9]. The
method relies on the concept of duality (see Chapter 17). Our exposition of
Khachiyan's algorithm is geared toward a basic understanding of the method.
For a detailed rigorous treatment of the method, we refer the reader to [101].
Consider the (primal) linear programming problem
minimize
subject to
cTx
Ax > b
x>0.
We write the corresponding dual problem,
maximize
λ b
subject to
λ τ Α < cT
λ>0.
Recall that the two LP problems above constitute the symmetric form of
duality. From Theorem 17.1, if x and λ are feasible solutions to the primal
and dual problems, respectively, and cTx = λ 6, then x and λ are optimal
solutions to their respective problems. Using this result, we see that to solve
the primal problem it is enough to find a vector [ χ τ , λ ] τ that satisfies the
406
NONSIMPLEX METHODS
following set of relations:
crx = 6 τ λ ,
Ax > b,
ATX < c,
x>0,
λ>0.
Note that the equality cTx = b X is equivalent to the two inequalities
cTx - bTX < 0,
-cTx
+ bTX < 0.
' °1
-6'1
Taking this into account, we can represent
the previous set of relations as
T
&
0
0
X
-b
<
0
X
0
-A
AT
c
-In
_
0_
""·* m.
0
0
Therefore, we have reduced the problem of finding an optimal solution to
the primal-dual pair into one of finding a vector [ccT, λ ] τ that satisfies the
system of inequalities above. In other words, if we can find a vector that
satisfies the system of inequalities, then this vector gives an optimal solution
to the primal-dual pair. On the other hand, if there does not exist a vector
satisfying the system of inequalities, then the primal-dual pair has no optimal
feasible solution. In the subsequent discussion, we simply represent the system
of inequalities as
Pz<<7,
where
P =
cT
-cT
-A
-In
0
0
-b1
bT
0
0
AT
-In
Z =
X
X
Q =
' 0"
0
-b
0
c
. 0.
In our discussion of Khachiyan's algorithm, we will not be using these forms
of P , ςτ, and z specifically; we simply treat Pz < q as a generic matrix
inequality, with P , g, and z as generic entities. Let r and s be the sizes of q
and z, respectively; that is, P G R r x s , z G R s , and q e Mr.
KHACHIYAN'S METHOD
407
Khachiyan's method solves the LP problem by first determining if there
exists a vector z that satisfies the inequality Pz < q; that is, the algorithm
decides if the system of linear inequalities above is consistent If the system
is consistent, then the algorithm finds a vector z satisfying the system. In
the following we refer to any vector satisfying the system as a solution to the
system. We assume that the entries in P and q are all rational numbers. This
is not a restriction in practice, since any representation of our LP problem
on a digital computer will involve only rational numbers. In fact, we assume
further that the entries in P and q are all integers. We can do this without
loss of generality since we can always multiply both sides of the inequality
Pz < q by a sufficiently large number to get only integer entries on both
sides.
Before discussing Khachiyan's algorithm, we introduce the idea of an ellipsoid. To this end, let z £ Rs be a given vector and let Q be an s x s
nonsingular matrix. Then, the ellipsoid associated with Q centered at z is
defined as the set
EQ(z) = {z + Qy : y e Rs,\\y\\ < l } .
The main idea underlying Khachiyan's algorithm is as follows. Khachiyan's
algorithm is an iterative procedure, where at each iteration we update a vector
z^ and a matrix Qk. Associated with z^ and Qk is an ellipsoid
EQk{z^).
At each step of the algorithm, the associated ellipsoid contains a solution to
the given system of linear inequalities. The algorithm updates z^ and Qk
in such a way that the ellipsoid at the next step is "smaller" than that of
the current step, but at the same time is guaranteed to contain a solution
to the given system of inequalities, if one exists. If we find that the current
point z^ satisfies Ρζ^ < ςτ, then we terminate the algorithm and conclude
that z^ is a solution. Otherwise, we continue to iterate. The algorithm
has a fixed prespecified maximum number of iterations N to be performed,
where N is a number that depends on L and s. Note that we are not free
to choose N—it is computed using a formula that uses the values of L and
s. The constant L is itself a quantity that we have to compute beforehand
using a formula that involves P and q. When we have completed N iterations
without finding a solution in an earlier step, we terminate the algorithm. The
associated ellipsoid will then have shrunk to the extent that it is smaller than
the precision of computation. At this stage, we will either discover a solution
inside the ellipsoid, if indeed a solution exists, or we will find no solution inside
the ellipsoid, in which case we conclude that no solution exists.
As we can see from the description above, Khachiyan's approach is a radical
departure from the classical simplex method for solving LP problems. The
method has attracted a lot of attention, and many studies have been devoted
to it. However, as we pointed out earlier, the algorithm is of little practical
value for solving real-world LP problems. Therefore, we do not delve any
further into the details of Khachiyan's algorithm. We refer the interested
reader to [101].
408
NONSIMPLEX METHODS
Despite its practical drawbacks, Khachiyan's method has inspired other
researchers to pursue the development of computationally efficient algorithms
for solving LP problems with polynomial complexity. One such algorithm is
attributed to Karmarkar, which we discuss in Section 18.4.
18.3
Affine Scaling Method
Basic Algorithm
In this section we describe a simple algorithm, called the affine scaling method,
for solving linear programming problems. This description is to prepare the
reader for our discussion of Karmarkar's method in the next section. The affine
scaling method is a an interior-point method. Such methods differ fundamentally from the classical simplex method in one main respect: An interior-point
method starts inside the feasible set and moves within it toward an optimal
vertex. In contrast, the simplex method jumps from vertex to vertex of the
feasible set seeking an optimal vertex.
To begin our description of the affine scaling method, consider the LP
problem
minimize
subject to
c x
Ax = b
x>0.
Note that the feasibility constraints have two parts: Ax = b and x > 0.
Suppose that we have a feasible point x^ that is strictly interior (by strictly
interior we mean that all of the components of x^ are strictly positive). We
wish to find a new point x^ by searching in a direction d^0' that decreases
the objective function. In other words, we set
where ao is a step size. In the gradient method (Chapter 8) we used the
negative gradient of the objective function for the search direction. For the
LP problem, the negative gradient of the objective function is —c. However,
if we set S ' = — c, the point x^ may not lie inside the feasible set. For
a^1) to lie inside the feasible set, it is necessary that S0' be a vector in the
nullspace of A. Indeed, because x^ is feasible, we have Ax^ = b. We also
require that Ax^ = b. Combining these two equations yields
A (χ™ - *(°>) = a0Ad<°> = 0.
To choose a direction d^ that lies in the nullspace of A but is still "close" to
—c, we orthogonally project — c onto the nullspace of A and take the resulting
AFFINE SCALING METHOD
Figure 18.1
409
Results of projected gradient step from center and noncenter points.
projection as S°\ The orthogonal projection of any vector onto the nullspace
of A involves multiplication by the following matrix P , called the orthogonal
projector (see Section 3.3 and Example 12.5):
P =
In-AT(AAT)-1A.
We set S0' to be in the direction of the orthogonal projection of —c onto the
nullspace of A:
d<°> = -Pc.
It is easy to check that APc = 0 and hence Ax^ = b. In summary, given a
feasible point x^°\ we find a new feasible point x^ using
x™ = *«>) _
aopc,
where the choice of the step size ao is discussed later in the section. The
choice of x^ above can be viewed as one iteration of a projected gradient
algorithm, discussed in Section 23.3.
We now make the observation that the point x^ should be chosen close to
the center of the feasible set. Figure 18.1 illustrates this observation. Comparing the center and noncenter starting points in the figure, we can see that
if we start at the center of the feasible set, we can take a larger step in the
search direction. This larger step from the center point should yield a lowercost value for the new point compared with the step originating from the
noncenter point.
Suppose that we are given a point x^ that is feasible but is not a center
point. We can transform the point to the center by applying what is called
an affine scaling. For simplicity, suppose that A = [ 1 , 1 , . . . , l]/n and b = [1].
It is easy to see that the center of this feasible set is the point e = [ 1 , . . . , 1] T .
To transform x^ to e, we use the affme-scaling transformation
e = £>ö1x(°))
410
NONSIMPLEX METHODS
where DQ is a diagonal matrix whose diagonal entries are the components of
the vector x^:
,(o)
D0=dmg[x?\...,x^}
=
,(o)
Note that DQ is invertible because we assumed that x^ is strictly interior.
For general A and b we will still use the same amne-scaling transformation
as above. In general, we may not be at precisely the center of the feasible
set, but we hope that the transformed point will be "close" to the center. At
least the point e is equidistant from the boundaries of the positive orthant
{x : x > 0}.
Once the starting point is at (or close to) the center of the feasible set after performing the amne-scaling transformation, we can proceed as described
before. Because we have transformed the original vector x^ via premultiplication by DQ1, effectively changing the coordinate system, we also need to
represent the original LP problem in the new coordinates. Specifically, the
LP problem in the transformed coordinates takes the form
minimize CQX
subject to
ÄQX — b
x > 0,
where
c 0 = D0c,
AQ
=
AD0.
In the new (x) coordinate system we construct the orthogonal projector
P0 = i n - Ä0 (Ä 0 Ä 0 ) _ 1 Ä 0
and set d
to be in the direction of the orthogonal projection of — Co onto
the nullspace of Äo:
Then, compute x^
a (0) = -Poco.
using
x^
= »<°> -
a0P0co,
where ä^0) = DQ1X^°\
TO obtain a point in the original coordinates, we
perform the transformation
*W =
D0x{1).
AFFINE SCALING METHOD
The procedure above takes a point x^
This procedure can be represented as
and generates a new point
411
x^\
x( 1 )=x(°)+a 0 d (0) ,
where
rf(°) =
-DQPDQC.
We repeat the procedure iteratively to generate a sequence of points
where
{x^},
with
Pk = Ind<*> =
Ak (ÄkÄk
)_1Äfc,
-DkPkDkc.
At each stage of the algorithm, we have to ensure that the point x^ is strictly
interior. Note that the condition Ax^ = b is satisfied automatically at each
stage because of the way we select
However, we also need to guarantee
that x\* > 0 for i = 1 , . . . ,n. This can be done through appropriate choice
of the step size ak, discussed next.
The main criterion for choosing ak is to make it as large as possible, but
not so large that some components of x( fc+1 ) become nonpositive. That is, we
select ak so that x\
— x\' + akd\ ' > 0 for i = 1 , . . . , n. To proceed, first
define
x{k)
rk = min
γγ^.
{ ^><ο }
df]
The number rk represents the largest value of the step size ak such that all
the components of x( fc+1 ) are nonnegative. To ensure that χ^+^ is strictly
interior, we use a step size of the form ak = ark, where a G (0,1). Typical
values of a for this method are a = 0.9 or 0.99 (see [96, p. 572]).
Unlike the simplex method, the affine scaling method will not reach the
optimal solution in a finite number of steps. Therefore, we need a stopping
criterion. For this, we can use any of the stopping criteria discussed in Section 8.2. For example, we can stop if
|cx(/c+l)_ca.(/c)|
max{l, |ccc(fc)|}
where ε > 0 is a prespecified threshold (see also [96, p. 572] for a similar
stopping criterion, as well as an alternative criterion involving duality).
412
NONSIMPLEX METHODS
Two-Phase Method
To implement the affine scaling method described above, we need an initial
feasible starting point that is strictly interior. We now describe a method
to find such a starting point. After the starting point is found, we can then
proceed to search for an optimal solution to the problem. This approach
involves two phases: In phase I we find an initial strictly interior feasible
point, and in phase II we use the result of phase I to initialize the affine
scaling algorithm to find an optimal solution. This procedure is analogous to
the two-phase simplex algorithm described in Section 16.6.
We now describe phase I of the two-phase affine scaling method. Let u be
an arbitrary vector with positive components, and let
v = b — Au.
If v = 0, then u is a strictly interior feasible point. We can then set x^ = u
and proceed to phase II, where we apply the affine scaling method as described
before. On the other hand, if v φ 0, we construct the following associated
artificial problem:
minimize
subject to
y
[A, v]
= b
>0.
The artificial problem above has an obvious strictly interior feasible point:
Using this point as the initial point, we can apply the affine scaling algorithm
to the artificial problem. Because the objective function in the artificial problem is bounded below by 0, the affine scaling method will terminate with some
optimal solution.
Proposition 18.1 The original LP problem has a feasible solution if and
only if the associated artificial problem has an optimal feasible solution with
objective function value zero.
□
Proof =>: If the original problem has a feasible solution x, then the vector
[ x T , 0 ] T is a feasible solution to the artificial problem. Clearly, this solution
has an objective function value of zero. This solution is therefore optimal for
the artificial problem, since there can be no feasible solution with negative
objective function value.
KARMARKAR'S METHOD
413
<ί=: Suppose that the artificial problem has an optimal feasible solution
with objective function value zero. Then, this solution must have the form
[sc T ,0] T , where x > 0. Hence, we have Ax = 6, and x is a feasible solution
to the original problem.
I
Suppose that the original LP problem has a feasible solution. By Proposition 18.1, if we apply the affine scaling method to the artificial problem (with
initial point [tt T ,l] T ), the algorithm will terminate with objective function
value zero. The optimal solution will be of the form [ccT, 0] T . We argue that x
will in general be a strictly interior feasible point. It is easy to see that x > 0.
To convince ourselves that each component of x will be positive in general,
note that the subset of optimal feasible solutions of the artificial problem in
which one or more among the first n components are zero is a very small or
thin subset of the set of all optimal feasible solutions. By small or thin we
mean in the sense that a two-dimensional plane in R 3 is small or thin. In
particular, the volume of the two-dimensional plane in M3 is zero. Thus, it is
very unlikely that the affine scaling algorithm will terminate with an optimal
feasible solution in which one or more among the first n components are zero.
Having completed phase I as described above, we then use the first n components of the terminal optimal feasible solution for the artificial problem
as our initial point for the affine scaling method applied to the original LP
problem. This second application of the affine scaling algorithm constitutes
phase II.
In theory, phase I generates a feasible point to initiate phase II. However,
because of the finite precision of typical computer implementations, the solution obtained from phase I may not, in fact, be feasible. Moreover, even if the
initial point in phase II is feasible, in practice the iterates may lose feasibility,
owing to finite precision computations. Special procedures for dealing with
such problems are available. For a discussion of numerical implementation of
affine scaling algorithms, see [42, Section 7.1.2].
18.4
Karmarkar's Method
Basic Ideas
Like the affine scaling method, Karmarkar's method for solving LP problems
differs fundamentally from the classical simplex method in various respects.
First, Karmarkar's method is an interior-point method. Another difference
between Karmarkar's method and the simplex method is that the latter stops
when it finds an optimal solution. On the other hand, Karmarkar's method
stops when it finds a solution that has an objective function value that is
less than or equal to a prespecified fraction of the original guess. A third
difference between the two methods is that the simplex method starts with
LP problems in standard form, whereas Karmarkar's method starts with LP
problems in a special canonical form, which we call Karmarkar's canonical
414
NONSIMPLEX METHODS
form. We discuss this canonical form in the next subsection. While more
recent interior-point methods are recognized to be superior to Karmarkar's
original algorithm in efficiency and robustness, a study of Karmarkar's method
provides an informative introduction to the study of more advanced interiorpoint methods.
Karmarkar's Canonical Form
To apply Karmarkar's algorithm to a given LP problem, we must first transform the given problem into a particular form, which we refer to as Karmarkar's canonical form. Karmarkar's canonical form is written as
cTx
Ax = 0
minimize
subject to
n
2= 1
x >0,
where x = [ # i , . . . , x n ] T . As in our discussion of Khachiyan's method, we
assume without loss of generality that the entries of A and c are integers.
We now introduce some notation that allows convenient manipulation of
the canonical form. First, let e = [ 1 , . . . , 1] T be the vector in Rn with each
component equal to 1. Let Ω denote the nullspace of A, that is, the subspace
Ω = {x e Rn : Ax = 0}.
Define the simplex in R n by
Δ = { Ι Ε Γ :
eTx = 1, x > 0}.
We denote the center of the simplex Δ by
a0
e
n
Clearly, αο Ε Δ. With the notation above, Karmarkar's canonical form can
be rewritten as
cTx
x £ Ω Π Δ.
minimize
subject to
Note that the constraint set (or feasible set) Ω Π Δ can be represented as
Ω Π Δ = {x e Rn : Ax = 0, eTx = 1, x > 0}
= <x e
A
X
fol
x >0
KARMARKAR'S METHOD
415
Example 18.1 Consider the following LP problem, taken from [125]:
minimize
5xi + 4x2 + 8x3
subject to
x\ + #2 -I- #3 = 1
#1,^2, #3 > 0.
Clearly, this problem is already in Karmarkar's canonical form, with c T =
[5,4,8], and A = O. The feasible set for this example is illustrated in Figure 18.2.
■
1
Figure 18.2
x
2
Feasible set for Example 18.1.
Example 18.2 Consider the following LP problem, taken from [110]:
minimize
subject to
3xi + 3x2 _ #3
2xi — 3x2 + X3 = 0
%1 + %2 + #3 = 1
Z l , £ 2 , # 3 > 0.
This problem is in Karmarkar's canonical form, with c T = [3,3,-1] and
A = [2, —3,1]. The feasible set for this example is illustrated in Figure 18.3
(adapted from [110]).
I
We show later that any LP problem can be converted into an equivalent
problem in Karmarkar's canonical form.
Karmarkar's Restricted Problem
Karmarkar's algorithm solves LP problems in Karmarkar's canonical form,
with the following assumptions:
A. The center ao of the simplex Δ is a feasible point: ao G Ω.
416
NONSIMPLEX METHODS
-x
Figure 18.3
2
The feasible set for Example 18.2.
B. The minimum value of the objective function over the feasible set is zero.
C. The (m + 1) x n matrix
\A\
[eTJ
has rank m + 1.
D. We are given a termination parameter q > 0, such that if we obtain a
feasible point x satisfying
^
< 2-,
c1 a0
then we consider the problem solved.
Any LP problem that is in Karmarkar's canonical form and that also satisfies
the four assumptions above is called a Karmarkar's restricted problem. In the
following we discuss the assumptions and their interpretations.
We begin by looking at assumption A. We point out that this assumption
is not restrictive, since any LP problem that has an optimal feasible solution
can be converted into a problem in Karmarkar's canonical form that satisfies
assumption A. We discuss this in the next subsection.
We next turn our attention to assumption B. Any LP problem in Karmarkar's canonical form can be converted into one that satisfies assumption
B, provided that we know beforehand the minimum value of its objective
function over the feasible set. Specifically, suppose that we are given an LP
problem where the minimum value of the objective function is M. As in
[110], consider the function f(x) = cTx — M. Then, using the property that
e T ai = 1 on the feasible set, we have that for any feasible x,
f(x) = cTx - M = cTx - MeTx
= (c T - MeT)x
= cTx,
KARMARKAR'S METHOD
417
where cT = cT — M e T . Notice that the objective function above has a
minimum value of zero and is a linear function of x. We can replace the
original objective function with the new objective function above, without
altering the solution.
Example 18.3 Recall the LP problem in Example 18.1:
minimize
subject to
5a; i + 4^2 + 8x3
x\ + X2 + X3 = 1
£ l , Z 2 , # 3 > 0.
The problem satisfies assumption A (and assumption C) but not assumption
B, since the minimum value of the objective function over the feasible set is 4.
To convert the above into a problem that satisfies assumption B, we replace
c T = [ 5 , 4 , 8 ] b y c T = [l,0,4].
I
Example 18.4 The reader can easily verify that the LP problem in Example 18.2 satisfies assumptions A, B, and C.
I
Assumption C is a technical assumption that is required in the implementation of the algorithm. Its significance will be clear when we discuss the
update equation in Karmarkar's algorithm.
Assumption D is the basis for the stopping criterion of Karmarkar's algorithm. In particular, we stop when we have found a feasible point satisfying
cTx/cTao
< 2~q. Such a stopping criterion is inherent in any algorithm that
uses finite-precision arithmetic. Observe that the stopping criterion above
depends on the value of c T ao- It will turn out that Karmarkar's algorithm
uses ao as the starting point. Therefore, we can see that the accuracy of the
final solution in the algorithm is influenced by the starting point.
From General Form to Karmarkar's Canonical Form
We now show how any LP problem can be coverted into an equivalent problem in Karmarkar's canonical form. By equivalent we mean that the solution
to one can be used to determine the solution to the other, and vice versa.
To this end, recall that any LP problem can be transformed into an equivalent problem in standard form. Therefore, it suffices to show that any LP
problem in standard form can be transformed into an equivalent problem in
Karmarkar's canonical form. In fact, the transformation given below (taken
from [71]) will also guarantee that assumption A of the preceding subsection
is satisfied.
To proceed, consider a given LP problem in standard form:
minimize
subject to
cTx,
Ax = b
x>0.
x G M71
418
NONSIMPLEX METHODS
We first present a simple way to convert this problem into Karmarkar's canonical form, ignoring the requirement to satisfy assumption A. For this, define
a new variable z G JRn+1 by
x\
111
T
T
Also define c' = [c ,0] and A' = [A, —b]. Using this notation, we can now
rewrite the LP problem above as
minimize
subject to
cf z,
A'z = 0
z >0.
z G Rn+1
We need one more step to transform the problem into one that includes
the constraint that the decision variables sum to 1. For this, let y =
[i/i, · · · ,2/n,2M+i]T G M n + 1 , where
;
rf
h xn + 1
1
" χι + · · · + χ η + 1'
Vi =
2/71+1
xi-\
;
2 = l,...,n
This transformation from x to y is called a projective transformation. It can
be shown that (see later)
cTx = 0
o
Ax = b o
x >0
c' y = 0,
Äy = 0,
Φ> y > 0.
Therefore, we have transformed the given LP problem in standard form into
the following problem, which is in Karmarkar's canonical form:
minimize
subject to
c' y,
y G Rn+1
A'y = 0
eTy = l
2/>0.
The transformation technique above can be modified slightly to ensure that
assumption A holds. We follow the treatment of [71]. We first assume that we
are given a point a = [αχ,..., an] that is a strictly interior feasible point; that
is, Aa = b and a > 0. We show later how this assumption can be enforced.
Let P+ denote the positive orthant of Mn, given by P+ = {x G Mn : x > 0}.
Let Δ = {x G M n + 1 : e T x = 1, x > 0} be the simplex in R n + 1 . Define the
map Γ : P+ -► Δ by
T^^IT^X),...,^!^)]1
KARMARKAR'S METHOD
419
with
T
i{x) = — /
,
xi/ai H
Tn+i(x) = —
El/ai H
X% a%
,
\-xn/an
-
—r,
+1
—.
i = l,...,n
hXri/on + 1
We call the map T a projective transformation of the positive orthant P+ into
the simplex Δ (for an introduction to projective transformations, see [68]).
The transformation T has several interesting properties (see Exercises 18.4,
18.5, and 18.6). In particular, we can find a vector d G M n + 1 and a matrix
A1 G R ™ * ^ 1 ) such that for each x G M n ,
cTx = 0
&
c,TT(x) = 0
Ax = b
&
ÄT{x)
and
=0
(see Exercises 18.5 and 18.6 for the forms of A' and d). Note that for each
x G Mn, we have eTT(x) = 1, which means that T(x) G Δ. Furthermore,
note that for each x G R n ,
x >0
Φ> Τ(χ) > 0.
Taking this into account, consider the following LP problem (where y is the
decision variable):
. . .
,τ
minimize c y
subject to A'y = 0
e T t/ = l
2/>0.
Note that this LP problem is in Karmarkar's canonical form. Furthermore,
in light of the definitions of d and A!\ the above LP problem is equivalent
to the original LP problem in standard form. Hence, we have converted
the LP problem in standard form into an equivalent problem in Karmarkar's
canonical form. In addition, because a is a strictly interior feasible point, and
a 0 = T(a) is the center of the simplex Δ (see Exercise 18.4), the point ao
is a feasible point of the transformed problem. Hence, assumption A of the
preceding subsection is satisfied for the problem above.
We started this discussion with the assumption that we are given a' point a
that is a strictly interior feasible point of the original LP problem in standard
form. To see how this assumption can be made to hold, we now show that we
can transform any given LP problem into an equivalent problem in standard
form where such a point a is explicitly given. To this end, consider a given
420
NONSIMPLEX METHODS
LP problem of the form
minimize
subject to
cTx
Ax>b
x>0.
Note that any LP problem can be converted into an equivalent problem of the
above form. To see this, recall that any LP problem can be transformed into
an equivalent problem in standard form. But any problem in standard form
can be represented as above, since the constraint Ax = b can be written as
Ax > 6, —Ax > —b. We next write the dual to the problem above:
maximize
λ b
subject to
λ τ Λ < cT
λ>0.
As we did in our discussion of Khachiyan's algorithm, we now combine the
primal and dual problems to get
cTx - bTX = 0,
Ax > 6,
T
AX
< c,
x > 0,
λ>0.
As we pointed out in the earlier section on Khachiyan's algorithm, the original
LP problem is solved if and only if we can find a pair (cc, λ) that satisfies the
set of relations above. This follows from Theorem 17.1. We now introduce
slack and surplus variables u and v to get the following equivalent set of
relations:
cTx - bT\
= 0,
Ax — v = 6,
Α τ λ + u = c,
cc,A, u, v > 0.
Let x0 e R n , λ 0 G R m , u0 £ Mn, and v0 G R m be points that satisfy x0 > 0,
λο > 0, UQ > 0, and Vo > 0. For example, we could choose XQ = [ 1 , . . . , 1] T ,
and likewise with λο, u$, and VQ. Consider the LP problem
minimize
subject to
z
cTx — bTX + (—c T x 0 + bT\0)z = 0
Ax — v + (6 — AXQ + VQ)Z = b
AT\
+ u + (c- Α τ λ 0 )2: = c
χ , λ , u, υ,ζ > 0.
KARMARKAR'S METHOD
421
We refer to the above as the Karmarkar's artificial problem, which can be
represented in matrix notation as
cTx
minimize
Ax = b
subject to
x > 0,
where
T
X = [x
C
=
,XT,uT,vT,z}T,
1®2ηι+2η> ^
'
T
-6T
•^·
Umxm
AT
' c
A=
(1
oTm
0J
Umxn
T
(-cTx0 + bT\0)
(6 - Ax0 + v0)
(c-ATA0)
-*m
^nxm
(the subscripts above refer to the dimensions/sizes of the corresponding matrices/vectors). Observe that the following point is a strictly interior feasible
point for the problem above:
\x
λ
u
x0
λο
=
U0
V
vo
[_z_
_ 1_
Furthermore, the minimum value of the objective function for Karmarkar's
artificial problem is zero if and only if the previous set of relations has a
solution, that is, there exists cc, λ, u, and v satisfying
cTx - bT\ 0,
Ax — v = 6,
Α τ λ + u = c,
χ , λ , η , υ > 0.
Therefore, Karmarkar's artificial LP problem is equivalent to the original LP
problem:
minimize
subject to
c x
Ax > b
x > 0.
Note that the main difference between the original LP problem and Karmarkar's artificial problem is that we have an explicit strictly interior feasible
point for Karmarkar's artificial problem, and hence we have satisfied the assumption that we imposed at the beginning of this subsection.
422
NONSIMPLEX METHODS
The Algorithm
We are now ready to describe Karmarkar's algorithm. Keep in mind that
the LP problem we are solving is a Karmarkar's restricted problem, that is,
a problem in Karmarkar's canonical form and satisfies assumptions A, B, C,
and D. For convenience, we restate the problem:
minimize
subject to
cTx,
x G M.n
x G Ω Π Δ,
where Ω = {x G W1 : Ax = 0} and Δ = {x G Rn : eTx = l,x > 0}.
Karmarkar's algorithm is an iterative algorithm that, given an initial point
χ(°ϊ and parameter q, generates a sequence x^l\x^2\
. . . , x ^ N \ Karmarkar's
algorithm is described by the following steps:
1. Initialize: Set k :— 0; x^ — do — e/n.
2. Update: Set χ^+^
below.
= Φ(α5^), where Φ is an update map described
3. Check the stopping criterion: If the condition cTx^/cTx^
is satisfied, then stop.
< 2~q
4. Iterate: Set k := k + 1; go to step 2.
We describe the update map Φ as follows. First, consider the first step
in the algorithm: χ^ = αο· To compute x^\ we use the familiar update
equation
where a is a step size and S0' is an update direction. The step size a is
chosen to be a value in (0,1). Karmarkar recommends a value of 1/4 in his
original paper [71]. The update direction er0) is chosen as follows. First,
note that the gradient of the objective function is c. Therefore, the direction
of maximum rate of decrease of the objective function is —c. However, in
general, we cannot simply update along this direction, since x^ is required
to lie in the constraint set
Ω Π Δ = {x G R n : Ax = 0, eTx = 1, x > 0}
xe
n .
A
X
° ,*>ol
n
= { x e R" : BB00xx == \ 0 , x > 0
1
KARMARKAR'S METHOD
423
where B0 € R ^ * 1 ) * " is given by
B0 =
A
Note that since x^ e Ω Π Δ, then for x^ = a;(0) + ad ( 0 ) also to lie in
Ω Π Δ, the vector d^ must be an element of the nullspace of Bo. Hence, we
choose d^ to be in the direction of the orthogonal projection of — c onto the
nullspace of Bo- This projection is accomplished by the matrix Po given by
Po = In — Bo C^o-Bo )~ -BoNote that BoB0
to be the vector
is nonsingular by assumption C. Specifically, we choose cl· 0 '
-rc^0', where
PQC
e(°> = \\Po4
and r = 1/\/n(n — 1). The scalar r is incorporated into the update vector
cr 0 ' for the following reason. First, observe that r is the radius of the largest
sphere inscribed in the simplex Δ (see Exercise 18.7). Therefore, the vector
^(0) _ rg(o) p 0 m t s m the direction of the projection cr 0 ' of c onto the nullspace
of Bo and χ^ = χ^ + a<r 0 ' is guaranteed to lie in the constraint set Ω Π Δ.
In fact, x^ lies in the set Ω Π Δ Π {x : \\x — ao\\ < r}. Finally, we note that
a^1) is a strictly interior point of Δ.
The general update step x^^
= Φ(α5^) is performed as follows. We first
give a brief description of the basic idea, which is similar to the update from
χ(°ϊ to a^1) described above. However, note that x^ is, in general, not at
the center of the simplex. Therefore, let us first transform this point to the
center. To do this, let Dk be a diagonal matrix whose diagonal entries are
the components of the vector x^:
,(*)
0
0
,(*)
Dk
It turns out that because x^ is a strictly interior point of Δ, χ^ is a strictly
interior point of Δ for all k (see Exercise 18.10). Therefore, Dk is nonsingular
and
l/x\ (*)
0
Dkl
=
1/x·
(k)
λ
Consider the mapping Uk : Δ —► Δ given by Uk{x) = Dk 1x/eTDk
χ.
Note that Uk(x^)
= e/n = a 0 . We use Uk to change the variable from
424
NONSIMPLEX METHODS
x to x = Uk(x).
We do this so that x^ is mapped into the center of
the simplex, as indicated above. Note that Uk is an invertible mapping,
with x = U^ix)
= Dkx/eT Dkx.
Letting x^ = Uk(x^)
= ao, we
can now apply the procedure that we described before for getting a^1) from
χ(°) = ao. Specifically, we update x^ to obtain äj(fc+1) using the update
formula χ^+^ = χ^ + aS-k\ To compute dP*\ we need to state the original
LP problem in the new variable x:
minimize
subject to
cTDkx
ADkx = 0
x e Δ.
The reader can easily verify that the LP problem above in the new variable x is equivalent to the original LP problem in the sense that x* is an
optimal solution to the original problem if and only if Uk(x*) is an optimal solution to the transformed problem. To see this, simply note that
x = Uk(x) = D^x/e1
D^lx, and rewrite the objective function and constraints accordingly (see Exercise 18.8). As before, let
ADk
We choose (Γ ' — —r<r \ where er ^ is the normalized projection of
—(cT Dk)T = —Dkc onto the nullspace of Bk, and r = l/y/n(n — 1) as
before. To determine er \ we define the projector matrix Pk by
Pk = In~
Note that BkBk
fore given by
Bk (BkBk
)~
Bk.
is nonsingular (see Exercise 18.9). The vector c^k' is there{k) =
PkDkc
\\PkDkc\\'
The direction vector (r ' is then
\\PkDkc\\
The updated vector x( fc+1 ) = x^ + aS ' is guaranteed to lie in the transformed feasible set {x : ADkx = 0}(ΊΔ. The final step is to apply the inverse
transformation U^1 to obtain cc^fc+1^:
Note that χ^+^ lies in the set Ω Π Δ. Indeed, we have already seen that Uk
and U^1 map Δ into Δ. To see that Α χ ^ + 1 ) = 0, we simply premultiply the
foregoing expression by A and use the fact that ADkx^k+l>) = 0.
KARMARKAR'S METHOD
425
We now summarize the update x<fc+1) = *(«<*>):
1. Compute the matrices:
„(*)
0
Dk =
„(*0
ADk
j
2. Compute the orthogonal projector onto the nullspace of Bk:
Pk = In-
Bk (BkBk
)"
Bk.
3. Compute the normalized orthogonal projection of c onto the nullspace of
Bk:
P D C
g(*) =
* *
\\PkDkc\\'
4. Compute the direction vector:
where r = 1/\/n{n — 1).
5. Compute x( fc+1 ) using
x(fc+1)=o0+ad(fc)>
where a is the prespecified step size, a G (0,1).
6. Compute χ^+^
by applying the inverse transformation U"^1:
*(*+!) = t / - i ( ä ( * + D )
Dkx(k+V
e T D f c ä( f c + 1 )'
The matrix Pk in step 2 is needed solely for computing PkDkc in step
3. In fact, the two steps can be combined in an efficient way without having
to compute Pk explicitly, as follows. We first solve a set of linear equations
BkBk y = BkDkc (for the variable y), and then compute PkDkc using the
expression PkDkc = Dkc — B^y.
For more details on Karmarkar's algorithm, see [42], [55], [71], and [124].
For an informal introduction to the algorithm, see [110]. For further reading
on other nonsimplex methods in linear programming, see [42], [55], [96], and
[119]. A continuous gradient system for solving linear programming problems
is discussed in [26]. An interesting three-article series on developments of
426
NONSIMPLEX METHODS
the linear programming area before and after 1984 appeared in SI AM News,
Vol. 22, No. 2, March 1989. The first article in this journal issue contains an
account by Wright on recent progress and a history of linear programming
from the early 1800s. The second article, by Anstreicher, focuses on interiorpoint algorithms developed since 1984. Finally in the third article in the series,
Monma surveys computational implementations of interior-point methods.
EXERCISES
18.1 Write a simple MATLAB function to implement the affine scaling algorithm. The inputs are c, A, 6, and χ(°\ where x^ is a strictly feasible initial point. Test the function on the problem in Example 16.2; use
*<°> = [2,3,2,3,3] τ .
18.2 Write a MATLAB routine that implements the two-phase affine scaling
method. It may be useful to use the MATLAB function of Exercise 18.1. Test
the routine on the problem in Example 16.5.
18.3 For a given linear programming problem of the form
minimize
subject to
cTx
Ax > b
x > 0,
the associated Karmarkar's artificial problem can be solved directly using the
affine scaling method. Write a simple MATLAB program to solve problems of
the form above by using the affine scaling algorithm applied to the associated
Karmarkar's artificial problem. It may be useful to use the MATLAB function
of Exercise 18.1. Test your program on the problem in Example 15.15.
18.4 Let a G Mn, a > 0. Let T = [ T i , . . . , T n +i] be the projective transformation of the positive orthant P+ of R n into the simplex Δ in M n + 1 , given
by
f
*<£* , _L1
ifl<z<n
[m/αι+...+χη/αη+ι
if i = n H-1.
,
Prove the following properties of T (see [71]):
1. T is a one-to-one mapping; that is, T(x) = T(y) implies that x = y.
2. T maps P+ onto Δ \ {x : xn+i = 0} = {x G Δ : x n +i > 0}; that is, for
each y G {x G Δ : £ n +i > 0}, there exists x G P+ such that y = T(x).
3. The inverse transformation of T exists on {x G Δ : xn+\ > 0} and is
given by T " 1 = [T~\ . . . , Γ " 1 ] Τ , with T ^ ) =
aiyi/yn+1.
EXERCISES
427
4. T maps a to the center of the simplex Δ, that is, T{a) = e/(n + 1) =
[l/(n + l ) , . . . , l / ( n + l ) ] e M n + 1 .
5. Suppose that x satisfies Ax = 6, and y
[yiai,.. .,ynan]T·
Then, Ax' = byn+i.
= T(x).
Let x'
=
18.5 Let T be the projective transformation in Exercise 18.4 and A G R m x n
be a given matrix. Prove that there exists a matrix Af G M m x ( n + 1 ) such that
Ax = b if and only if A'T(x) = 0.
Hint: Let the zth column of Al be given by a* times the zth column of A,
i = 1 , . . . , n, and the (n + l)th column of A' be given by —6.
18.6 Let T be the projective transformation in Exercise 18.4 and c G W1 be
a given vector. Prove that there exists a vector d G E n + 1 such that cTx = 0
if and only if c'TT(x) = 0.
Hint: Use property 3 in Exercise 18.4, with the d = [c' l 5 ..., c ^ + 1 ] T given by
c't = diCi, i = 1 , . . . , n, and c^ + 1 = 0.
18.7 Let Δ = {x G R n : eTx = l , x > 0} be the simplex in E n , n > 1,
and let do = e/n be its center. A sphere of radius r centered at ao is the
set {x G R n : ||x — «oll ^ r}- The sphere is said to be inscribed in A if
{x G Mn : \\x — ao|| = r, eTx — 1} C Δ. Show that the largest such sphere
has radius r = 1/\/n(n — 1).
18.8 Consider the following Karmarkar's restricted problem:
minimize
subject to
c a?
Ax = 0
Let aio G Δ be a strictly interior point of Δ, and D be a diagonal matrix
whose diagonal entries are the components of x$. Define the map U : Δ —► Δ
by U(x) = D~1x/eJD~lx.
Let x = U(x) represent a change of variable.
Show that the following transformed LP problem in the variable äj,
minimize
subject to
cT Dx
ADx = 0
X G Δ,
is equivalent to the original LP problem above in the sense that x* is an
optimal solution to the original problem if and only if x* = U(x*) is an
optimal solution to the transformed problem.
428
NONSIMPLEX METHODS
18.9 Let A e
satisfies
Wnx n , ra < n, and Ω = {x : Ax = 0}. Suppose that A
rank
A
= ra + l.
Let XQ G Δ η Ω be a strictly interior point of Δ C W1 and Z> be a diagonal
matrix whose diagonal entries are the components of XQ. Consider the matrix
B defined by
Γ
B =
Show that mnkB
ΑΌ]
= ra + 1, and hence BB
is nonsingular.
18.10 Show that in Karmarkar's algorithm, x^
οίΔ.
is a strictly interior point
CHAPTER 19
INTEGER LINEAR PROGRAMMING
19.1
Introduction
This chapter is devoted to linear programs with the additional constraint
that the solution components be integers. Such problems are called integer
linear programming (ILP) (or simply integer programming) problems, and
arise naturally in many practical situations. For example, in Example 15.1,
the decision variables represent production levels, which we allowed to take
real values. If production levels correspond to actual numbers of products,
then it is natural to impose the constraint that they be integer valued. If we
expect solutions that are very large in magnitude, then ignoring the integer
constraint might have little practical consequence. However, in cases where
the solution is a relatively small integer (on the order of 10, say), then ignoring
the integer constraint could lead to dramatically erroneous solutions.
Throughout this section, we use the notation Z for the set of integers, Z n
the set of vectors with n integer components, and Zmxn the set of m x n
matrices with integer entries. Using this notation, we can express an ILP
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
429
430
INTEGER LINEAR PROGRAMMING
problem in following form:
minimize
subject to
c x
Ax = b
x>0
xezn.
19.2
Unimodular Matrices
There is a class of ILP problems that can be solved using standard linear
programming methods. To proceed, we need some definitions and background
results. The reader should recall the definition of a minor from Section 2.2.
Definition 19.1 An rax n integer matrix A G Z m x n , ra < n, is unimodular
if all its nonzero rath-order minors are ± 1 (i.e., either 1 or —1).
I
Unimodular matrices play a special role in the context of linear equations
and integer basic solutions. Consider the linear equation Ax = b with A G
Z m X n , ra < n. Let ß b e a corresponding basis matrix (an ra x ra matrix
consisting of ra linearly independent columns of A). Then, the unimodularity
of A is equivalent to | d e t B | = 1 for any such B. The following lemma
connects unimodularity with integer basic solutions.
Lemma 19.1 Consider the linear equation Ax = b where A G Z m x n ,
ra < n, is unimodular and b G Z m . Then, all basic solutions have integer
components.
□
Proof. As usual, suppose that the first ra columns of A constitute a basis,
and that B is the invertible ra x ra matrix composed of these columns. Then
the corresponding basic solution is
x —
B~lb
0
Because all the elements of A are integers, B is an integer matrix. Moreover,
because A is unimodular, | d e t B | = 1. This implies that the inverse Β~λ is
also an integer matrix (see [62, p. 21]). Therefore, x* is an integer vector. I
Corollary 19.1 Consider the LP constraint
Ax — b
x > 0,
mxr
where A is unimodular, A G Z
\ m < n, and b G Z m . Then, all basic
feasible solutions have integer components.
U
UNIMODULAR MATRICES
431
Unimodularity allows us to solve ILP problems using the simplex method.
Specifically, consider the ILP problem
minimize
subject to
cTx
Ax = b
x>0
xezn
where A G Z m X n , m < n, is unimodular and b G Z m . Then, the corollary
above tells us that if we consider the associated LP problem
minimize
subject to
ex
Ax = b
x>0,
the optimal basic feasible solution is an integer vector. This means that we
can apply the simplex method to the LP problem above to obtain a solution
to the original ILP problem.
Example 19.1 Consider the following ILP problem:
maximize
subject to
2^i -+- 5x2
x\ + xs = 4
#2 +
%A
= 6
xi + #2 + #5 = 8
^1,^2,^3,^4,^5 > 0
^1,^2,^3,^4,^5 Ξ Z
We can write this problem in matrix form with
1 0
1 0 0
0 1 0
1 0 ,
1 1 0
0 1
6 =
4
6
8
Notice that 6 G Z 3 . Moreover, it is easy to check that A is unimodular.
Hence, the ILP problem above can be solved by solving the LP problem
maximize 2xi + hx2
subject to xi + #3 = 4
X2 + X4 = 6
X\ + %2 + #5 = 8
^1,^2,^3,^4,^5 > 0.
432
INTEGER LINEAR PROGRAMMING
This was done in Example 16.2 using the simplex method, yielding optimal
solution [2,6,2,0,0] T , which is an integer vector.
I
In general, when the matrix A is not unimodular, the simplex method
applied to the associated LP problem yields a noninteger optimal solution.
However, in some cases, even if A is not unimodular, the simplex method still
produces an integer optimal basic feasible solution. To see this, suppose that
we are given A G Z m x n , m < n, and b G Z m . Note that as long as each mxm
basis matrix B consisting of columns of A corresponding to a basic feasible
solution has the property that | d e t i ? | = 1, we can use the argument in the
proof of Lemma 19.1 to conclude that the basic feasible solution is an integer
vector. Equivalently, we can draw this conclusion if each basis submatrix B
of A such that |det JB| Φ 1 corresponds to a nonfeasible basic solution. We
illustrate this in the following example.
Example 19.2 Consider the ILP problem
minimize
subject to
— x\ — 2x2
— 2x\ + X2 + #3 = 2
— X\ + X2 + X4 = 3
xi + #5 = 3
Xi > 0, i = 1 , . . . ,5
Xi G Z, i — 1 , . . . ,5.
Can this ILP problem be solved using the simplex method? We can easily
verify that the matrix
" - 2 1 1 0 θ"
- 1 1 0
1 0
1 0
0 0 1
is not unimodular. Indeed, it has one (and only one) basis submatrix with
determinant other than ± 1 , consisting of the first, fourth, and fifth columns
of A. Indeed, if we write B — [0,1,0,4,0,5], then detB = —2. However, a
closer examination of this matrix and the vector b = [2,3,3] T reveals that
the corresponding basic solution is not feasible: B~lb = [—1,2,4]T (which,
coincidentally, happens to be an integer vector). Therefore, for this problem, applying the simplex method to the associated LP problem will produce
an integer optimal basic feasible solution, which also solves the original ILP
problem.
UNIMODULAR MATRICES
433
We begin by forming the first tableau,
cT
αι
-2
-1
1
-1
a2
1
1
0
-2
«3
ei4
1
0
0
0
0
1
0
0
a5
0
0
1
0
b
2
3
3
0
We have r2 = —2. Therefore, we introduce a2 into the new basis. We calculate
the ratios 2/io/2/i2, y%2 > 0, to determine the pivot element:
— = 2/12
and
1
V2
3
2/22
1'
° =
We will use 2/12 as the pivot. Performing elementary row operations, we obtain
the second tableau,
a,\
rT
03
a2
04
-2
1
1
1 0 - 1
1 0
0
-5 0
2
05
b
0
0 2
1 0 1
0
1 3
0
0 4
We now have n = — 5 < 0. Therefore, we introduce αι into the new basis.
We next calculate the ratios 2/20/2/22, y%2 > 0, to determine the pivot element:
M= I
2/21
1
an d
*» = *.
2/31
1
We will use 2/21 as the pivot. Performing row elementary operations, we obtain
the third tableau,
a2 <*3 0 4 a 5 b
Ol
1 -1 2
0 4
0
1
1
0 1
0 -1
1
1
1 2
0
0
0 9
0
0 -3
5
We have r% = — 3 < 0. Therefore, we introduce 03 into the new basis. We
next calculate the ratios 2/^0/2/22, 2/ΐ2 > 0, to determine the pivot element,
2/30
2/33
=
2
1'
434
INTEGER LINEAR PROGRAMMING
We will use 2/33 as the pivot. Performing row elementary operations, we obtain
the fourth tableau,
CL\
rT
Q>2
O3
CI4
CI5
0
1 0
1 1
1 0
0
0
1
0
0
1 - 1 1
0
0
0
2
3
b
6
3
2
15
All reduced cost coefficients are now positive, which means that the current
solution is optimal. This solution is [3,6,2,0,0] T .
I
Next, we consider ILP problems of the form
minimize
subject to
cTx
Ax < b
x>0
xezn
We have seen in Section 15.5 that we can transform the inequality constraint
Ax < b into standard form by introducing slack variables. Doing so would
lead to a new problem in standard form for which the constraint has the form
[A, I]y = b (where the vector y contains x and the slack variables). To deal
with matrices of the form [A, J], we need another definition.
Definition 19.2 An m x n integer matrix A G Z m X n is totally unimodular
if all its nonzero minors are ± 1 .
I
By minors here we mean pth-order minors for p < min(m, n). Equivalently, a
matrix A G Z m x n is totally unimodular if and only if all its square invertible
submatrices have determinant ± 1 . By a submatrix of A we mean a matrix
obtained by removing some columns and rows of A. It is easy to see from this
definition that if an integer matrix is totally unimodular, then each entry is
0, 1, or —1. The next proposition relates the total unimodularity of A with
the unimodularity of [A, I] (see also Exercise 19.3).
Proposition 19.1 If an m x n integer matrix A G Z m x n is totally unimodular, then the matrix [A, I] is unimodular.
□
Proof. Let A satisfy the assumptions of the proposition. We will show that
any m x m invertible submatrix of [A,/] has determinant ± 1 . We first note
that any m x m invertible submatrix of [A, I] that consists only of columns
of A has determinant ± 1 because A is totally unimodular. Moreover, the
m x m submatrix / satisfies det 1=1.
Consider now an m x m invertible submatrix of [A, I] composed of k
columns of A and m — k columns of i\ Without loss of generality, suppose that this submatrix is composed of the last k columns of A and the first
UNIMODULAR MATRICES
435
m — k columns of J; that is, the m x m invert ible submatrix is
B = I an_fc+i
···
an
e\
···
em_J =
■Hm—k,k
-*m—k
Bk,k
O
where e* is the ith column of the identity matrix. This choice of columns is
without loss of generality because we can exchange rows and columns to arrive
at this form, and each exchange only changes the sign of the determinant.
Moreover, note that detB — ± d e t Bk,k (see also Exercises 19.4 and 2.4).
Thus, Bk,k is invert ible because B is invert ible. Moreover, because Bk,k is a
submatrix of A and A is totally unimodular, det Bk,k = ± 1 . Hence, det B =
± 1 also. Thus any mxm invert ible submatrix of [A, I] has determinant ± 1 ,
which implies that [A, I] is unimodular.
I
Combining the result above with Lemma 19.1, we obtain the following
corollary.
Corollary 19.2 Consider the LP constraint
[A,I]x = b
x>0,
where A G Z m X n is totally unimodular and b G Z m . Then, all basic feasible
solutions have integer components.
D
Total total unimodularity of A allows us to solve ILP problems of the
following form using the simplex method:
minimize
subject to
cTx
Ax < b
x>0
xezn
where b G Z m . Specifically, we first consider the associated LP problem
minimize
subject to
cTx
Ax < b
x>0.
If A is totally unimodular, then the corollary above tells us that once we
convert this problem into standard form by introducing a slack-variable vector
minimize
subject to
c x
[A, I]
x,z
> 0,
436
INTEGER LINEAR PROGRAMMING
the optimal basic feasible solution is an integer vector. This means that we
can apply the simplex method to the LP problem above to obtain a solution
to the original ILP problem. Note that although we only needed the x part
of the solution to be integer, the slack-variable vector z is automatically integer for any integer x, because both A and b only contain integers (see also
Exercise 19.5).
Example 19.3 Consider the following ILP problem:
maximize
subject to
2x\ + 5x2
x\ < 4
%2 < 6
X\ + %2 < 8
Xi,^2 > 0
£ i , £ 2 € Z.
This problem can be written in the matrix form above with
"l
0
1
o"
4
1
1
6
It is easy to check that A is totally unimodular. Hence, the ILP problem
above can be solved by solving the LP problem
maximize 2xi + 5x 2
subject to xi + #3 = 4
#2 + #4 = 6
Xl + ^2 + #5 = 8
^ 1 , ^ 2 , ^ 3 , ^ 4 , ^ 5 > 0,
as was done in Example 16.2.
As discussed before, even if [A, I] is not unimodular, the simplex algorithm
might still yield a solution to the original ILP. In particular, even if A is not
totally unimodular, the method above might still work, as illustrated in the
following example.
Example 19.4 Consider the following ILP problem:
maximize
subject to
X\ + 2^2
— 2x\ + X2 < 2
x\ — %2 > —3
x\ < 3
X\ > 0, #2 > 0, #1,£2 € Ζ·
THE GOMORY CUTTING-PLANE METHOD
437
We first express the given problem in this equivalent form:
minimize
subject to
— X\ — 2x2
— 2x\ + x2 <2
- X\ + X2 < 3
x\ < 3
X\ > 0, X2 > 0, Xi,^2
We next represent the problem above in standard form by introducing slack
variables £3, #4, and x§ to obtain
minimize
subject to
— X\ — 2x2
— 2x\ + #2 + #3
- x\ + x2
+XA
x\
Xi > 0,
=2
=3
+ £5 = 3
z = 1 , . . . ,5.
This problem is now of the form in Example 19.2, where the simplex method
was used. Recall that the solution is [3,6,2,0,0] T . Thus, the solution to the
original problem is x* = [3,6] T .
Note that the matrix
" - 2 1|
-1 1
1 0
is not totally unimodular, because it has an entry (—2) not equal to 0, 1, or
—1. Indeed, the matrix [A,/] is not unimodular. However, in this case, the
simplex method still produced an optimal solution to the ILP, as explained
in Example 19.2.
I
19.3
The Gomory Cutting-Plane Method
In 1958, Ralph E. Gomory [54] proposed a method where noninteger optimal
solutions obtained using the simplex method are successively removed from
the feasible set by adding constraints that exclude these noninteger solutions
from the feasible set. The additional constraints, referred to as Gomory cuts,
do not eliminate integer feasible solutions from the feasible set. The process
is repeated until the optimal solution is an integer vector.
To describe Gomory cuts, we use the floor operator, defined next.
Definition 19.3 The floor of a real number, denoted [x\, is the integer obtained by rounding x toward —00.
I
For example, [3.4J = 3 and [—3.4J = - 4 .
438
INTEGER LINEAR PROGRAMMING
Consider the ILP problem
minimize
subject to
ex
Ax = b
x>0
xezn.
We begin by applying the simplex method to obtain an optimal basic feasible
solution to the LP problem
minimize
subject to
cTx
Ax = b
x>0.
As usual, suppose that the first m columns form the basis for the optimal
basic feasible solution. The corresponding canonical augmented matrix is
αι
1
0
a2
0
1
·· .
·· .
0
0
0
0
· ·
Obi
ttm+1
Q"m
o
o
·· •
.· •
0
0
2/l,m+l
2/2,m+l
···
1
···
0
2/i,m+l
···
0
···
1
J/m,m+l
Q>n
2/0
'*
2/l,n
2/10
'
2/2,n
2/20
·'·
Vi,n
ViO
''*
Vrn,n
VmO
Consider the ith component of the optimal basic feasible solution, yio. Suppose that yio is not an integer. Note that any feasible vector x satisfies the
equality constraint (taken from the ith row)
n
X
i+ Σ
j=m+l
yi X
33
=
^°*
We use this equation to derive an additional constraint that would eliminate the current optimal noninteger solution from the feasible set without
eliminating any integer feasible solution. To see how, consider the inequality
constraint
x
*+
n
Σ
j=m+l
L2/iiJ^ ^ 2/it).
Because [yij\ < yij, any x > 0 that satisfies the first equality constraint
above also satisfies this inequality constraint. Thus, any feasible x satisfies
this inequality constraint. Moreover, for any integer feasible vector cc, the
THE GOMORY CUTTING-PLANE METHOD
439
left-hand side of the inequality constraint is an integer. Therefore, any integer
feasible vector x also satisfies
n
j=m+l
Subtracting this inequality from the equation above, we deduce that any integer feasible vector satisfies
n
Σ
j=m+l
(yii ~
L^J)^·
> 2/<o - Li/ioJ.
Next, notice that the optimal basic feasible solution above does not satisfy this
inequality, because the left-hand side for the optimal basic feasible solution is
0, but the right-hand side is a positive number. Therefore, if we impose the
additional inequality constraint above to the original LP problem, the new
constraint set would be such that the current optimal basic feasible solution
is no longer feasible, but yet every integer feasible vector remains feasible.
This new constraint is called a Gomory cut
To transform the new LP problem into standard form, we introduce the
surplus variable xn+i to obtain the equality constraint
n
j=m+l
For convenience, we will also call this equality constraint a Gomory cut. By
augmenting this equation into A and 6, or canonical versions of them (e.g.,
in the form of a simplex tableau), we obtain a new LP problem in standard
form. We can then solve the new problem using the simplex method and
examine the resulting optimal basic feasible solution. If the solution satisfies
the integer constraints, then we are done—this vector gives an optimal solution
to the original ILP problem by extracting the appropriate components. If the
solution does not satisfy the integer constraints, we introduce another Gomory
cut and repeat the process. We call this procedure the Gomory cutting-plane
method.
Note that in applying the Gomory cutting-plane method, we only need to
introduce enough cuts to satisfy the integer constraints for the original ILP
problem. The additional variables introduced by slack variables or by the
Gomory cuts are not constrained to be integers.
In the following two examples, we illustrate how the Gomory cutting-plane
method can be implemented by incorporating Gomory cuts directly into the
simplex tableau.
440
INTEGER LINEAR PROGRAMMING
Example 19.5 Consider the following ILP problem 1 :
maximize
3#i + 4^2
2
-x\ + X2 < 3
5
2
2
subject to
-Xl
- -x2
< 1
> 0
X\,X2
Xl,X 2 £ ^ ·
We first solve the problem graphically. The constraint set Ω for the associated
LP problem (without integer constraints) can be found by calculating the
extreme points:
x& =
o ο ] Τ , χ{2) = [ΐ ο ] Τ , *<3> = [ο 3 ] T , x ( 4 ) = [ff ψ]Τ
In Figure 19.1, we show the feasible set Ω. In Figure 19.2, we show the feasible
set for the ILP problem, which allows us to solve the problem graphically. The
solution is obtained by finding the straight line f = 3xi + 4x2 with largest
/ that passes through a feasible point with integer components. This can be
accomplished by first drawing the line / = 3#i + 4x 2 for / = 0 and then
gradually increasing the values of / , which corresponds to sliding across the
feasible region until the straight line passes through the "last" integer feasible
point yielding the largest value of the objective function. From Figure 19.2,
we can see that the optimal solution to the ILP problem is [2,2] T .
We now solve the problem using the Gomory cutting-plane method. First
we represent the associated LP problem in standard form:
maximize
subject to
3x\ + 4x 2
2
-xi + X2 + X3 — 3
5
2
2
-Xl - -X2 +X4 — 1
5
5
^ 1 , ^ 2 , ^ 3 , ^ 4 > 0.
Note that we only need the first two components of the solution to be integers.
We can start the simplex method because we have an obvious basic feasible
solution. The first tableau is
CLi
2
5
2
5
CT
-3
a2
1
2
5
-4
«3
a4
1
0
0
0
1
0
b
3
1
0
Thanks to David Schvartzman Cohenca for his solution to this problem.
THE GOMORY CUTTING-PLANE METHOD
0
0.5
Figure 19.1
0
F i g u r e 19.2
1
1.5
2
2.5
3
3.5
4
Feasible set Ω for LP problem in Example 19.5.
0.5
1
1.5
2
2.5
3
3.5
4
Graphical solution for ILP problem in Example 19.5.
441
442
INTEGER LINEAR PROGRAMMING
We bring ci2 into the basis and pivot about the element (1,2) to obtain
2
5
14
25
7
—-k5
r
Q>2
«3
1
0
0
1
b
3
a±
0
1
0
2
5
4
11
5
12
Next, we pivot about the element (2,1) to obtain
a>2 o 3
ai
CI4
_io
14
25
14
5
2
10
14
10
14
0
1
5
0
20
14
55
14
35
2
The corresponding optimal basic feasible solution is
55
14
10
7
Q
U
Q1
U
which does not satisfy the integer constraints.
We start by introducing the Gomory cut corresponding to the first row of
the tableau. We obtain
10
4
3 + Ϊ 4 * 4 "ϊΤ
#5
=
6
14'
our tableau:
d\
0
1
0
0
a<i
1
0
0
0
a3
10
14
10
14
10
14
5
GL4
10
14
25
14
4
14
5
2
a5
0
0
-1
0
b
20
14
55
14
6
14
35
2
Pivoting about the element (3,3) gives
a\
0
1
0
0
Q,2
as
«4
05
1
0
0
0
0
0
1
0
-1
1
1
3
2
2
5
1
2
_7
5
7
^y
The corresponding optimal basic feasible solution is [7/2,1,3/5,0,0] T , which
still does not satisfy the integer constraint.
Next, we construct the Gomory cut for the second row of the tableau:
-X4 —
XQ
THE GOMORY CUTTING-PLANE METHOD
443
We add this constraint to our tableau to obtain
rT
a\
0
1
0
0
0
a,2 as a\
a^
1 0 - 1 1
3
1
0
0
2
2
7
1
0
5
5
1
0
0
0
2
1
0
0
7
2
ae
0
0
0
-1
0
7
2
3
5
1
2
29
2
a>2 03
Pivoting about (4,4), we get
d\
T
0
1
0
0
0
1
0
0
0
0
0
0
1
0
0
0,4
05
ag
b
0
0
0
1
0
1
1
-2
2
3
2
I
15
0I
7
5
4
-2
1
5
1
1
14
In this optimal basic feasible solution, the first two components are integers.
Thus, we conclude that the solution to our ILP is [2,2] T , which agrees with
the graphical solution in Figure 19.2.
I
In Example 19.5, the final solution to the LP problem after applying the
Gomory cutting-plane method is not an integer vector. Only the first two
components are integers, as these are the only two components in the original
ILP problem. As pointed out earlier, the slack variables and variables introduced by the Gomory cuts are not constrained to be integers. However, if
we are given an ILP problem with inequality constraints as in Example 19.5
but with only integer values in constraint data, then the slack variables and
those introduced by the Gomory cuts are automatically integer valued (see
also Exercise 19.9). We illustrate this in the following example.
E x a m p l e 19.6 Consider the following ILP problem:
maximize
subject to
3xi 4- 4x2
3xi — X2 < 12
3xi + l l x 2 < 66
Xl,X2 > 0
X i , x 2 G Z.
A graphical solution to this ILP problem is shown in Figure 19.3. As in Example 19.5, the solution is obtained by finding the straight line / = 3xi +4x2
with largest / that passes through a feasible point with integer components.
This point is [5,4] T .
444
INTEGER LINEAR PROGRAMMING
7
6
5
4
x" 3
2
1
0
- 1 0
1
2
Ί
3
4
5
6
Figure 19.3 Graphical solution of the ILP problem in Example 19.6, where integer
feasible solutions are marked with heavy dots.
We now solve the ILP problem above using the simplex method with Gomory cuts. We first represent the associated LP problem in standard form by
introducing slack variables x$ and #4. The initial tableau has the form
a\
cT
CL2 o>3
3 - 1 1
3
11 0
-3 -4
0
04
b
0
1
0
12
66
0
In this case there is an obvious initial basic feasible solution available, which
allows us to initialize the simplex method to solve the problem. After two
iterations of the simplex algorithm, the final tableau is
1
0
0
rT
0
1
0
b
tt4
»3
11
36
1
12
7
12
1
36
1
12
5
12
11
2
9
2
69
2
with optimal solution
*· = [¥ 1 ° °]'·
Both basic components are noninteger. Let us construct a Gomory cut for
the first basic component x\ = 11/2. Prom the first row of the tableau, the
associated constraint equation is
Xl
11
+ 36*
3+
1
4
11
36* = T ·
THE GOMORY CUTTING-PLANE METHOD
445
7r
4
4
Liiiy
•
2h
l[
4
·
·
·
2
3
·
·
Figure 19.4
Graphical solution of the ILP in Example 19.6 after adding the
constraint x\ < 5 to the original constraints.
If we apply the floor operator to this equation as explained before, we get
an inequality constraint
x\ < 5.
A graphical solution of the above problem after adding this inequality constraint to the original LP problem is shown in Figure 19.4. We can see that
in this new problem, the first component of the optimal solution is an integer,
but not the second. This means that a single Gomory cut will not suffice.
To continue with the Gomory procedure for the problem using the simplex
method, we first write down the Gomory cut
11
1
1
3 6 * 3 + 3 g * 4 - * 5 = 2.
We now obtain a new tableau by augmenting the previous tableau with the
above constraint:
αι a2
a3 a,4 a5 b
11
1
0 11
0
1
36
36
2
1
1
0 92
1
0
12 12
11
1
-1 21
0
0
36
36
7
5
0 69
0
0
12
12
2
At this point, there is no obvious basic feasible solution. However, we can
easily use the two-phase method. This yields
ai
tt2
1 0
03
04
0
0
a$
b
1
5
446
INTEGER LINEAR PROGRAMMING
which has all nonnegative reduced cost coefficients. Hence, we obtain the
optimal basic feasible solution
51
x
18
11
0u
11
0 u1
T
As expected, the second component does not satisfy the integer constraint.
Next, we write down the Gomory cut for the basic component x\ = 51/11
using the numbers in the second row of the tableau:
1
8
7
Χ4+ Χ5 Χ6=
η
η ~
η'
Updating the tableau gives
ai
G&2 as
1
0
0
0
0
T
0
1
0
0
0
0
0
1
0
0
a±
0
a$
1
a§
0
1
11
1
11
1
11
4
11
3
11
36
11
8
11
21
11
0
0
-1
0
51
11
18
11
7
11
369
11
Again, there is no obvious basic feasible solution. Applying the two-phase
method gives
a\ a<i as a± a§ a6
b
11
33
0
1 0 0
8
8
3
39
0 1 0
0
8
8
9
9
0 0 1
0
2
2
11
7
0 0 0
1
T
1
n
21
255
r
0 0 0
0
8
The corresponding optimal basic feasible solution still does not satisfy the
integer constraints; neither the first nor the second components are integer.
Next, we introduce the Gomory cut using the numbers in the second row
of the previous tableau to obtain
T
a\
ci2
03
CI4
a§
a§
1
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
-i
0
±±
1
8
1
2
1
8
1
8
1
8
0
0
1
0
0
3
8
9
2
11
8
5
8
21
8
a?
b
O f
0
0
0
-1
0
39
8
9
2
7
8
7
8
255
8
EXERCISES
447
Applying the two-phase method again gives
a\
τ
1
0
0
0
0
0
α<ι α^
0
1
0
0
0
0
α±
α§
ÖLQ
CLQ
0
0
0
1
0
0
1
0
0
0
0
1
0
0
0
1
0
0
0
_i
2
_7
2
5
2
_1
2
1
α7
0
1
2
1
2
11
2
1
2
2
b
5
4
1
7
0
31
(Note that this basic feasible solution is degenerate—the corresponding basis
is not unique.) The corresponding optimal basic feasible solution is
5
4
1 7
0
-iT
0
0 ,
which satisfies the integer constraints. From this, we see that the integer
optimal solution to the original ILP problem is [5,4] T , which agrees with our
graphical solution in Figure 19.3.
In this example, we note that the final solution to LP problem after introducing slack variables and using the Gomory cutting-plane method is an
integer vector. The reason for this, in contrast with Example 19.5, is that the
original ILP inequality constraint data has only integers.
I
A linear programming problem in which not all of the components are required to be integers is called a mixed integer linear programming (MILP)
problem. Gomory cuts are also relevant to solving MILP problems. In fact,
Example 19.5 is an instance of an MILP problem, because the slack variables
in the standard form of the problem are not constrained to be integers. Moreover, the cutting-plane idea also has been applied to nonsimplex methods and
nonlinear programming algorithms.
For other methods for solving ILPs, see [119].
EXERCISES
19.1 Show that if A is totally unimodular, then so is any submatrix of it.
19.2 Show that if A is totally unimodular, then so is A .
19.3 Show that A is totally unimodular if and only [A, I] is totally unimodular. This result is stronger than Proposition 19.1.
19.4 Consider the matrix B in the proof of Proposition 19.1:
r>
I Hm—k,k
Bk,k
J-m—k
O
448
INTEGER LINEAR PROGRAMMING
Show that det B = ± d e t Bk,k19.5 Consider the constraint
Ax < 6,
xezn
where A and b contain only integers. Suppose that we introduce the slackvariable vector z to obtain the equivalent constraint
[A,T\
xez71
z>0.
Show that if z satisfies this constraint (with some x), then z is an integer
vector.
19.6 Write a MATLAB program to generate Figures 19.1 and 19.2.
19.7 Consider the constraint in standard form Ax = b. Suppose that we
augment this with a Gomory cut to obtain
x
Xn+l
= b.
Let x n +i satisfy this constraint with an integer vector x. Show that xn+i is
an integer.
19.8 Consider the ILP problem in standard form
minimize
subject to
cTx
Ax = b
x>0
xezn.
Show that if we use the Gomory cutting-plane method with the simplex algorithm, then the final optimal basic feasible solution, including the variables
introduced by the Gomory method, is an integer vector. (Use Exercise 19.7.)
19.9 Consider the ILP problem
minimize
subject to
~τ■
c x
Ax < b
x>0
xezn.
EXERCISES
449
Suppose that we introduce slack variables to convert the problem into standard form, and we use the Gomory cutting-plane method with the simplex
algorithm to solve the resulting problem. Show that the final optimal basic
feasible solution, including the slack variables and the variables introduced by
the Gomory method, is an integer vector. (Use Exercises 19.5 and 19.8.)
19.10 Use a graphical method to find an integer solution to the dual of the
ILP problem in Example 19.5.
PART IV
NONLINEAR CONSTRAINED
OPTIMIZATION
CHAPTER 20
PROBLEMS WITH EQUALITY
CONSTRAINTS
20.1
Introduction
In this part we discuss methods for solving a class of nonlinear constrained
optimization problems that can be formulated as
minimize
f(x)
subject to
n
hi(x) = 0,
i = 1,..., m
9j(x) < 0 ,
j = l,...,p,
n
n
where x e R , / : R -> R, ft» : R -> R, ga : R n -> R, and m < n.
In vector notation, the problem above can be represented in the following
standard form:
minimize
subject to
f(x)
h(x) = 0
9(x) < 0,
where h : R
terminology.
n
-> R
m
and g : R
n
-> W.
As usual, we adopt the following
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
453
454
PROBLEMS WITH EQUALITY CONSTRAINTS
Definition 20.1 Any point satisfying the constraints is called a feasible point
The set of all feasible points,
{x e Rn : h(x) = 0, g(x) < 0},
is called a feasible set
I
Optimization problems of the above form are not new to us. Indeed, linear
programming problems of the form
minimize
subject to
cTx
Ax — b
x > 0,
which we studied in Part III, are of this type.
As we remarked in Part II, there is no loss of generality by considering only
minimization problems. For if we are confronted with a maximization problem, it can easily be transformed into the minimization problem by observing
that
maximize/(x) = minimize—/(x).
We illustrate the problems we study in this part by considering the following
simple numerical example.
Example 20.1 Consider the following optimization problem:
minimize
subject to
{x\ — l ) 2 + #2 — 2
X2 — x\ = 1,
Xi + %2 < 2.
This problem is already in the standard form given earlier, with f(xi,X2) =
(xi - l ) 2 + X2 - 2, h(xi,x2) = #2 — #i - 1, and g(xi,X2) = #i + #2 2. This problem turns out to be simple enough to be solved graphically
(see Figure 20.1). In the figure the set of points that satisfy the constraints
(the feasible set) is marked by the heavy solid line. The inverted parabolas
represent level sets of the objective function /—the lower the level set, the
smaller the objective function value. Therefore, the solution can be obtained
by finding the lowest-level set that intersects the feasible set. In this case, the
minimizer lies on the level set with / = —1/4. The minimizer of the objective
function is x* = [1/2,3/2] T .
I
In the remainder of this chapter we discuss constrained optimization problems with only equality constraints. The general constrained optimization
problem is discussed in the chapters to follow.
PROBLEM FORMULATION
Figure 20.1
20.2
455
Graphical solution to the problem in Example 20.1.
Problem Formulation
The class of optimization problems we analyze in this chapter is
minimize
subject to
f(x)
h(x) = 0,
where x G R n , / : R n -► R, h : R n -» R m , h = [hu · ·, ^m] T , and m < n.
We assume that the function h is continuously differentiable, that is, h G C 1 .
We introduce the following definition.
Definition 20.2 A point x* satisfying the constraints
fti(x*)
=
0 , . . . , hm(x*) = 0 is said to be a regular point of the constraints if the gradient
vectors Vfti(a5*),..., Vftm(a;*) are linearly independent.
I
Let Dh(x*) be the Jacobian matrix of h = [/ii,..., hm]T at £C*, given by
"v/ii(aj*) T 1
|"Dfti(x·)"
\Dhm(x*)m
yhm{x*)T\
Then, x* is regular if and only if rank Dh(x*) = m (i.e., the Jacobian matrix
is of full rank).
The set of equality constraints h\(x) = 0 , . . . ,hm(x) = 0, hi : R n —► R,
describes a surface
5 = {x G R n : fti(x) = 0 , . . . , hm(x) - 0}.
456
PROBLEMS WITH EQUALITY CONSTRAINTS
x
1
^x3
X2
S=K[x1lx2fX3]T:x2-^=0}
Figure 20.2
Two-dimensional surface in R3.
Assuming that the points in S are regular, the dimension of the surface S is
n — m.
Example 20.2 Let n = 3 and m = 1 (i.e., we are operating in R 3 ). Assuming
that all points in S are regular, the set 5 is a two-dimensional surface. For
example, let
hi(x) = X2 — x\ = 0.
Note that Vhi(x)
this case,
= [0,1, - 2 x 3 ] T , and hence for any
XGR3,
Vfei(x) φ 0. In
dim S = dim{x : hi (x) — 0} = n — m = 2.
See Figure 20.2 for a graphical illustration.
I
Example 20.3 Let n = 3 and m — 2. Assuming regularity, the feasible set
5 is a one-dimensional object (i.e., a curve in R 3 ). For example, let
hi(x) = xi,
h2(x) — X2- #!·
In this case, Vhi(a?) = [1,0,0] T and Vh2(x) = [0, l , - 2 x 3 ] T . Hence, the
vectors Vfti(x) and V/i2(ic) are linearly independent in R 3 . Thus,
d i m 5 = dim{ic : h\(x) = 0, h2{x) = 0} = n — m = 1.
See Figure 20.3 for a graphical illustration.
20.3
I
Tangent a n d N o r m a l Spaces
In this section we discuss the notion of a tangent space and normal space at
a point on a surface. We begin by defining a curve on a surface S.
TANGENT AND NORMAL SPACES
Figure 20.3
457
One-dimensional surface in R3.
Definition 20.3 A curve C on a surface 5 is a set of points {x(t) G S : t G
(a, 6)}, continuously parameterized by t G (a, 6); that is, x : (a, b) —» S is a
continuous function.
|
A graphical illustration of the definition of a curve is given in Figure 20.4.
The definition of a curve implies that all the points on the curve satisfy the
equation describing the surface. The curve C passes through a point cc* if
there exists t* G (a, b) such that x(t*) = x*.
Intuitively, we can think of a curve C = {x(t) : t £ (a, b)} as the path
traversed by a point x traveling on the surface S. The position of the point
at time t is given by x(t).
Definition 20.4 The curve C = {x(t) : t G (a, b)} is differentiable if
. , x dx , x
»(*) = -*(«> =
ii(f)l
Xn(t)\
exists for all t G (a, b).
Figure 20.4
Curve on a surface.
458
PROBLEMS WITH EQUALITY CONSTRAINTS
x(b)
(t)
"OurveC
x(a)
Figure 20.5
Geometric interpretation of the differentiability of a curve.
The curve C = {x{t) : t G (a, b)} is twice differentiable if
*»=$<«=
Xn(t)\
exists for all t G (a, b).
I
Note that both x(t) and x(t) are n-dimensional vectors. We can think
of x(t) and x(t) as the velocity and acceleration, respectively, of a point
traversing the curve C with position x(t) at time t. The vector x{t) points in
the direction of the instantaneous motion of x(t). Therefore, the vector x(t*)
is tangent to the curve C at x* (see Figure 20.5).
We are now ready to introduce the notions of a tangent space. For this
recall the set
S = {x G R n : h(x) = 0},
where h € C1. We think of 5 as a surface in W1.
Definition 20.5 The tangent space at a point x* on the surface S = {x G
R n : h(x) = 0} is the set T(x*) = {?/ : Dh(x*)y = 0}.
I
Note that the tangent space T(x*) is the nullspace of the matrix Dh(x*):
T(x*)=Af(Dh(x*)).
The tangent space is therefore a subspace of R n .
Assuming that x* is regular, the dimension of the tangent space is n — m,
where m is the number of equality constraints hi(x*) = 0. Note that the
tangent space passes through the origin. However, it is often convenient to
picture the tangent space as a plane that passes through the point x*. For
this, we define the tangent plane at x* to be the set
TP(x*) = T(x*) + x* = {x + x* : x G T(x*)}.
TANGENT AND NORMAL SPACES
459
Tangent Plane
Figure 20.6
Tangent plane to the surface S at the point x*.
Figure 20.6 illustrates the notion of a tangent plane, and Figure 20.7, the
relationship between the tangent plane and the tangent space.
Example 20.4 Let
S = { x G R 3 : fti(sc) = xi = 0, h2(x) = xi - x2 = 0}.
Then, S is the a^-axis in R 3 (see Figure 20.8). We have
Dh(x) =
Vfti(») T "
Wh2(x)T
1
1
0
-1
0
0
Because Vfti and Vh2 are linearly independent when evaluated at any x G 5,
all the points of S are regular. The tangent space at an arbitrary point of S
is
T(x) = {y : V h i ( x ) T y - 0, Vh2(x)Ty
= 0}
v
2/·
1 o
1 -1
= {[0,0,α]
τ
o"
0
2/i
2/2
_2/3_
= 0>
)
:aeR}
— the Xß-axis in R 3 .
In this example, the tangent space T(x)
dimensional subspace of R 3 .
at any point x G S is a oneI
Intuitively, we would expect the definition of the tangent space at a point
on a surface to be the collection of all "tangent vectors" to the surface at that
460
PROBLEMS WITH EQUALITY CONSTRAINTS
Figure 20.7
Tangent spaces and planes in R2 and M3.
point. We have seen that the derivative of a curve on a surface at a point is
a tangent vector to the curve, and hence to the surface. The intuition above
agrees with our definition whenever x* is regular, as stated in the theorem
below.
Theorem 20.1 Suppose that x* e S is a regular point and T(x*) is the
tangent space at x*. Then, y G T(x*) if and only if there exists a differentiable
curve in S passing through x* with derivative y at x*.
D
Proof <=: Suppose that there exists a curve {x(t) : t G (a, b)} in S such that
x(t*) = x* and x(t*) = y for some t* G (a, b). Then,
h(x(t)) = 0
TANGENT AND NORMAL SPACES
461
AX3
1
\
1
1
1
1
^ —
Vh 2 (x)'
Vh^x)
Figure 20.8
\
\
s
N
/
\
\
*T(x)
h 1= 0
^
~7o\
X
h2=o\
The surface S = {x G R3 : xi = 0, xi - x2 = 0}.
for all t G (a, 6). If we differentiate the function h(x(t))
using the chain rule, we obtain
d
at
h(x(t)) = Dh(x(t))x(t)
with respect to £
= 0
for all t G (a, b). Therefore, at t* we get
Dh{x*)y
= 0,
and hence y G T(x*).
=>: To prove this, we need to use the implicit function theorem. We refer
the reader to [88, p. 325].
I
We now introduce the notion of a normal space.
Definition 20.6 The normal space N(x*) at a point x* on the surface S =
{xeRn
: h(x) = 0} is the set N(x*) = {x G R n : x = Dh(x*)T z, z G R m } .
We can express the normal space N(x*) as
N(x*)=U(Dh{x*)T),
that is, the range of the matrix Dh(x*)T. Note that the normal space N(x*)
is the subspace of R n spanned by the vectors V/u(cc*),..., V/i m (x*); that is,
N(x*) = span[Vfti(x*),..., Vftm(a;*)]
= { x G R n : x = ^iVhi(a;*) + --- + ^ m V/i m (a;*), zu...,zm
G R}.
462
PROBLEMS WITH EQUALITY CONSTRAINTS
Normal space in R3.
Figure 20.9
Note that the normal space contains the zero vector. Assuming that x*
is regular, the dimension of the normal space N(x*) is ra. As in the case of
the tangent space, it is often convenient to picture the normal space N(x*)
as passing through the point x* (rather than through the origin of Rn). For
this, we define the normal plane at x* as the set
NP(x*)
= N(x*) + x* = {x + x* G R n : x G AT(x*)}.
Figure 20.9 illustrates the normal space and plane in R 3 (i.e., n — 3 and
m = l).
We now show that the tangent space and normal space are orthogonal
complements of each other (see Section 3.3).
Lemma 20.1 We have T(x*) = N(x*)±
and T(x*) ± = N(x*).
D
Proof. By definition of T(x*), we may write
T(x*) = {yeRn:
Hence, by definition of N(x*),
we also have T{x*)^ = N(x*).
xTy
= 0 for all x G N(x*)}.
we have T{x*) = N(x"f)±.
By Exercise 3.11
I
By Lemma 20.1, we can write Rn as the direct sum decomposition (see
Section 3.3):
Μη = ΛΓ(χ*)ΘΤ(ίΕ*);
that is, given any vector v G Mn, there are unique vectors w G N(x*) and
y G T(x*) such that
v = w + y.
LAGRANGE CONDITION
20.4
463
Lagrange Condition
In this section we present a first-order necessary condition for extremum problems with constraints. The result is the well-known Lagrange's theorem. To
better understand the idea underlying this theorem, we first consider functions of two variables and only one equality constraint. Let h : R 2 —► R be the
constraint function. Recall that at each point x of the domain, the gradient
vector Wh(x) is orthogonal to the level set that passes through that point.
Indeed, let us choose a point
] ' such that h(x*) = 0, and assume
that Vft(x*) Φ 0. The level set through the point x* is the set {x : h{x) = 0}.
We then parameterize this level set in a neighborhood of x* by a curve {#(£)},
that is, a continuously differentiate vector function x : R —» R 2 such that
x(t)
xi(t)
X2{t)
t G (a, 6),
x* = x(**), x(t*) φ 0, t* G (a, 6).
We can now show that Vft(a?*) is orthogonal to x(t*). Indeed, because h is
constant on the curve {x(i) : t G (a, 6)}, we have that for all t G (a, 6),
h(x(t)) = 0.
Hence, for all t G (a, 6),
fth(x(t))=0.
Applying the chain rule, we get
4-h(x{t))
at
= Vh{x(t))Tx{t)
= 0.
Therefore, V/i(a?*) is orthogonal to x(t*).
Now suppose that x* is a minimizer of / : R 2 —> R on the set {x : ft(x) =
0}. We claim that V/(x*) is orthogonal to ώ(ί*). To see this, it is enough to
observe that the composite function of t given by
4>{t) = f(x(t))
achieves a minimum at t*. Consequently, the first-order necessary condition
for the unconstrained extremum problem implies that
Applying the chain rule yields
0 = ±φ{ΐ) = ν/(χ(ί')) τ χ(ί·) = Vf(x*)Tx(t*).
Thus, V/(x*) is orthogonal to x(t*). The fact that x(t*) is tangent to the
curve {x(t)} at x* means that V/(x*) is orthogonal to the curve at x* (see
Figure 20.10).
464
PROBLEMS WITH EQUALITY CONSTRAINTS
Figure 20.10 The gradient V/(x*) is orthogonal to the curve {x(t)} at the point
x* that is a minimizer of / on the curve.
Recall that Vh(x*) is also orthogonal to x(t*). Therefore, the vectors
Vh(x*) and V/(x*) are parallel; that is, Vf(x*) is a scalar multiple of
Vh(x*). The observations above allow us now to formulate Lagrange's theorem for functions of two variables with one constraint.
Theorem 20.2 Lagrange's Theorem for n = 2, m = 1. Let the point x*
be a minimizer of f : R 2 —> R subject to the constraint h(x) = 0, h : R 2 —► R.
TAen, V/(x*) and Vft(x*) are parallel. That is, ifVh(x*) φ 0, ίΛβη ί/iere
exists a scalar λ* 5?/c/i that
V/(x*) + A*V/i(x*) = 0.
D
In Theorem 20.2, we refer to λ* as the Lagrange multiplier. Note that
the theorem also holds for maximizers. Figure 20.11 gives an illustration of
Lagrange's theorem for the case where x* is a maximizer of / over the set
{x : h(x) = 0}.
Lagrange's theorem provides a first-order necessary condition for a point
to be a local minimizer. This condition, which we call the Lagrange condition,
consists of two equations:
ν / ( χ * ) + λ*ν/ι(**) = 0
h(x*) = 0.
Note that the Lagrange condition is necessary but not sufficient. In Figure 20.12 we illustrate a variety of points where the Lagrange condition is
LAGRANGE CONDITION
465
z=f(x1,x2)
yh(x*)
Figure 20.11
Lagrange's theorem for n — 2, m = 1.
satisfied, including a case where the point is not an extremizer (neither a
maximizer nor a minimizer).
We now generalize Lagrange's theorem for the case when / : R n —> R and
h : R n -► R m , m < n.
Theorem 20.3 Lagrange's Theorem.
Let x* be a local minimizer (or
maximizer) of f : R n -► R, subject to h(x) = 0, h : R n -► R m , m < n.
Assume that x* is a regular point. Then, there exists λ* G R m 5^c/i that
D/(x*) + A*TD/i(ai*) = 0 T .
Proof. We need to prove that
V/(x*) =
-Dh(x*)T\*
for some λ* G R m ; that is, V/(a?*) G ft(£>ft(x*)T) = AT(x*). But
by Lemma 20.1, N(x*) — T(x*)±.
Therefore, it remains to show that
V/(**) G T ( * * ) x .
We proceed as follows. Suppose that
y e TV).
Then, by Theorem 20.1, there exists a differentiable curve {x(t) : t G (a, b)}
such that for all t G (a, 6),
Λ(®(ί)) = 0,
466
PROBLEMS WITH EQUALITY CONSTRAINTS
y f(x*>
h=0
h=0
(a)
(b)
AVh(x*)
h=0
(c)
(d)
Figure 20.12
Four examples where the Lagrange condition is satisfied: (a)
maximizer, (b) minimizer, (c) minimizer, (d) not an extremizer. (Adapted from [120].)
and there exists t* G (a, b) satisfying
x(t*) = x*, x(t*) = y.
Now consider the composite function φ(ί) = f(x(t)). Note that t* is a local
minimizer of this function. By the first-order necessary condition for unconstrained local minimizers (see Theorem 6.1),
Applying the chain rule yields
^ ( f ) = Df{x')x(f)
So all y 6 T(x*) satisfy
= Df(x*)y
V / ( z * ) T y = 0;
= Vf{x*)Ty
- 0.
LAGRANGE CONDITION
467
h=0
Vh(x*)
Figure 20.13
Example where the Lagrange condition does not hold.
that is,
v/(a:*) G T(x*y
This completes the proof.
Lagrange's theorem states that if x* is an extremizer, then the gradient
of the objective function / can be expressed as a linear combination of the
gradients of the constraints. We refer to the vector λ* in Theorem 20.3 as the
Lagrange multiplier vector, and its components as Lagrange multipliers.
From the proof of Lagrange's theorem, we see that a compact way to write
the necessary condition is V/(x*) G N(x*). If this condition fails, then x*
cannot be an extremizer. This situation is illustrated in Figure 20.13.
Notice that regularity is stated as an assumption in Lagrange's theorem.
This assumption plays an essential role, as illustrated in the following example.
Example 20.5 Consider the following problem:
minimize
subject to
f(x)
h(x) = 0,
where f(x) = x and
f 2
x
h(x) = < 0
(x-l)2
ifx<0
if 0 < x < 1
if x > l .
The feasible set is evidently [0,1]. Clearly, x* = 0 is a local minimizer.
However, f'(x*) = 1 and h'(x*) = 0. Therefore, x* does not satisfy the
468
PROBLEMS WITH EQUALITY CONSTRAINTS
necessary condition in Lagrange's theorem. Note, however, that x* is not a
regular point, which is why Lagrange's theorem does not apply here.
I
It is convenient to introduce the Lagrangian function I : Mn x R m —> R,
given by
l{x,X) = f(x) + XTh(x).
The Lagrange condition for a local minimizer x* can be represented using the
Lagrangian function as
Dl(x*,\*) = 0T
for some λ*, where the derivative operation D is with respect to the entire
argument [ χ τ , λ ] τ . In other words, the necessary condition in Lagrange's
theorem is equivalent to the first-order necessary condition for unconstrained
optimization applied to the Lagrangian function.
To see the above, denote the derivative of I with respect to x as Dxl and
the derivative of / with respect to λ as D\l. Then,
Dl{x, X) = [DJ(x, \),Dxl(x,
X)].
Note that Dxl(x, X) = Df(x)+XTDh(x)
and D\l(x, X) = h(x)T.
Lagrange's theorem for a local minimizer x* can be stated as
DJ{x*,X*)
= 0T,
Dxl(x*,X*)
= 0T
Therefore,
for some λ*, which is equivalent to
Dl{x*,X*) = 0T.
In other words, the Lagrange condition can be expressed as Dl(x*,\*) = 0 T .
The Lagrange condition is used to find possible extremizers. This entails
solving the equations
DJ(x,X)
= 0T,
Dxl(x,X)
= 0T.
The above represents n + m equations i n n + m unknowns. Keep in mind
that the Lagrange condition is necessary but not sufficient; that is, a point x*
satisfying the equations above need not be an extremizer.
Example 20.6 Given a fixed area of cardboard, we wish to construct a closed
cardboard box with maximum volume. We can formulate and solve this problem using the Lagrange condition. Denote the dimensions of the box with
maximum volume by #ι, Χ2, and £3, and let the given fixed area of cardboard
be A. The problem can then be formulated as
maximize
χχχ^Χζ
Subject tO
X\X2 + #2^3 + #3#1 = 7Γ-
A
LAGRANGE CONDITION
469
We denote f(x) = —x\x2x^ and h(x) = xiX2-\-x2Xs+XsXi —A/2. We have
V/(a?) = -[χ2Χ3,χιΧζ,χιχ2]τ
and V/i(x) = [x2 +x$,x\ + £3,£i + X2V - Note
that all feasible points are regular in this case. By the Lagrange condition,
the dimensions of the box with maximum volume satisfies
#2^3 - Κχ2 + X3) - 0
£1^3 - λ(Χι + £ 3 ) = 0
£ i £ 2 - A(a?i + £ 2 ) = 0
A
£ l £ 2 + #2^3 + #3^1
~ 2~'
where λ e R.
We now solve these equations. First, we show that that #1, #2, #3, a n d A are
all nonzero. Suppose that Xi = 0. By the constraints, we have #2^3 = A/2.
However, the second and third equations in the Lagrange condition yield
Xx2 = Xx3 = 0, which together with the first equation implies that £2 £3 = 0.
This contradicts the constraints. A similar argument applies to £2 and £3.
Next, suppose that λ = 0. Then, the sum of the three Lagrange equations
gives x2xz + x\x$ + x\X2 = 0, which contradicts the constraints.
We now solve for £1, x2, and £3 in the Lagrange equations. First, multiply
the first equation by x\ and the second by £2, and subtract one from the
other. We arrive at χ^Χ(χι — x2) = 0. Because neither £3 nor λ can be zero
(by part b), we conclude that X\ — x2. We similarly deduce that x2 = £3.
From the constraint equation, we obtain x\ — x2 — £3 = y/A/Q.
Notice that we have ignored the constraints that £1, x2, and £3 are positive
so that we can solve the problem using Lagrange's theorem. However, there
is only one solution to the Lagrange equations, and the solution is positive.
Therefore, if a solution exists for the problem with positivity constraints on
the variables £1, x2, and £3, then this solution must necessarily be equal to
the solution above obtained by ignoring the positivity constraints.
I
Next we provide an example with a quadratic objective function and a
quadratic constraint.
Example 20.7 Consider the problem of extremizing the objective function
f(x) =x\ + x\
on the ellipse
{[£i,£ 2 ] T : h(x) = x\ + 2x\-l
We have
ν/(χ)-[2£!,2£2]Τ,
ν / ι ( χ ) = [2£!,4£ 2 ] τ .
= 0}.
470
PROBLEMS WITH EQUALITY CONSTRAINTS
Thus,
Dxl(x, X) = Dx[f(x)
+ Xh(x)] = [2xi + 2λχι, 2x2 + 4λχ2]
and
D\l(x, X) = h(x) = x\ + 2x\ - 1.
Setting Dxl(x, X) = 0
unknowns
T
and D\l(x, X) = 0, we obtain three equations in three
2xi + 2λχι = 0,
2x 2 + 4λχ 2 = 0,
X\
~~\~ AXo
^
A·
All feasible points in this problem are regular. Prom the first of the equations
above, we get either x\ = 0 or λ = — 1. For the case where X\ = 0, the
second and third equations imply that λ = —1/2 and x 2 = ± l / \ / 2 . For the
case where λ = — 1, the second and third equations imply that x\ = ± 1 and
x2 = 0. Thus, the points that satisfy the Lagrange condition for extrema are
0
,
_l/>/2
x& =
0
-1/^2
,
x& =
1
,
0
xW =
-1
0
Because
and
f(xV)
= f(xW)
= \
f(x(V)
= f(xW)
= l
we conclude that if there are minimizers, then they are located at x^ and
x^2\ and if there are maximizers, then they are located at x^ and x^.
It turns out that, indeed, x^ and x^ are minimizers and x^ and x^
are maximizers. This problem can be solved graphically, as illustrated in
Figure 20.14.
I
In the example above, both the objective function / and the constraint
function h are quadratic functions. In the next example we take a closer look
at a class of problems where both the objective function / and the constraint
h are quadratic functions of n variables.
E x a m p l e 20.8 Consider the following problem:
. .
maximize
xTQx
,
τ η
x' Px
where Q = QT > 0 and P = PT > 0. Note that if a point x = [ # i , . . . , xn]T
is a solution to the problem, then so is any nonzero scalar multiple of it,
tx = [txi,...
)txn]T,
t φ 0.
LAGRANGE CONDITION
Figure 20.14
Indeed,
471
Graphical solution of the problem in Example 20.7.
(tx)TQ(tx)
(tx)TP(tx)
t2xTQx
t2xTPx
xTQx
xTPx
Therefore, to avoid the multiplicity of solutions, we further impose the constraint
xTPx = 1.
The optimization problem becomes
maximize
x
Qx
T
subject to
= 1.
x Px
Let us write
xTQx,
f(x) =
h(x) = 1-
xTPx.
Any feasible point for this problem is regular (see Exercise 20.13). We now
apply Lagrange's method. We first form the Lagrangian function
Z(x, λ) = xTQx
+ λ(1 -
xTPx).
Applying the Lagrange condition yields
DJ{x, X) = 2xTQ - 2XxTP
Dxl(x,X)
= l-xTPx
= 0.
= 0T,
472
PROBLEMS WITH EQUALITY CONSTRAINTS
The first of the equations above can be represented as
Qx - XPx = 0
or
(XP - Q)x = 0.
This representation is possible because P = P and Q = Q . B y assumption
P > 0, hence P _ 1 exists. Premultiplying (XP — Q)x — 0 by P - 1 , we obtain
(λ/ η - Prefix
or, equivalently,
P~lQx
=0
= Xx.
Therefore, the solution, if it exists, is an eigenvector of P _ 1 Q , and the Lagrange multiplier is the corresponding eigenvalue. As usual, let x* and λ* be
the optimal solution. Because x*TPx* = 1 and P _ 1 Q x * = A*cc*, we have
X*
=x*TQx*.
Hence, λ* is the maximum of the objective function, and therefore is, in fact,
the maximal eigenvalue of P~lQ. It is also called the maximal generalized
eigenvalue.
I
In the problems above, we are able to find points that are candidates for
extremizers of the given objective function subject to equality constraints.
These critical points are the only candidates because they are the only points
that satisfy the Lagrange condition. To classify such critical points as minimizers, maximizers, or neither, we need a stronger condition—possibly a necessary and sufficient condition. In the next section we discuss a second-order
necessary condition and a second-order sufficient condition for minimizers.
20.5
Second-Order Conditions
We assume that / : W1 —► M. and h : R n —> R m are twice continuously
differentiable: f,heC2.
Let
l(x, λ) = f(x) + XTh(x) = f(x) + λιΛι(χ) + · · · +
Xmhm(x)
be the Lagrangian function. Let L(x, X) be the Hessian matrix of Z(x, λ) with
respect to x:
L(x, X) = F(x) + λιJii(a;) + · · · +
XmHmix),
where F(x) is the Hessian matrix of / at x and Hk(x)
of hk at x, k = 1 , . . . , m, given by
Hk(x)
is the Hessian matrix
"äif( X )
"'
dx^dxx ( X )
3 ^fc (γ)
...
d hk (γ)
=
I
SECOND-ORDER CONDITIONS
473
We introduce the notation [AJH"(SC)]:
[XH(X)} = Aiffifc) + · · ■ +
\mHm(x).
Using the notation above, we can write
L(x,\)
= F(x) +
[\H(x)}.
Theorem 20.4 Second-Order Necessary Conditions. Let x* be a local
minimizer of f : W1 —► R subject to h(x) = 0, h : Rn —► M m , m < n, and
f,h EC2. Suppose that x* is regular. Then, there exists λ* G Rm such that:
1.
Df(x*)+\*TDh{x*)=0T.
2. For all y G T{x*), we have yTL(x*,\*)y
> 0.
D
Proof. The existence of λ* G R m such that Df(x*) + \*TDh(x*)
= 0T
follows from Lagrange's theorem. It remains to prove the second part of the
result. Suppose that y G T(x*); that is, y belongs to the tangent space to
S = {x G W1 : h(x) = 0} at x*. Because h G C2, following the argument of
Theorem 20.1, there exists a twice-differentiable curve {x(t) : t G (a, b)} on 5
such that
a;(t*)=a;*, i(t*) = y
for some i* G (a, 6). Observe that by assumption, t* is a local minimizer of
the function <j>(t) — f(x(t)). Prom the second-order necessary condition for
unconstrained minimization (see Theorem 6.2), we obtain
Using the formula
±{y{t)Tz(t))=z(t)^(t)
+ y(t)T^(t)
and applying the chain rule yields
= x{t*)T F(x*)x(t*) +
T
= y F(x*)y
Df{x*)x(t*)
+ Df(x*)x(t*)
Because h(x(t)) = 0 for all t € (a, b), we have
^\*Th(x(t))
= 0.
> 0.
474
PROBLEMS WITH EQUALITY CONSTRAINTS
Thus, for all t G (a, 6),
έλ·τ"Μ'»=!
~ dt
\*T-h(x(t))
J2\*k-hk(x(t))
Lfc=i
£
dt
J2\*kDhk(x(t))x(t)
.k=l
tlX^(Dhk(x(t))x(t))
=
fe=l
J^K
[x(t)THk(x(t))x(t)
+
Dhk(x(t))x(t)]
fe=l
= xT(t)\X*H(x(t))}x(t)
= 0.
\*TDh{x(t))x{t)
+
In particular, the above is true for t = t*; that is,
yT[X*H(x*)]y
+ X*T Dh(x*)x(t*)
= 0.
Adding this equation to the inequality
yTF(x*)y
+
Df(x*)x(t*)>0
yields
yT (F(x*) + [λ*Η(χ·)]) y + (Df(x*) + \*TDh(x*))x(t*)
But, by Lagrange's theorem, Df(x*) + X*TDh(x*)
y T (F(x*) + [\*H(x*)})y
which proves the result.
> 0.
— 0 T . Therefore,
= yTL{x\\*)y
> 0,
I
Observe that L(x, X) plays a similar role as the Hessian matrix F(x) of the
objective function / did in the unconstrained minimization case. However,
we now require that L{x*, λ*) > 0 only on T(x*) rather than on Rn.
The conditions above are necessary, but not sufficient, for a point to be a
local minimizer. We now present, without a proof, sufficient conditions for a
point to be a strict local minimizer.
Theorem 20.5 Second-Order
Sufficient
Conditions.
Suppose that
f,h eC2 and there exists a point x* G W1 and λ* G R m such that:
1. D/(x*) + A * T D / i ( : z * ) = 0 T .
SECOND-ORDER CONDITIONS
we have yTL(x*,X*)y
2. For all y e T(x*), y^O,
475
> 0.
Then, x* is a strict local minimizer of f subject to h(x) = 0.
D
Proof. The interested reader can consult [88, p. 334] for a proof of this result.
Theorem 20.5 states that if an cc* satisfies the Lagrange condition, and
Ζ/(χ*,λ*) is positive definite on T(x*), then x* is a strict local minimizer.
A similar result to Theorem 20.5 holds for a strict local maximizer, the only
difference being that L(x*,\*) be negative definite on T(x*). We illustrate
this condition in the following example.
Example 20.9 Consider the following problem:
ιιια,Λ.11U 1
where
Q
xTQx
xTPx'
~
4 θ"
0 1
P =
2 0
0 1
As pointed out earlier, we can represent this problem in the equivalent form
maximize
x
Qx
T
subject to
x Px = 1.
The Lagrangian function for the transformed problem is given by
l(x, X) = xTQx
+ λ(1 -
xTPx).
The Lagrange condition yields
( A J - P _ 1 Q ) x = 0,
where
PlQ
=
2 0
0 1
There are only two values of λ that satisfy (XI — P~lQ)x = 0, namely, the
eigenvalues of P~XQ: X\ — 2, X2 — 1. We recall from our previous discussion
of this problem that the Lagrange multiplier corresponding to the solution is
the maximum eigenvalue of P~1Q^ namely, λ* = λι = 2. The corresponding
eigenvector is the maximizer—the solution to the problem. The eigenvector
corresponding to the eigenvalue λ* = 2 satisfying the constraint xTPx = 1
is ±x*, where
476
PROBLEMS WITH EQUALITY CONSTRAINTS
At this point, all we have established is that the pairs (±χ*,λ*) satisfy
the Lagrange condition. We now show that the points ±x* are, in fact, strict
local maximizers. We do this for the point x*. A similar procedure applies to
—x*. We first compute the Hessian matrix of the Lagrangian function. We
have
L(x*,\*)
v
y
p
= 2Q-2XP=
The tangent space T(x*) to {x : 1 — xTPx
= {y:y
Note that for each y e T(x*),
.
= 0} is
T(x*) = {y e R 2 : x*JPy
= {y:[V2,0]y
°
10 —2J
= 0}
= 0}
= [0,a]T, a G R}.
2/^0,
yT£(a*,A*)y=[0,a]
0
0
0
-2
-2a 2 < 0.
Hence, Χ(χ*,λ*) < 0 on T(x*), and thus x* = [l/>/2,0] T is a strict local
maximizer. The same is true for the point —x*. Note that
x*TQx*
x*TPx*
= 2
which, as expected, is the value of the maximal eigenvalue of P~1Q. Finally,
we point out that any scalar multiple tx* of as*, t φ 0, is a solution to the
original problem of maximizing xTQx/xTPx.
I
20.6
Minimizing Quadratics Subject to Linear Constraints
Consider the problem
. . .
minimize
subject to
1
-x
τ^Qx
Ax = 6,
where Q > 0, A £ R m X n , m < n, rank A = m. This problem is a special
case of what is called a quadratic programming problem (the general form of
a quadratic programming problem includes the constraint x > 0). Note that
the constraint set contains an infinite number of points (see Section 2.3). We
now show, using Lagrange's theorem, that there is a unique solution to the optimization problem above. Following that, we provide an example illustrating
the application of this solution to an optimal control problem.
MINIMIZING QUADRATICS SUBJECT TO LINEAR CONSTRAINTS
477
To solve the problem, we first form the Lagrangian function
+ λτ(6 -
/(», λ) = -xTQx
Ax).
The Lagrange condition yields
DJ{x*, X*) = x*TQ - λ * τ A = 0 T .
Rewriting, we get
x*
=Q~1AT\*.
Premultiplying both sides of the above by A gives
=AQ1AT\*.
Ax*
Using the fact that Ax* = 6, and noting that AQ~1AT
Q > 0 and rank A = m, we can solve for λ* to obtain
is invertible because
(AQ'1AT)'1b.
λ* =
Therefore, we obtain
x* =
Q-1AT(AQ-1AT)-1b.
The point x* is the only candidate for a minimizer. To establish that x* is
indeed a minimizer, we verify that x* satisfies the second-order sufficient conditions. For this, we first find the Hessian matrix of the Lagrangian function
at (a:*,A*). We have
L{x*,\*) = Q,
which is positive definite. Thus, the point x* is a strict local minimizer. We
will see in Chapter 22 that x* is, in fact, a global minimizer.
The special case where Q = J n , the n x n identity matrix, reduces to the
problem considered in Section 12.3. Specifically, the problem in Section 12.3
is to minimize the norm ||x|| subject to Ax = b. The objective function
here is f(x) = \\x\\, which is not differentiable at x = 0. This precludes
the use of Lagrange's theorem because the theorem requires differentiability
of the objective function. We can overcome this difficulty by considering an
equivalent optimization problem:
minimize
subject to
-||#||2
Ax = b.
The objective function ||a?||2/2 has the same minimizer as the previous objective function ||x||. Indeed, if x* is such that for all x G R n satisfying Ax = 6,
||^*|| < II&H, then ||ic*||2/2 < ||x|| 2 /2. The same is true for the converse.
Because the problem of minimizing ||x|| 2 /2 subject to Ax = b is simply the
478
PROBLEMS WITH EQUALITY CONSTRAINTS
problem considered above with Q — I n , we easily deduce the solution to be
x* = A (AA ) _ 1 6 , which agrees with the solution in Section 12.3.
Example 20.10 Consider the discrete-time linear system model
+ buk,
xk = axk-i
k > 1,
with initial condition XQ given. We can think of {xk} as a discrete-time signal
that is controlled by an external input signal {uk}. In the control literature,
Xk is called the state at time k. For a given XQ, our goal is to choose the control
signal {uk} so that the state remains "small" over a time interval [l,iV], but
at the same time the control signal is "not too large." To express the desire
to keep the state {xk} small, we choose the control sequence to minimize
1
N
On the other hand, maintaining a control signal that is not too large, we
minimize
The two objectives above are conflicting in the sense that they cannot, in
general, be achieved simultaneously—minimizing the first may result in a large
control effort, while minimizing the second may result in large states. This is
clearly a problem that requires compromise. One way to approach the problem
is to minimize a weighted sum of the two functions above. Specifically, we
can formulate the problem as
minimize
subject to
1 N
- 2_\ {QX1 +
ru
Tj
Xk = axk-i + buk, k = 1 , . . . , TV, x 0 given,
where the parameters q and r reflect the relative importance of keeping the
state small versus keeping the control effort not too large. This problem is an
instance of the linear quadratic regulator (LQR) problem (see, e.g., [15], [20],
[85], [86], or [99]). Combining the two conflicting objectives of keeping the
state small while keeping the control effort small is an instance of the weighted
sum approach (see Section 24.4).
MINIMIZING QUADRATICS SUBJECT TO LINEAR CONSTRAINTS
479
To solve the problem above, we can rewrite it as a quadratic programming
problem. Define
Q =
QIN
O
O
rIN
' 1
-a
-a
axo
0
o
-6
:
I
0
b =
...
1
-b
0
z = [XI,...,XN,UI,...,UN]T
.
0
With these definitions, the problem reduces to the previously considered
quadratic programming problem,
. . .
minimize
1 τ Qz
^
-z
subject to
Az — 6,
where Q is 2N x 27V, A is N x 27V, and b e RN. The solution is
z*
1-1
ΛΤ L [ - 1
l L
=QA (AQ-LAΛΤ\-Ι,
)- b.
The first N components of z* represent the optimal state signal in the interval
[1, N], whereas the second iV components represent the optimal control signal.
In practice, computation of the matrix inverses in the formula for z* above
may be too costly. There are other ways to tackle the problem by exploiting
its special structure. This is the study of optimal control (see, e.g., [15], [20],
[85], [86], or [99]).
I
The following example illustrates an application of the above discussion.
Example 20.11 Credit-Card Holder Dilemma. Suppose that we currently
have a credit-card debt of $10,000. Credit-card debts are subject to a monthly
interest rate of 2%, and the account balance is increased by the interest
amount every month. Each month we have the option of reducing the account balance by contributing a payment to the account. Over the next 10
months, we plan to contribute a payment every month in such a way as to minimize the overall debt level while minimizing the hardship of making monthly
payments.
480
PROBLEMS WITH EQUALITY CONSTRAINTS
g15000
c
JS
(0
~10000
3
o
* 5000
5
6
Month
10
3000
5
6
Month
Figure 20.15
Plots for Example 20.11 with q = 1 and r = 10.
We solve our problem using the LQR framework described in Example 20.10. Let the current time be 0, Xk the account balance at the end
of month fc, and Uk our payment in month k. We have
xk = 1.02zfc_i -uk,
k = 1 , . . . , 10;
that is, the account balance in a given month is equal to the account balance
in the previous month plus the monthly interest on that balance minus our
payment that month. Our optimization problem is then
minimize
subject to
1
10
Ö Σ faXi +
i=l
rU
i)
Xk = 1.02xfc_i — Uk, k = 1 , . . . , 10, x0 = 10,000,
which is an instance of the LQR problem. The parameters q and r reflect
our priority in trading off between debt reduction and hardship in making
payments. The more anxious we are to reduce our debt, the larger the value
of q relative to r. On the other hand, the more reluctant we are to make
payments, the larger the value of r relative to q.
The solution to the problem above is given by the formula derived in Example 20.10. In Figure 20.15 we plot the monthly account balances and payments
over the next 10 months using q = 1 and r = 10. We can see here that our
debt has been reduced to less than $1000 after 10 months, but with a first
EXERCISES
2
481
lllllllllll
0
"0
1
2
3
4
5 Month
6
7
8
9
10
3000
Figure 20.16
Plots for Example 20.11 with q = 1 and r = 300.
payment close to $3000. If we feel that a payment of $3000 is too high, then
we can try to reduce this amount by increasing the value of r relative to q.
However, going too far along these lines can lead to trouble. Indeed, if we use
q = 1 and r = 300 (see Figure 20.16), although the monthly payments do not
exceed $400, the account balance is never reduced by much below $10,000. In
this case, the interest on the account balance eats up a significant portion of
our monthly payments. In fact, our debt after 10 months will be higher than
$10,000.
■
For a treatment of optimization problems with quadratic objective functions, subject to linear or quadratic constraints, arising in communication and
signal processing, see [105] and [106].
EXERCISES
20.1 Consider the following constraints on R 2 :
h(xux2)
= (xi - 2) 2 = 0
and
g(xi,x2)
= (x2 + I ) 3 < 0.
Find the set of feasible points. Are the feasible points regular? Justify your
answer.
482
PROBLEMS WITH EQUALITY CONSTRAINTS
20.2 Find local extremizers for the following optimization problems:
a.
Minimize
x\ + 2x\x<i + 3x 2 + 4χχ + 5x2 + 6^3
subject to
b.
x\+ 2x2 = 3
4#i + 5x3 = 6.
Maximize
4xi + x\
subject to x\ + x\ = 9.
c.
Maximize
x\x2
subject to x\ + 4#2 = 1·
20.3 Find minimizers and maximizers of the function
f{x) = (aTx)(bTx),
x G R3,
subject to
xi + x2 — 0
X2 + ^3 = 0,
where
Γο"
1
L°
and b =
"l]
0
lj
20.4 Consider the problem
minimize
subject to
f(x)
h(x) = 0,
where / : R 2 -> R, ft : R 2 -> R, and V / ( » ) = [χι,Χι + 4] T . Suppose that x*
is an optimal solution and Vft(x*) = [1,4] T . Find Vf(x*).
20.5 Consider the problem
minimize
subject to
where x0 = [1,
11 x — XQ \ | 2
||x|| 2 = 9,
A/3] T ·
a. Find all points satisfying the Lagrange condition for the problem.
EXERCISES
483
b . Using second-order conditions, determine whether or not each of the
points in part a is a local minimizer.
20.6 We wish to construct a closed box with minimum surface area that
encloses a volume of V cubic feet, where V > 0.
a. Let a, 6, and c denote the dimensions of the box with minimum surface area (with volume V). Derive the Lagrange condition that must be
satisfied by a, b, and c.
b . What does it mean for a point x* to be a regular point in this problem?
Is the point x* = [a,fr,c] T a regular point?
c. Find a, 6, and c.
d. Does the point x* = [a,b,c]T found in part c satisfy the second-order
sufficient condition?
20.7 Find local extremizers of
a. / ( x i , X2, X3) = x\ + 3 x | + xs subject to x\-\-x\-\-x\
b . f(xi,x2)
= 16.
= x\ + x\ subject to 3x\ + 4xix 2 + 6x| = 140.
20.8 Consider the problem
minimize
subject to
2x\ + 3#2 — 4,
x\,x2
GR
£1X2 = 6.
a. Use Lagrange's theorem to find all possible local minimizers and maximizers.
b . Use the second-order sufficient conditions to specify which points are
strict local minimizers and which are strict local maximizers.
c. Are the points in part b global minimizers or maximizers? Explain.
20.9 Find all maximizers of the function
/(xi
'*
x
2)
18x? - 8x1X2 + 12x1
= —2xTT^i—■
20.10 Find all solutions to the problem
3
4
maximize
xT
subject to
||#|| 2 = 1.
0
3 x
484
PROBLEMS WITH EQUALITY CONSTRAINTS
20.11 Consider a matrix A with the property that A A has eigenvalues
ranging from 1 to 20 (i.e., the smallest eigenvalue is 1 and the largest is 20).
Let x be a vector such that ||x|| = 1, and let y = Ax. Use Lagrange multiplier
methods to find the range of values that \\y\\ can take.
Hint: What is the largest value that \\y\\ can take? What is the smallest value
that 11 y \ | can take?
20.12 Consider a matrix A G R m X n . Define the induced 2-norm of A,
denoted ||A||2, to be the number
||A|| 2 = max{||Ax|| : x e R n , ||α|| = 1},
where the norm || · || on the right-hand side above is the usual Euclidean norm.
Suppose that the eigenvalues of A A are λ ι , . . . , λ η (ordered from largest
to smallest). Use Lagrange's theorem to express || A|| 2 in terms of the eigenvalues above (cf. Theorem 3.8).
20.13 Let P = PT be a positive definite matrix. Show that any point x
satisfying 1 — xTPx = 0 is a regular point.
20.14 Consider the problem
maximize
ax\ + bx2,
subject to
x\ + x\ = 2,
X\,%2 £ R
where a,b G R. Show that if [1,1] T is a solution to the problem, then a = b.
20.15 Consider the problem
minimize
X\X2 — 2#i,
subject to
x\ — x\ = 0.
#i,#2 € M
a. Apply Lagrange's theorem directly to the problem to show that if a solution exists, it must be either [1,1] T or [—1,1]T.
b . Use the second-order necessary conditions to show that [—1,1]T cannot
possibly be the solution.
c. Use the second-order sufficient conditions to show that [1,1] T is a strict
local minimizer.
20.16 Let A G R m x n , m < n, rank A = m, and x0 G Rn. Let x* be the
point on the nullspace of A that is closest to XQ (in the sense of Euclidean
norm).
a. Show that x* is orthogonal to x* — XQ.
EXERCISES
485
b . Find a formula for x* in terms of A and XQ.
20.17 Consider the problem
minimize
\\\Ax-b\\*
subject to
Cx = d,
where A G R m x n , m > n, C G R p x n , p < n, and both A and C are of full
rank. We wish to find an expression for the solution (in terms of A, 6, C,
and d).
a. Apply Lagrange's theorem to solve this problem.
b . As an alternative, rewrite the given optimization problem in the form of
a quadratic programming problem and apply the formula in Section 20.6
to obtain the solution.
20.18 Consider the problem of minimizing a general quadratic function subject to a linear constraint:
minimize
subject to
-xTQx
— cTx + d
Ax — 6,
where Q = Q > 0, A G R m x n , m < n, rank A = m, and d is a constant.
Derive a closed-form solution to the problem.
20.19 Let L be an n x n real symmetric matrix, and let M be a subspace
of R n with dimension m < n. Let { & i , . . . , 6 m } C Mn be a basis for M,
and let B be the n x m matrix with bi as the ith column. Let LM be the
mxm matrix defined by LM = B LB. Show that L is positive semidefinite
(definite) on M if and only if LM is positive semidefinite (definite).
Note: This result is useful for checking that the Hessian of the Lagrangian
function at a point is positive definite on the tangent space at that point.
20.20 Consider the sequence {xk}, %k £ ^ generated by the recursion
Xk+i = axk + buki
k >0
(a, b G R, a, b φ 0),
where ^0,^1,^2»··· is a sequence of "control inputs," and the initial condition
#o Φ 0 is given. The recursion above is also called a discrete-time linear
system. We wish to find values of control inputs uo and u\ such that #2 = 0,
and the average input energy (UQ + uf)/2 is minimized. Denote the optimal
inputs by UQ and u\.
486
PROBLEMS WITH EQUALITY CONSTRAINTS
a. Find expressions for UQ and u* in terms of a, 6, and XQ.
b . Use the second-order sufficient conditions to show that the point tx* =
[tio,^*] T in part a is a strict local minimizer.
20.21 Consider the discrete-time linear system Xk = %Xk-i +^fc> k > 1, with
#0 = 1· Find the values of the control inputs U\ and U2 to minimize
2 , 1 2 , 1 2
*2 + 2 M 1 + 3^2·
20.22 Consider the discrete-time linear system x^+i = Xk + 2^^, 0 < A: < 2,
with #o = 3. Use the Lagrange multiplier approach to calculate the optimal
control sequence {uo, ^1,^2} that transfers the initial state XQ to £3 = 9 while
minimizing
! 2
fc=0
CHAPTER 21
PROBLEMS WITH INEQUALITY
CONSTRAINTS
21.1
Karush-Kuhn-Tucker Condition
In Chapter 20 we analyzed constrained optimization problems involving only
equality constraints. In this chapter we discuss extremum problems that also
involve inequality constraints. The treatment in this chapter parallels that of
Chapter 20. In particular, as we shall see, problems with inequality constraints
can also be treated using Lagrange multipliers.
We consider the following problem:
minimize
subject to
f(x)
h(x) = 0,
where / : Rn -> R, h : W1 -+ R m , m < n, and g : Rn -> W. For the general
problem above, we adopt the following definitions.
Definition 21.1 An inequality constraint gj(x) < 0 is said to be active at
x* if 9j{x*) = 0. It is inactive at x* if gj(x*) < 0.
I
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
487
488
PROBLEMS WITH INEQUALITY CONSTRAINTS
By convention, we consider an equality constraint hi(x) = 0 to be always
active.
Definition 21.2 Let x* satisfy h(x*) = 0, g(x*) < 0, and let J(x*) be the
index set of active inequality constraints:
Then, we say that x* is a regular point if the vectors
Vfti(x*),
Vgj(x*),
l<i<ra,
j G J(x*)
are linearly independent.
I
We now prove a first-order necessary condition for a point to be a local
minimizer. We call this condition the Karush-Kuhn-Tucker (KKT) condition.
In the literature, this condition is sometimes also called the Kuhn-Tucker
condition.
T h e o r e m 21.1 Karush-Kuhn-Tucker
(KKT) Theorem. Let f,h,g G
C 1 . Let x* be a regular point and a local minimizer for the problem of minimizing f subject to h(x) = 0, g(x) < 0. Then, there exist λ* G R m and
μ* G Rp such that:
1. μ* > 0.
2. Df(x*) + \*TDh(x*)
S. ß*Tg{x*)
+ ß*TDg{x*)
= 0T.
= 0.
D
In Theorem 21.1, we refer to λ* as the Lagrange multiplier vector and μ* as
the Karush-Kuhn-Tucker (KKT) multiplier vector. We refer to their components as Lagrange multipliers and Karush-Kuhn-Tucker (KKT) multipliers,
respectively.
Before proving this theorem, let us first discuss its meaning. Observe that
μj > 0 (by condition 1) and gj{x*) < 0. Therefore, the condition
μ* τ 0(χ*) = ßlgi(x*)
+ · · ■ + μ^ρί**) = 0
implies that if gj(x*) < 0, then μ^ = 0; that is, for all j 0 J(as*), we have
μ^ = 0. In other words, the KKT multipliers μ!· corresponding to inactive constraints are zero. The other KKT multipliers, μ*, i G J(x*), are nonnegative;
they may or may not be equal to zero.
Example 21.1 A graphical illustration of the KKT theorem is given in Figure 21.1. In this two-dimensional example, we have only inequality constraints
KARUSH-KUHN-TUCKER CONDITION
Figure 21.1
489
Illustration of the Karush-Kuhn-Tucker (KKT) theorem.
9j(&) < 0, j = 1,2,3. Note that the point x* in the figure is indeed a minimizer. The constraint gz(x) < 0 is inactive: g${x*) < 0; hence μ£ = 0. By
the KKT theorem, we have
V/Or*) + μΐν9ι(χ*)
+ μ*2ν92(χ*)
= 0,
or, equivalently,
V/(x*) = -μΐν9ι(χ*)
-
M5V^(X*),
where μ\ > 0 and μ\ > 0. It is easy to interpret the KKT condition graphically for this example. Specifically, we can see from Figure 21.1 that V/(x*)
must be a linear combination of the vectors — S7gi(x*) and — V#2(#*) with
positive coefficients. This is reflected exactly in the equation above, where
the coefficients μ\ and μ\ are the KKT multipliers.
I
We apply the KKT condition in the same way that we apply any necessary
condition. Specifically, we search for points satisfying the KKT condition and
treat these points as candidate minimizers. To summarize, the KKT condition
consists of five parts (three equations and two inequalities):
1. μ* > 0.
2. Df(x*) + \*TDh{x*)
3.
μ*τ9{χ*)=0.
4- ß*TDg(x*)
= 0T.
490
PROBLEMS WITH INEQUALITY CONSTRAINTS
4. fe(x*) = 0.
5. g(x*) < 0.
We now prove the KKT theorem.
Proof of the Karush-Kuhn-Tucker Theorem. Let x* be a regular local minimizer of / on the set {x : h(x) = 0,g(x) < 0}. Then, x* is also a regular
local minimizer of / on the set {x : h(x) = 0,gj(x) = 0,j G J(x*)} (see
Exercise 21.16). Note that the latter constraint set involves only equality
constraints. Therefore, from Lagrange's theorem, it follows that there exist
vectors λ* G R m and μ* G W such that
Df(x*) + X*TDh(x*)
+ ß*TDg(x*)
= 0T,
where for all j 0 J{x*), we have μ!· = 0. To complete the proof it remains to
show that for all j G J(x*), we have μ!· > 0 (and hence for all j = 1 , . . . ,p,
we have μ!· > 0, i.e., μ* > 0). We use a proof by contradiction. So suppose
that there exists j G J(x*) such that μ!· < 0. Let S and T(x*) be the surface
and tangent space defined by all other active constraints at x*. Specifically,
S = {x : h(x) = 0, gi(x) = 0, i G J(x*),i
and
f (x*) = {2/ : L > M * > = 0, D9i(x*)y
Φ j}
= 0, ί G J(x*), ί ^ j}·
We claim that by the regularity of x*, there exists y G T(cc*) such that
Dft(x*)2/^0.
To see this, suppose that for all y G T(x*), V^j(x*) T y = Dgj(x*)y = 0.
This implies that V^j(x*) G T^cc*)-1. By Lemma 20.1, this, in turn, implies
that
Vgj(x*) G span[V/ifc(x*), fc = 1 , . . . , m, V^(x*), z G J(x*), i ^ j ] .
But this contradicts the fact that x* is a regular point, which proves our claim.
Without loss of generality, we assume that we have y such that Dgj(x*)y < 0.
Consider the Lagrange condition, rewritten as
Df(x*) + \*TDh(x*) + ß*aD9j{x*) + Σμ*ίΌ9ί{χ*)
= 0T.
ίφύ
If we postmultiply the above by y and use the fact that y G T(x*), we get
Df(x*)y
Because Dgj(x*)y
=
-μ)Ό9ί{χ*)ν.
< 0 and we have assumed that μ^ < 0, we have
Df(x*)y
< 0.
KARUSH-KUHN-TUCKER CONDITION
20 V ^ =
Figure 21.2
491
10 Ω
Circuit in Example 21.2.
Because y G T(x*}, by Theorem 20.1 we can find a differentiable curve
{x(t) : t G (a, b)} on S such that there exists t* G (a, b) with x(i*) = x* and
i(t*) = y. Now,
p(x(t*)) = Df{x*)y < 0.
The above means that there is a δ > 0 such that for all t G (t*, t* + 5], we
have
/(*(<))</(x(t*)) = /0O·
On the other hand,
d_ (x(t*)) =
Dgj(x*)y<0,
gj
dt
and for some ε > 0 and all ί G [t*, ί*+ε], we have that gj(x(t)) < 0. Therefore,
for all t G (ί*,Γ +πύη{<ϊ,ε}], we have that gj(x(t)) < 0 and f(x(t)) < f{x*).
Because the points x(t), t G (t*,t* + min{£,ε}], are in 5, they are feasible
points with lower objective function values than x*. This contradicts the
assumption that x* is a local minimizer, which completes the proof.
I
E x a m p l e 21.2 Consider the circuit in Figure 21.2. Formulate and solve the
KKT condition for the following problems.
a. Find the value of the resistor R > 0 such that the power absorbed by
this resistor is maximized.
b . Find the value of the resistor R > 0 such that the power delivered to the
10-Ω resistor is maximized.
Solution:
a. The power absorbed by the resistor R is p = i2R, where i =
optimization problem can be represented as
minimize
subject to
400i?
(10 + R)2
— R < 0.
10
2
5^. The
492
PROBLEMS WITH INEQUALITY CONSTRAINTS
The derivative of the objective function is
400(10 + R)2 - 800^(10 + R)
(10 + # )
4
400(10 - R)
" "
(10 + R)3 '
Thus, the KKT condition is
400(10 - R)
(io + i?) 3
μ
"0,
μ>0,
μR = 0,
-R<0.
We consider two cases. In the first case, suppose that μ > 0. Then,
R = 0. But this contradicts the first condition above. Now suppose that
μ = 0. Then, by the first condition, we have R = 10. Therefore, the only
solution to the KKT condition is R = 10, μ = 0.
b . The power absorbed by the 10-Ω resistor is p = i 2 10, where z = 20/(10 +
i2). The optimization problem can be represented as
. . .
minimize
4000
— -—- —77
(10 + # ) 2
— R < 0.
subject to
The derivative of the objective function is
8000
(10 + R)3'
Thus, the KKT condition is
8000
(10 + Ä) 3
μ
" '
μ>0,
μ β = 0,
-Ä<0.
As before, we consider two cases. In the first case, suppose that μ >
Then, # = 0, which is feasible. For the second case, suppose that μ =
But this contradicts the first condition. Therefore, the only solution
the KKT condition is R = 0, μ = 8.
0.
0.
to
I
In the case when the objective function is to be maximized, that is, when
the optimization problem has the form
maximize
subject to
f(x)
h(x) = 0
9(x) < 0,
KARUSH-KUHN-TUCKER CONDITION
493
the KKT condition can be written as
1. μ* > 0.
2. -Df{x*)
+ \*TDh{x*)
3. μ*τ9{χ*)
= 0.
+ μ* τ £>0(α*) = 0 T .
4. h(x*) = 0.
5. »(«*) < 0.
The above is easily derived by converting the maximization problem above
into a minimization problem, by multiplying the objective function by —1. It
can be further rewritten as
1. μ* < 0.
2. Df(x*)
+ \*TDh(x*)
+ μ*ΎΌ9{χ*)
= 0T.
3. μ* τ 0(α*) = 0.
4. /ι(χ*) = 0.
5. <?(**) < 0.
The form shown above is obtained from the preceding one by changing the
signs of μ* and λ* and multiplying condition 2 by —1.
We can similarly derive the KKT condition for the case when the inequality
constraint is of the form g(x) > 0. Specifically, consider the problem
minimize
subject to
f(x)
h(x) = 0
9(x) > 0.
We multiply the inequality constraint function by —1 to obtain — g(x) < 0.
Thus, the KKT condition for this case is
1. μ* > 0.
2. Df(x*)
+ \*TDh(x*)
- μ* τ £>0(χ*) = 0 T .
3. μ* Τ 0(χ*)=Ο.
4. h{x*) = 0.
5. g(x*) > 0.
Changing the sign of μ* as before, we obtain
1. μ* < 0.
494
PROBLEMS WITH INEQUALITY CONSTRAINTS
2. Df(x*) + A* T Dft(x*) + μ* τ £>0(**) = <>T
3. μ* τ ^(χ*) = 0.
4. h(x*) = 0.
5. g(x*) > 0.
For the problem
maximize
subject to
f(x)
h(x) = 0
9{x) > 0,
the KKT condition is exactly the same as in Theorem 21.1, except for the
reversal of the inequality constraint.
E x a m p l e 21.3 In Figure 21.3, the two points X\ and #2 are feasible points;
that is, g(x\) > 0 and #(#2) > 0, and they satisfy the KKT condition.
The point X\ is a maximizer. The KKT condition for this point (with KKT
multiplier μ\) is
1. μι > 0.
2. V/(a5i)+MiV^(«i) = 0.
3. μι9(χι)
= 0.
4. 0(*i) > 0.
The point X2 is a minimizer of / . The KKT condition for this point (with
KKT multiplier /X2) is
1. μ 2 < 0.
2. V/(a? 2 ) + /x2V£(tt2) = 0.
3. μ29{Χ2) = 0.
4. <?(*2) > 0.
■
Example 21.4 Consider the problem
minimize
/ (x 1, Χ2)
subject to
£i,#2 > 0,
where
f(xi, x 2 ) = x\ + x\ + xiX2 — 3#i.
KARUSH-KUHN-TUCKER CONDITION
495
WVf( Xl )
Figure 21.3
minimizer).
Points satisfying the KKT condition (x± is a maximizer and X2 is a
The KKT condition for this problem is
1. μ = [μι,μ2]Τ
< 0.
2. £ > / ( * ) + μ τ = 0 T .
3. μτχ
= 0.
4. χ > 0.
We have
Df{x)
= [2xi + x2 - 3, zi + 2x2]·
This gives
2xi + X2 + Mi = 3,
xi + 2x 2 + M2 = 0,
μιΧι + μ2^2 = 0.
We now have four variables, three equations, and the inequality constraints
on each variable. To find a solution (as*,/x*), we first try
*
Mi
which gives
= 0,
,* _ 3
1 —
2'
x
2
μ>2
~
=
= o,
3
2
496
PROBLEMS WITH INEQUALITY CONSTRAINTS
The above satisfies all the KKT and feasibility conditions. In a similar fashion,
we can try
μ* = 0 , x\ = 0,
which gives
x*=0,
μί=3.
This point clearly violates the nonpositivity constraint on μ\.
The feasible point above satisfying the KKT condition is only a candidate
for a minimizer. However, there is no guarantee that the point is indeed
a minimizer, because the KKT condition is, in general, only necessary. A
sufficient condition for a point to be a minimizer is given in the next section.
■
Example 21.4 is a special case of a more general problem of the form
minimize
subject to
f(x)
x > 0.
The KKT condition for this problem has the form
μ<0,
ν/(*)+μ = 0,
μτχ
= 0,
x >0.
Prom the above, we can eliminate μ to obtain
V / ( * ) > 0,
T
x V / 0 r ) = 0,
x >0.
Some possible points in 1R2 that satisfy these conditions are depicted in Figure 21.4.
For further results related to the KKT condition, we refer the reader to
[90, Chapter 7].
21.2
Second-Order Conditions
As in the case of extremum problems with equality constraints, we can also
give second-order necessary and sufficient conditions for extremum problems
involving inequality constraints. For this, we need to define the following
matrix:
£ ( χ , λ , μ ) - F(x) + [XH(x)} + [μβ(χ)],
SECOND-ORDER CONDITIONS
497
Figure 21.4 Some possible points satisfying the KKT condition for problems with
positive constraints. (Adapted from [13].)
where F(x) is the Hessian matrix of / at cc, and the notation [XH(x)] represents
[XH(x)] = λ ι Η ι ( α ) + · · · + A m ff m (x),
as before. Similarly, the notation [/xG(x)] represents
[μβ(χ)] = μιβι(χ)
Η
h pbpGp{x),
where Gk{x) is the Hessian of g^ at £c, given by
r
Gk(x)
I
d29k („\
&M.
dxndkXl
^w
d
9k
(χ)
...
\X)
d gk
In the following theorem, we use
T(x*) = {y€Rn:
Dh(x*)y = 0, D9j(x')y
= 0, j € J(x*)},
498
PROBLEMS WITH INEQUALITY CONSTRAINTS
that is, the tangent space to the surface defined by active constraints.
Theorem
minimizer
m < n, g
there exist
21.2 Second-Order Necessary Conditions. Let x* be a local
of f : R n -> R subject to h(x) = 0, g(x) < 0, h : R n -» R m ,
: M.n ^> R p , and f,h,g G C2. Suppose that x* is regular. Then,
λ* G R m and μ* G R p such that:
1. μ* > 0, Df{x*) + λ* τ £>/ι(χ*) + μ* τ Dg(x*) = 0 T , μ* τ ^(»*) = 0.
£ For a// y G T(sc*) we ftave y1'L(x*,
λ*, ß*)y > 0.
D
Proof. Part 1 is simply a result of the KKT theorem. To prove part 2, we note
that because the point as* is a local minimizer over {x : h(x) = 0, 0(215) < 0},
it is also a local minimizer over {x : h(x) = 0, Qj{x) = 0, j e J(cc*)}; that
is, the point x* is a local minimizer with active constraints taken as equality
constraints (see Exercise 21.16). Hence, the second-order necessary conditions
for equality constraints (Theorem 20.4) are applicable here, which completes
the proof.
I
We now state the second-order sufficient conditions for extremum problems
involving inequality constraints. In the formulation of the result, we use the
following set:
Τ(χ*,μ*) = {y : Dh(x*)y = 0, Dgi(x*)y
= 0,i G J{x\ μ*)},
where </(χ*,μ*) = {i : g%{x*) = Ο,μ* > 0}. Note that J(x*,ß*) is a subset
of J(x*): </(χ*,μ*) C J(x*). This, in turn, implies that T(x*) is a subset of
f(as*,M*):r(x*)cf(e»,M*).
Theorem 21.3 Second-Order
Sufficient
Conditions.
Suppose that
/ , ^ , / i e C2 and there exist a feasible point x* G R n and vectors λ* G R m and
μ* G R p such that:
1. μ* > 0, £>/(**) + λ* τ Ζ)/ι(χ*) + μ*τΌ9{χ*)
2. For all y G T(cc*, μ*), y ^0,
we have yTL(x*,
= 0 T , μ* τ ^(χ*) = 0.
λ*, μ*)?/ > 0.
77ien, x* zs a sinci /ocaZ minimizer of f subject to h(x) = 0, g(x) < 0.
Proof For a proof of this theorem, we refer the reader to [88, p. 345].
□
I
A result similar to Theorem 21.3 holds for a strict local maximizer, the
only difference being that we need μ* < 0 and that L(x*,A*) be negative
definite on Γ(χ*,μ*).
Example 21.5 Consider the following problem:
minimize
subject to
x\X2
x\ + X2 > 2
x2 > x\.
SECOND-ORDER CONDITIONS
499
a. Write down the KKT condition for this problem.
b . Find all points (and KKT multipliers) satisfying the KKT condition. In
each case, determine if the point is regular.
c. Find all points in part b that also satisfy the SONC.
d. Find all points in part c that also satisfy the SOSC.
e. Find all points in part c that are local minimizers.
Solution:
a. Write f(x) = X1X2, gi{x) = 2 — xi—x2,
condition is
and g2(x) =x\— X2· The KKT
X2 - μι + M2 = 0,
χι - μι - μ2 = 0,
μι(2 - χ ι - χ2) + μ2{χ\ - x2) = 0,
μι,μ2 > 0,
2 — χι — χ2 < 0,
Χι — %2 < 0.
b . It is easy to check that μ\ φ 0 and μ2 ^ 0. This leaves us with only one
solution to the KKT condition: x\ = x\ — 1, μ\ = 1, μ2 = 0. For this
point, we have Dgi(x*) = [—1,-1] and Dg2(x*) = [1,-1]· Hence, x* is
regular.
c. Both constraints are active. Hence, because x* is regular, T(x*) = {0}.
This implies that the SONC is satisfied.
d. Now,
Σ(χ\μ*)=
I
o
.
Moreover, Τ(χ*,μ*) = {y : [-1,-1]*/ = 0} = {y : yx = -y2}. Pick
y = [ 1 , - 1 ] τ β Τ(χ*,μ*). We have yTL(x*^*)y
= - 2 < 0, which
means that the SOSC fails.
e. In fact, the point x* is not a local minimizer. To see this, draw a picture
of the constraint set and level sets of the objective function. Moving in
the feasible direction [1,1] T , the objective function increases; but moving
in the feasible direction [—1,1]T, the objective function decreases.
■
We now solve analytically the problem in Example 20.1 that we solved
graphically earlier.
500
PROBLEMS WITH INEQUALITY CONSTRAINTS
E x a m p l e 21.6 We wish to minimize f(x) = {x\ — l ) 2 + X2 — 2 subject to
h(x) = X2 — x\ — 1 = 0,
g{x) = xi + X2 — 2 < 0.
For all a: 6 R 2 , we have
Dh(x) = [-1,1],
Dg(x) = [1,1].
Thus, Vft(aj) and Vg(x) are linearly independent and hence all feasible points
are regular. We first write the KKT condition. Because Df(x) = [2#i — 2,1],
we have
Df(x)
+ XDh(x) + ßDg(x) = [2xx - 2 - λ + μ,1 + λ + μ ] = 0 τ ,
μ(χι + X2 - 2) = 0,
μ>0,
^2 - Xi - 1 = 0,
xi + x 2 - 2 < 0.
To find points that satisfy the conditions above, we first try μ > 0, which
implies that Χχ + X2 — 2 = 0. Thus, we are faced with a system of four linear
equations
2xi - 2 - λ + μ = 0,
1 + λ + μ = 0,
x 2 - #1 - 1 = 0,
Xl + X2 ~ 2 = 0.
Solving the system of equations above, we obtain
1
31 = 2'
X2 =
3
2'
λ==_1,
^
=
0
·
However, the above is not a legitimate solution to the KKT condition, because
we obtained μ = 0, which contradicts the assumption that μ > 0.
In the second try, we assume that μ = 0. Thus, we have to solve the system
of equations
2xi - 2 - A = 0,
1 + A = 0,
X2 - xi - 1 = 0,
and the solutions must satisfy
g(xi, x 2 ) = xi + #2 - 2 < 0.
EXERCISES
501
Solving the equations above, we obtain
1
3
^
^
Note that cc* = [1/2,3/2] satisfies the constraint g{x*) < 0. The point x*
satisfying the KKT necessary condition is therefore the candidate for being a
minimizer.
We now verify if x* = [1/2,3/2] τ , λ* = —1, μ* = 0, satisfy the secondorder sufficient conditions. For this, we form the matrix
L(x\
λ*, μ*) - F(x*) + \*H{x*)
+
"2 0"
0 0"
0
0
+ (-1)
0
ß*G{x*)
0
+ (0)
0 0"
0
0
"2 0'
0 0
We then find the subspace
f(x*,ß*)
= {y:Dh(x*)y
= 0}.
Note that because μ* = 0, the active constraint g(x*) = 0 does not enter the
computation of Γ(χ*,μ*). Note also that in this case, T(x*) = {0}. We have
Τ(χ*,μ*)
= {y : [-1, l]y = 0} = {[a,a]T : a G R}.
We then check for positive definiteness of L(x*, λ*, μ*) on T(x*, μ*). We have
y T L(cc*,A*^*)t/ = [a, a]
2 0
0 0
= 2a 2 .
Thus, L(iE*,A*,/i*) is positive definite on Γ(χ*,μ*).
Observe that
L(x*, λ*,μ*) is, in fact, only positive semidefinite on R 2 .
By the second-order sufficient conditions, we conclude that x* =
[1/2,3/2] is a strict local minimizer.
I
EXERCISES
21.1 Consider the optimization problem
minimize
subject to
x\ + Ax\
x\ + 2x\ > 4.
a. Find all the points that satisfy the KKT conditions.
502
PROBLEMS WITH INEQUALITY CONSTRAINTS
b . Apply the SOSC to determine the nature of the critical points from the
previous part.
21.2 Find local extremizers for:
a. x\ + x\ — 2x\ — 10x2 + 26 subject to jrx2 — x\ < 0, 5xi + \x2 < 5.
b . x\ + x\ subject to x\ > 0, X2 > 0, x\ + x^ > 5.
c. #i + 6x1X2 — 4xi — 2x2 subject to x\ + 2x 2 < 1, 2xi — 2x2 < 1.
21.3 Find local minimizers for x\ + x\ subject to x\ + 2xiX2 + x | = 1,
#i - ^2 < 0.
21.4 Write down the Karush-Kuhn-Tucker condition for the optimization
problem in Exercise 15.8.
21.5 Consider the problem
minimize
subject to
X2 — (xi — 2) 3 + 3
x 2 > 1,
where x\ and X2 are real variables. Answer each of the following questions,
making sure that you give complete reasoning for your answers.
a. Write down the KKT condition for the problem, and find all points that
satisfy the condition. Check whether or not each point is regular.
b . Determine whether or not the point(s) in part a satisfy the second-order
necessary condition.
c. Determine whether or not the point(s) in part b satisfy the second-order
sufficient condition.
21.6 Consider the problem
minimize X2
subject to
X2 > — (xi — l ) 2 + 3.
a. Find all points satisfying the KKT condition for the problem.
b . For each point x* in part a, find T(x*), 7V(cc*), and
T(x*).
c. Find the subset of points from part a that satisfy the second-order necessary condition.
EXERCISES
503
21.7 Consider the problem of optimizing (either minimizing or maximizing)
(xi - 2) 2 + (x2 - l ) 2 subject to
2 — x\ — X2 > 0
xi > 0 .
The point x* = 0 satisfies the KKT conditions.
a. Does x* satisfy the FONC for minimization or maximization? What are
the KKT multipliers?
b . Does x* satisfy the SOSC? Carefully justify your answer.
21.8 Consider the optimization problem
minimize
subject to
where f(x)
0}.
f(x)
x £ Ω,
= x\x\, where x = [xi,x 2 ] T , and Ω = {x G R 2 : x\ = x2, x\ >
a. Find all points satisfying the KKT condition.
b . Do each of the points found in part a satisfy the second-order necessary
condition?
c. Do each of the points found in part a satisfy the second-order sufficient
condition?
21.9 Consider the problem
minimize
subject to
- | | A x — 6|| 2
x\-\
h xn = 1
X\ , . . . , Χγι ^ U.
a. Write down the KKT condition for the problem.
b . Define what it means for a feasible point x* to be regular in this particular
problem. Are there any feasible points in this problem that are not
regular? If yes, find them. If not, explain why not.
504
PROBLEMS WITH INEQUALITY CONSTRAINTS
21.10 Let g : R n -> R and x0 G R n be given, where g(x0) > 0. Consider the
problem
-\\x — Xo\\2
minimize
subject to
g(x) < 0.
Suppose that #* is a solution to the problem and g G C1. Use the KKT
theorem to decide which of the following equations/inequalities hold:
i. g(x*) < 0.
ii. g(x*) = 0.
iii. (x* -x0)TVg(x*)
< 0.
iv. (x* -x0)TVg(x*)
= 0.
v. (x* - x 0 ) T V^(ir*) > 0.
21.11 Consider a square room with corners located at [0,0] T , [0,2] T , [2,0] T ,
and [2,2] T (in R 2 ). We wish to find the point in the room that is closest to
the point [3,4] T .
a. Guess which point in the room is the closest point in the room to the
point [3,4] T .
b . Use the second-order sufficient conditions to prove that the point you
have guessed is a strict local minimizer.
Hint: Minimizing the distance is the same as minimizing the square distance.
21.12 Consider the quadratic programming problem
minimize
-xTQx
subject to
Ax < b,
where Q = QT > 0, A G R m x n , and b > 0. Find all points satisfying the
KKT condition.
21.13 Consider the linear programming problem
minimize
subject to
ax\ + 6x2
cx\+ dx2 = e
X\,X2
> 0,
where a, b, c, d, e G R are all nonzero constants. Suppose that x* is an optimal
basic feasible solution to the problem.
EXERCISES
505
a. Write down the Karush-Kuhn-Tucker condition involving x* (specifying
clearly the number of Lagrange and KKT multipliers).
b . Is x* regular? Explain.
c. Find the tangent space T(x*) (defined by the active constraints) for this
problem.
d. Assume that the relative cost coefficients of all nonbasic variables are
strictly positive. Does x* satisfy the second-order sufficient condition?
Explain.
21.14 Consider the problem
minimize
subject to
cTx
Ax < 0,
where A G M m x n , m < n, is of full rank. Use the KKT theorem to show that
if there exists a solution, then the optimal objective function value is 0.
21.15 Consider a linear programming problem in standard form (see Chapter 15).
a. Write down the Karush-Kuhn-Tucker condition for the problem.
b . Use part a to show that if there exists an optimal feasible solution to the
linear program, then there exists a feasible solution to the corresponding
dual problem that achieves an objective function value that is the same
as the optimal value of the primal (compare this with Theorem 17.1).
c. Use parts a and b to prove that if x* is an optimal feasible solutions of
the primal, then there exists a feasible solution λ* to the dual such that
(c T - λ* τ Λ)χ* = 0 (compare this with Theorem 17.3).
21.16 Consider the constraint set S = {x : h(x) = 0,g(x) < 0}. Let x* G S
be a regular local minimizer of / over S and J(x*) the index set of active
inequality constraints. Show that x* is also a regular local minimizer of /
over the set S' = {x : h(x) = 0,gj(x) = 0 , j G J(x*)}.
21.17 Solve the following optimization problem using the second-order sufficient conditions:
minimize
subject to
x\ + x\
x\ — #2 — 4 < 0
x2 - x\ - 2 < 0.
506
PROBLEMS WITH INEQUALITY CONSTRAINTS
See Figure 22.1 for a graphical illustration of the problem.
21.18 Solve the following optimization problem using the second-order sufficient conditions:
minimize
x\ + x\
subject to
X\ — x\ — 4 > 0
xx - 10 < 0.
See Figure 22.2 for a graphical illustration of the problem.
21.19 Consider the problem
minimize
x\ + x\
subject to
4 — x\ — x^ < 0
3^2 — #i < 0
— 3^2 — xi < 0.
Figure 22.3 gives a graphical illustration of the problem. Deduce from the
figure that the problem has two strict local minimizers, and use the secondorder sufficient conditions to verify the graphical solutions.
21.20 Consider the following optimization problem with an inequality constraint:
minimize
subject to
3#i
x\ + x\ > 2.
a. Does the point x* = [2,0] T satisfy the KKT (first-order necessary) condition?
b . Does the point x* = [2,0] T satisfy the second-order necessary condition
(for problems with inequality constraints)?
c. Is the point x* = [2,0] T a local minimizer?
(See Exercise 6.15 for a similar problem treated using set-constrained methods.)
21.21 Consider the problem
• ·
·
minimize
1
2
subject to
II 112
-as
ii
ii
T
ax =b
x > 0,
EXERCISES
507
where a G R n , a > 0, and b G R, b > 0. Show that if a solution to the
problem exists, then it is unique, and find an expression for it in terms of a
and b.
21.22 Consider the problem
minimize (x\ — a) 2 + (#2 — &)2>
#i, #2 € R
2
subject to re + #2 < 1,
where a, 6 G R are given constants satisfying a 2 + 62 > 1.
a. Let x* = [#*,#2] T be a solution to the problem. Use the first-order
necessary conditions for unconstrained optimization to show that (x\)2 +
(x*2)2 = 1.
b . Use the KKT theorem to show that the solution x* = [ χ ΐ , ^ ] 1 " is unique
and has the form x\ = αα, x\ = ab, where a G R is a positive constant.
c. Find an expression for a (from part b) in terms of a and b.
21.23 Consider the problem
x\ + (x2 + l ) 2 ,
minimize
subject to
#1, £2 £ R
#2 > exp(a?i)
[exp(x) = ex is the exponential of x\. Let as* = [#ί,#2] Τ be the solution to
the problem.
a. Write down the KKT condition that must be satisfied by x*.
b . Prove that x\ = exp(#i).
c. Prove that - 2 < x\ < 0.
21.24 Consider the problem
minimize
subject to
cTx + 8
- | | x | | 2 < 1,
where c G R n , c φ 0. Suppose that x* = a e is a solution to the problem,
where a G R and e = [ 1 , . . . , 1] T , and the corresponding objective value is 4.
a. Show that ||x*|| 2 = 2.
b . Find a and c (they may depend on n).
508
PROBLEMS WITH INEQUALITY CONSTRAINTS
21.25 Consider the problem with equality constraint
minimize
subject to
f(x)
h(x) = 0.
We can convert the above into the equivalent optimization problem
minimize
subject to
f(x)
-||Ma;)|| 2 < 0.
Write down the KKT condition for the equivalent problem (with inequality
constraint) and explain why the KKT theorem cannot be applied in this case.
CHAPTER 22
CONVEX OPTIMIZATION PROBLEMS
22.1
Introduction
The optimization problems posed at the beginning of this part are, in general,
very difficult to solve. The source of these difficulties may be in the objective
function or the constraints. Even if the objective function is simple and "wellbehaved," the nature of the constraints may make the problem difficult to
solve. We illustrate some of these difficulties in the following examples.
Example 22.1 Consider the optimization problem
minimize
subject to
x\ + x\
X2 — X\ — 2 < 0
x\ - x2 - 4 < 0.
The problem is depicted in Figure 22.1, where, as we can see, the constrained
minimizer is the same as the unconstrained minimizer. At the minimizer,
all the constraints are inactive. If we had only known this fact, we could
have approached this problem as an unconstrained optimization problem using
techniques from Part II.
I
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
509
510
CONVEX OPTIMIZATION PROBLEMS
Figure 22.1
the same.
Situation where the constrained and the unconstrained minimizers are
Example 22.2 Consider the optimization problem
minimize
subject to
2
I
2
X\ ~\~ Xo
x\ 10 < 0
xi - x\ - 4 > 0.
The problem is depicted in Figure 22.2. At the solution, only one constraint is
active. If we had only known about this we could have handled this problem
as a constrained optimization problem using the Lagrange multiplier method.
Example 22.3 Consider the optimization problem
minimize
2 .
2
X\ i Xo
subject to 4 — x\ —x\ < 0
3x2 — x\ <o
- 3 x 2 - xi < 0 .
The problem is depicted in Figure 22.3. This example illustrates the situation
where the constraints introduce local minimizers, even though the objective
function itself has only one unconstrained global minimizer.
|
Some of the difficulties illustrated in the examples above can be eliminated
if we restrict our problems to convex feasible regions. Admittedly, some important real-life problems do not fit into this framework. On the other hand,
INTRODUCTION
x r 10=0
Figure 22.2
Figure 22.3
Situation where only one constraint is active.
Situation where the constraints introduce local minimizers.
511
512
CONVEX OPTIMIZATION PROBLEMS
it is possible to give results of a global nature for this class of optimization
problems. In the next section, we introduce the notion of a convex function,
which plays an important role in our subsequent treatment of such problems.
22.2
Convex Functions
We begin with a definition of the graph of a real-valued function.
Definition 22.1 The graph of / : Ω -> R, Ω C R n , is the set of points in
Ω x R C R n + 1 given by
x
/(*)
:χβΩ
We can visualize the graph of / as simply the set of points on a "plot" of
f(x) versus x (see Figure 22.4). We next define the epigraph of a real-valued
function.
Definition 22.2 The epigraph of a function / : Ω —► R, Ω C R n , denoted
epi(/), is the set of points in Ω x R given by
epi(/)
: x e Ω, ß e R, ß >
f(x)}.
The epigraph epi(/) of a function / is simply the set of points in Ω x R on
or above the graph of / (see Figure 22.4). We can also think of epi(/) as a
subset of R n + 1 .
Recall that a set Ω C R n is convex if for every X\,xi G Ω and a G (0,1),
aa?i + (1 — a)#2 G Ω (see Section 4.3). We now introduce the notion of a
convex function.
Definition 22.3 A function / : Ω -► R, Ω C R n , is convex on Ω if its
epigraph is a convex set.
I
Theorem 22.1 / / a function f : Ω —► R, Ω C R n , is convex on Ω, then Ω is
a convex set.
D
Proof We prove this theorem by contraposition. Suppose that Ω is not a
convex set. Then, there exist two points yx and y2 such that for some a G
(0,1),
z = ay1 + (l- a)y2 £ Ω.
Let
ßi = f(vi),
& = /(y2)·
CONVEX FUNCTIONS
513
f(x)A
graph of f
Figure 22.4
Graph and epigraph of a function / : R
Then, the pairs
3/2
Ä
belong to the graph of / , and hence also the epigraph of / . Let
w = a
+ (1 - a)
2/2
A
We have
w =
aft + (1 - α)Α
But note that w 0 epi(/), because z 0 Ω. Therefore, epi(/) is not convex,
and hence / is not a convex function.
I
The next theorem gives a very useful characterization of convex functions.
This characterization is often used as a definition for a convex function.
T h e o r e m 22.2 A function f : Ω —> R defined on a convex set Ω C R n is
convex if and only if for all x,y G Ω and all a G (0,1), we have
f{ax + (1 - a)y) < af{x) + (1 -
a)f(y).
D
Proof <=: Assume that for all cc, y G Ω and a G (0,1),
f{ax + (1 - a)y) < af{x) + (1 -
a)f(y).
Let [as T ,a] T and [y T ,6] T be two points in epi(/), where a,b £
definition of epi(/) it follows that
/(*) < a,
f(y) < b.
Prom the
514
CONVEX OPTIMIZATION PROBLEMS
Therefore, using the first inequality above, we have
f(ax + (1 — a)y) < eta + (1 — a)b.
Because Ω is convex, ax + (1 — a)y G Ω. Hence,
ax + (1 — a)y
e epi(/),
aa + (1 — a)b
which implies that epi(/) is a convex set, and hence / is a convex function.
=>: Assume that / : Ω —► R is a convex function. Let x, y G Ω and
/ ( * ) = a,
/ ( » ) = 6.
Thus,
G epi(/).
Because / is a convex function, its epigraph is a convex subset of R n + 1 .
Therefore, for all a G (0,1), we have
a
+ (!-")
ax + (1 — α)τ/
G epi(/).
αα + (1 — α)6
The above implies that for all a G (0,1),
f(ax + (1 - a)y) < a a + (1 - a)6 = a / ( » ) + (1 - a ) / ( y ) .
This completes the proof.
I
A geometric interpretation of Theorem 22.2 is given in Figure 22.5. The
theorem states that if / : Ω —► R is a convex function over a convex set
Ω, then for all x, y G Ω, the points on the line segment in R n + 1 connecting
[x T , f{x)]T and [y T , f(y)]T must lie on or above the graph of / .
Using Theorem 22.2, it is straightforward to show that any nonnegative
scaling of a convex function is convex, and that the sum of convex functions
is convex.
Theorem 22.3 Suppose that f, / i , and fa are convex functions. Then, for
any a > 0, the function af is convex. Moreover, f\ + /2 is convex.
□
Proof. Let x, y G Ω and a G (0,1). Fix a > 0. For convenience, write / = af.
We have
f(ax + (1 - a)y) =
<
=
=
af(ax + (1 - a)y)
a (af(x) + (1 — a)f(y)) because / is convex and a > 0
a(a/(a:)) + ( l - a ) ( a / ( y ) )
af(x) + (1 - a ) / ( y ) ,
CONVEX FUNCTIONS
515
f(y)
af(x)+(1-a)f(y)
f(x)
f(ax+(1-a)y)
Figure 22.5
Geometric interpretation of Theorem 22.2.
which implies that / is convex.
Next, write fs = / i + / 2 . We have
fs(ax + (1 - a)y) = fi(ax
+ (1 - a)y) + / 2 ( a x + (1 - a)y)
< (α/χ(χ) + (1 - a ) / i ( y ) ) + (af2(x)
+ (1 - a)/ 2 (y))
by convexity of / i and / 2
= α ( Λ ( χ ) + / 2 (x)) + (1 - a)(/!(y) + / 2 (y))
= a/3(x) + ( l - a ) / 3 ( y ) ,
which implies that fs is convex.
I
Theorem 22.3 implies that for any given collection of convex functions
/ i , . . . , fa and nonnegative numbers C\,..., Q , the function c i / 2 H
\- ctjt is
convex. Using a method of proof similar to that used in Theorem 22.3, it is
similarly straightforward to show that the function m a x { / i , . . . , fa} is convex
(see Exercise 22.6).
We now define the notion of strict convexity.
Definition 22.4 A function / : Ω —> R on a convex set Ω C R n is stnctly
convex if for all x, y G Ω, χ φ y, and a G (0,1), we have
f{ax + (1 - a)y) < af(x)
+ (1 -
a)f(y).
■
516
CONVEX OPTIMIZATION PROBLEMS
From this definition, we see that for a strictly convex function, all points on
the open line segment connecting the points [ χ τ , / ( χ ) ] τ and [ y T , / ( y ) ] T lie
(strictly) above the graph of / .
Definition 22.5 A function / : Ω —* R on a convex set Ω C R n is (strictly)
concave if — / is (strictly) convex.
I
Note that the graph of a strictly concave function always lies above the line
segment connecting any two points on its graph.
To show that a function is not convex, we need only produce a pair of
points x, y G Ω and an a G (0,1) such that the inequality in Theorem 22.2 is
violated.
Example 22.4 Let f(x) = x\x<i. Is / convex over Ω = {x : x\ > 0, #2 > 0}?
The answer is no. Take, for example, x = [1,2] T G Ω and y = [2,1] T G Ω.
Then,
[2 - ol
ax + (1 - a)y = \
Hence,
1 + a\
f(ax + (1 - a)y) = (2 - a ) ( l + a) = 2 + a - a2
and
af(x)
+ (1 - a)f(y)
= 2.
If, for example, a = 1/2 e (0,1), then
/
Ua;+2i/j
=
4>2/(x)+2/(l/)'
which shows that / is not convex over Ω.
I
Example 22.4 is an illustration of the following general result.
Proposition 22.1 A quadratic form f : Ω —► R, Ω C R n , given by f(x) =
xTQx, Q G R n x n , Q = Q T , is convex on Ω if and only if for all x,y G Ω,
(x-y)TQ(x-y)>0.
u
Proof The result follows from Theorem 22.2. Indeed, the function f{x) =
xTQx is convex if and only if for every a G (0,1), and every x, y G R n , we
have
f(ax + (1 - a)y) < af(x) + (1 - a ) / ( y ) ,
or, equivalently,
af(x) + (1 - a)f(y)
- f(ax + (1 - a)y) > 0.
CONVEX FUNCTIONS
517
Substituting for / into the left-hand side of this equation yields
axTQx
+ (1 — OL)yTQy — (ctx + (1 - a)y)TQ(ax
T
= ax Qx
T
T
+ y Qy
2
2 T
— ay Qy
T
- (2a - 2a )x Qy
= a ( l — a)xTQx
—ax
- (1 - 2a +
— 2a(l — a)xTQy
= a ( l - a)(x - y)TQ{x
+ (1 -
a)y)
Qx
2
a )yTQy
+ a ( l — ct)yT Qy
- y).
Therefore, / is convex if and only if
a ( l - a)(a; - y)TQ(x
- y) > 0,
which proves the result.
E x a m p l e 22.5 In Example 22.4, f(x)
f(x) = xTQx, where
Q
= X\X2, which can be written as
1 0
1
1
0
Let Ω = {x : x > 0}, and x = [2,2] T G Ω, y = [1,3] T G Ω. We have
y-x
=
and
(y-x)TQ(y-x)
=
\[-l,l]
-ll
0
1
1
0
-1
1
= - 1 < 0.
Hence, by Proposition 22.1, / is not convex on Ω.
Diiferentiable convex functions can be characterized using the following
theorem.
Theorem 22.4 Let f : Ω —► M, / G C1, be defined on an open convex set
Ω C R n . Then, f is convex on Ω if and only if for all x, y G Ω,
f(y)>f(x) + Df(x)(y-x).
D
Proof =>: Suppose that / : Ω —► R is diiferentiable and convex. Then, by
Theorem 22.2, for any y, x G Ω and a G (0,1) we have
f(ay + (1 - a)x) < af(y)
+ (1 - a ) / ( x ) .
518
CONVEX OPTIMIZATION PROBLEMS
Rearranging terms yields
f(x + a(y - x)) - f(x) < a(f(y)
-
f(x)).
Upon dividing both sides of this inequality by a, we get
/(tt + a C y - * ) ) - / ( * )
a
If we now take the limit as a —> 0 and apply the definition of the directional
derivative of / at x in the direction y — x (see Section 6.2), we get
Df(x)(y-x)<f(y)-f(x)
or
f{v)>f{x)
+
Df{x){y-x).
<=: Assume that Ω is convex, / : Ω —> R is differentiable, and for all
X XI G Ω
f{y)>f{x)
+
Df(x)(y-x).
Let u, v G Ω and a G (0,1). Because Ω is convex,
w = au + (1 — a)v G Ω.
We also have
f(u)>f(w)
+
Df{w)(u-w)
f(v)>f(w)
+
Df(w)(v-w).
and
Multiplying the first of this inequalities by a and the second by (1 — a) and
then adding them together yields
af(u) + (1 - a)f{v)
But
Hence,
> f(w) + Df(w) (au + (1 - a)v -
w).
w — au + (1 — a)v.
af(u) + (1 - α ) / ( υ ) > f(au + (1 - o » .
Hence, by Theorem 22.2, / is a convex function.
I
In Theorem 22.4, the assumption that Ω be open is not necessary, as long
as f eC1 on some open set that contains Ω (e.g., / G C1 on R n ).
A geometric interpretation of Theorem 22.4 is given in Figure 22.6. To explain the interpretation, let xo G Ω. The function £(x) = f(x0) + Df(xo)(x —
Xo) is the linear approximation to / at ceo- The theorem says that the graph
of / always lies above its linear approximation at any point. In other words,
CONVEX FUNCTIONS
\
519
epi(f)
f(y)
f(x)+Df(x)(y-x)
:
*
Figure 22.6
y
x
y
'
Ω
Geometric interpretation of Theorem 22.4.
the linear approximation to a convex function / at any point of its domain
lies below epi(/).
This geometric idea leads to a generalization of the gradient to the case
where / is not differentiable. Let / : Ω —> R be defined on an open convex set
Ω C R n . A vector g G R n is said to be a subgradient of / at a point x G Ω if
for all y G Ω,
f(y)>f(x)+gT(y-x)·
As in the case of the standard gradient, if g is a subgradient, then for a given
Xo G Ω, the function £(x) = f(xo) + gT(x — xo) lies below epi(/).
For functions that are twice continuously differentiable, the following theorem gives another possible characterization of convexity.
Theorem 22.5 Let f : Ω —> R, / G C2, be defined on an open convex set
Ω c R n . Then, f is convex on Ω if and only if for each x G Ω, the Hessian
F(x) of f at x is a positive semidefinite matrix.
□
Proof. <=: Let x,y G Ω. Because / G C2, by Taylor's theorem there exists
a G (0,1) such that
f(y) = f(x) + Df{x){y
- x) + \{y - x)TF{x
+ a{y - x))(y - x).
Because F(x + a(y — x)) is positive semidefinite,
(y - x)TF(ay
+ (1 - a)x)(y
-x)>0.
520
CONVEX OPTIMIZATION PROBLEMS
Therefore, we have
f(v)>f(x)
+
Df(x)(y-x),
which implies that / is convex, by Theorem 22.4.
=>: We use contraposition. Assume that there exists x G Ω such that
Fix) is not positive semidefinite. Therefore, there exists d G R n such that
d F(x)d < 0. By assumption, Ω is open; thus, the point x is an interior
point. By the continuity of the Hessian matrix, there exists a nonzero s G R
such that x + sd G Ω, and if we write y = x + sd, then for all points z on the
line segment joining x and y, we have d F(z)d < 0. By Taylor's theorem
there exists a G (0,1) such that
f(y) = f(x) + Df(x)(y
-x)
= f(x) + Df(x)(y
+ i ( y - x)TF(x
- x) + -s2dTF(x
+ a(y - x))(y - x)
+ asd)d.
Because a G (0,1), the point x + a s d is on the line segment joining x and y,
and therefore
dTF(x + a s d ) d < 0.
Because 5 ^ 0, we have s 2 > 0, and hence
f{y)<f{x)
+
Df{x)(y-x).
Therefore, by Theorem 22.4, / is not a convex function.
I
Theorem 22.5 can be strengthened to include nonopen sets by modifying
the condition to be (y — x)TF(x)(y
— x) > 0 for all x,y G Ω (and assuming
that / G C2 on some open set that contains Ω; for example, / G C2 on R n ).
A proof similar to that above can be used in this case.
Note that by definition of concavity, a function / : Ω —> R, / G C2, is
concave over the convex set Ω C R n if and only if for all x G Ω, the Hessian
F(x) of / is negative semidefinite.
E x a m p l e 22.6 Determine whether the following functions are convex, concave, or neither:
1. / : R - > R , f{x) =
-8x2.
2. / : R 3 -» R, f(x) = 4x1 + 3ar| + bx\ + §χλχ2 + ΧχΧζ - 3xi - 2x2 + 15.
3. / : R 2 -* R, f(x) = 2χλχ2 - x \ -
x\.
Solution:
1. We use Theorem 22.5. We first compute the Hessian, which in this case
is just the second derivative: (d2f/dx2)(x)
= —16 < 0 for all x G R.
Hence, / is concave over R.
CONVEX OPTIMIZATION PROBLEMS
521
2. The Hessian matrix of / is
8 6
6 6
1 0
F(x)
1
0
10
The leading principal minors of F(x) are
Δ ι = 8 > 0,
det
8
6
6
6
12 > 0 ,
A 3 = d e t F ( x ) = 114>0.
Hence, F(x) is positive definite for all 1 6 R 3 . Therefore, / is a convex
function over M.3.
3. The Hessian of / is
F(x)
-2
2
2
-2
which is negative semidefinite for all x G R 2 . Hence, / is concave on R 2 .
22.3
Convex Optimization Problems
In this section we consider optimization problems where the objective function is a convex function and the constraint set is a convex set. We refer to
such problems as convex optimization problems or convex programming problems. Optimization problems that can be classified as convex programming
problems include linear programs and optimization problems with quadratic
objective function and linear constraints. Convex programming problems are
interesting for several reasons. Specifically, as we shall see, local minimizers
are global for such problems. Furthermore, first-order necessary conditions
become sufficient conditions for minimization.
Our first theorem below states that in convex programming problems, local
minimizers are also global.
Theorem 22.6 Let f : Ω —► R be a convex function defined on a Convex set
Ω C R n . Then, a point is a global minimizer of f over Ω if and only if it is
a local minimizer of f.
□
Proof. =>: This is obvious.
522
CONVEX OPTIMIZATION PROBLEMS
<=: We prove this by contraposition. Suppose that x* is not a global
minimizer of / over Ω. Then, for some y G Ω, we have f(y) < f(x*). By
assumption, the function / is convex, and hence for all a G (0,1),
/ ( a y + (1 - a)x*) < af(y) + (1 -
a)f(x*).
Because f(y) < /(#*), we have
af(y) + (1 - <*)/(*·) = a ( / ( y ) - /(**)) + /(**) < / ( * * ) .
Thus, for a l i a G (0,1),
/ ( a y + (l-a)x*) </(**).
Hence, there exist points that are arbitrarily close to x* and have lower objective function value. For example, the sequence {yn} of points given by
converges to cc*, and f(yn)
which completes the proof.
< f(x*)> Hence, x* is not a local minimizer,
I
We now show that the set of global optimizers is convex. For this, we need
the following lemma.
Lemma 22.1 Let g : Ω —► R be a convex function defined on a convex set
Ω C R n . Then, for each c e R, the set
r c = {x e Ω : g(x) < c}
□
is a convex set.
Proof. Let x,y G Tc. Then, g(x) < c and g(y) < c. Because g is convex, for
a l l a G (0,1),
g(ax + (1 - a)y) < ag{x) + (1 - a)g(y) < c.
Hence, ax + (1 — a)y G T c , which implies that Tc is convex.
I
Corollary 22.1 Let f : Ω —► R be a convex function defined on a convex set
Ω C R n . Then, the set of all global minimizers of f over Ω is a convex set.
D
Proof. The result follows immediately from Lemma 22.1 by setting
c = min/(x).
CONVEX OPTIMIZATION PROBLEMS
523
We now show that if the objective function is continuously differentiable
and convex, then the first-order necessary condition (see Theorem 6.1) for a
point to be a minimizer is also sufficient. We use the following lemma.
Lemma 22.2 Let f : Ω —► R be a convex function defined on the convex set
Ω C R n and f G Cl on an open convex set containing Ω. Suppose that the
point x* G Ω is such that for all x G Ω, χ φ x*, we have
>0.
Df(x*){x-x*)
Then, x* is a global minimizer of f over Ω.
D
Proof Because the function / is convex, by Theorem 22.4, for all x G Ω, we
have
f(x) >f(x*) +Df (**)(*-**)·
Hence, the condition Df{x*)(x
— x*) > 0 implies that f(x) > /(#*).
I
Observe that for any x G Ω, the vector x — x* can be interpreted as a
feasible direction at x* (see Definition 6.2). Using Lemma 22.2, we have the
following theorem (cf. Theorem 6.1).
Theorem 22.7 Let f : Ω —► R be a convex function defined on the convex
set Ω C R n , and f G C1 on an open convex set containing Ω. Suppose that
the point x* G Ω is such that for any feasible direction d at x*, we have
dTVf(x*)
> 0.
Then, x* is a global minimizer of f over Ω.
ü
Proof Let x G Ω, x ^ x*. By convexity of Ω,
x* + a(x - x*) = ax + (1 - a)x* G Ω
for all a G (0,1). Hence, the vector d = x — x* is a feasible direction at x*
(see Definition 6.2). By assumption,
Df(x*)(x
- x*) = d T V / ( x * ) > 0.
Hence, by Lemma 22.2, x* is a global minimizer of / over Ω.
I
From Theorem 22.7, we easily deduce the following corollary (compare this
with Corollary 6.1).
Corollary 22.2 Let f : Ω —> R, / G C1, be a convex function defined on the
convex set Ω C R n . Suppose that the point x* G Ω is such that
V/(**) = 0.
524
CONVEX OPTIMIZATION PROBLEMS
D
Then, x* is a global minimizer of f over Ω.
We now consider the constrained optimization problem
minimize
subject to
f(x)
h(x) = 0.
We assume that the feasible set is convex. An example where this is the case
is when
h(x) — Ax — 6.
The following theorem states that provided the feasible set is convex, the
Lagrange condition is sufficient for a point to be a minimizer.
Theorem 22.8 Let f : R n —► R, / G C1, be a convex function on the set of
feasible points
Ω = {x G R n : h{x) = 0},
where h : R n —> R m , h EC 1 , and Ω is convex. Suppose that there exist x* € Ω
and λ* e R m such that
\*TDh(x*)=0T.
Df(x*) +
Then, x* is a global minimizer of f over Ω.
D
Proof By Theorem 22.4, for all x G Ω, we have
f(x)>f(x*)
Substituting Df(x*)
= —\*TDh(x*)
+
Df(x*)(x-x*).
into the inequality above yields
f(x) > /(»*) - \*TDh(x*)(x
- x*).
Because Ω is convex, (1 — a)x* + ax G Ω for all a G (0,1). Thus,
h(x* + a(x - x*)) = h((l - a)x* + ax) = 0
for all a G (0,1). Premultiplying by λ* τ , subtracting \*Th(x*)
dividing by a, we get
A* T /i(x* + a(x - x*)) a
= 0, and
\*Th(x*)
for all a G (0,1). If we now take the limit as a —► 0 and apply the definition of the directional derivative of λ* h at x* in the direction x — x* (see
Section 6.2), we get
\*TDh(x*)(x
- x*) = 0.
Hence,
f(x) > /(«·),
CONVEX OPTIMIZATION PROBLEMS
which implies that x* is a global minimizer of / over Ω.
525
I
Consider the general constrained optimization problem
minimize
subject to
f(x)
h(x) = 0
g(x) < 0.
As before, we assume that the feasible set is convex. This is the case if, for
example, the two sets {x : h(x) = 0} and {x : g(x) < 0} are convex, because
the feasible set is the intersection of these two sets (see also Theorem 4.1).
We have already seen an example where the set {x : h(x) = 0} is convex. On
the other hand, an example where the set {x : g(x) < 0} is convex is when
the components of g = [#i,... ,gp]T are all convex functions. Indeed, the set
{x : g(x) < 0} is the intersection of the sets {x : gi(x) < 0}, i = 1 , . . . ,p.
Because each of these sets is convex (by Lemma 22.1), their intersection is
also convex.
We now prove that the Karush-Kuhn-Tucker (KKT) condition is sufficient
for a point to be a minimizer to the problem above.
Theorem 22.9 Let f : R n —> R, f G C1, be a convex function on the set of
feasible points
Ω = {x e Rn : h(x) = 0,g(x) < 0},
where h : Rn -► R m , g : R n -► Rp, h,g e C1, and Ω is convex. Suppose that
there exist x* G Ω, λ* G R m , and μ* G Rp, such that
1. μ* > 0.
2. Df(x*) + \*TDh(x*)
+ μ* τ £>0(α*) = 0 T .
3. μ*τ</(**) = 0.
Then, x* is a global minimizer of f over Ω.
D
Proof. Suppose that x G Ω. By convexity of / and Theorem 22.4,
f(x)>f(x*)
+
Df(x*)(x-x*).
Using condition 2, we get
f(x) > fix*) - \*TDh(x*)(x
- x*) - ß*TDg(x*)(x
- x*).
As in the proof of Theorem 22.8, we can show that λ* Dh(x*)(x — x*) = 0.
We now claim that μ*τDg(x*)(x
— x*) < 0. To see this, note that because
Ω is convex, (1 — a)x* + ax G Ω for all a G (0,1). Thus,
g(x* + a(x - x*)) = g((l - a)x* + ax) < 0
526
CONVEX OPTIMIZATION PROBLEMS
for all a G (0,1). Premultiplying by μ*τ > 0 T (by condition 1), subtracting
μ*Ύ g(x*) = 0 (by condition 3), and dividing by a, we get
μ*τ9(χ*
+ a{x - a?*)) - μ*Ί~ g{x*) < Q
a
~
We now take the limit as a —> 0 to obtain μ*Ύ Dg{x*){x — x*) < 0.
From the above, we have
f(x) > /(«*) - \*TDh{x*)(x
- x*) - μ*τΌ9(χ*)(χ
- x*)
for all x G Ω, which completes the proof.
I
E x a m p l e 22.7 A bank account starts out with 0 dollars. At the beginning of
each month, we deposit some money into the bank account. Denote by Xk the
amount deposited in the fcth month, k = 1,2,
Suppose that the monthly
interest rate is r > 0 and the interest is paid into the account at the end
of each month (and compounded). We wish to maximize the total amount
of money accumulated at the end of n months, such that the total money
deposited during the n months does not exceed D dollars (where D > 0).
To solve this problem we first show that the problem can be posed as a
linear program, and is therefore a convex optimization problem. Let y^ be
the total amount in the bank at the end of the fcth month. Then, yk =
(1 + r)(yk-i + Xfe), fc > 1, with yo = 0. Therefore, we want to maximize yn
subject to the constraint that Xk > 0, fc = 1 , . . . , n, and x\-\
\-xn < D. It
is easy to deduce that
yn = (1 + r)nXl
+ (1 + r)n~lx2
+ · · · + (1 + r)xn.
Let c T = [(1 + r ) n , ( l + r ) n " \ . . . , (1 + r)], e T = [1,...,1], and a; =
[ # i , . . . , xn]T. Then, we can write the problem as
maximize
subject to
cTx
eTx < D
x>0,
which is a linear program.
It is intuitively clear that the optimal strategy is to deposit D dollars
in the first month. To show that this strategy is indeed optimal, we use
Theorem 22.9. Let x* = [ D , 0 , . . . , 0 ] T G R n . Because the problem is a
convex programming problem, it suffices to show that x* satisfies the KKT
SEMIDEFINITE PROGRAMMING
527
condition (see Theorem 22.9). The KKT condition for this problem is
-οτ + μΜβτ-μ™τ
1
τ
μ( \β χ*-Ό)
= 0,
= 0,
μΜτχ*
= 0,
T
e x* - £> < 0,
-x* < 0,
μ (1) > 0,
μ (2) > 0,
eTx < D,
x > 0,
where μ^ G R and μ^ € R n . Let μ& = (1 + r)n and μ<2> = (1 + r ) n e - c.
Then, it is clear that the KKT condition is satisfied. Therefore, x* is a global
minimizer.
I
An entire book devoted to the vast topic of convexity and optimization is
[7]. For extensions of the theory of convex optimization, we refer the reader
to [136, Chapter 10]. The study of convex programming problems also serves
as a prerequisite to nondifferentiable optimization (see, e.g., [38]).
22.4
Semidefinite Programming
Semidefinite programming is a subfield of convex optimization concerned with
minimizing a linear objective function subject to a linear matrix inequality.
The linear matrix inequality constraint defines a convex feasible set over which
the linear objective function is to be minimized. Semidefinite programming
can be viewed as an extension of linear programming, where the componentwise inequalities on vectors are replaced by matrix inequalities (see Exercise 22.20). For further reading on the subject of semidefinite programming,
we recommend an excellent survey paper by Vandenberghe and Boyd [128].
Linear Matrix Inequalities and Their Properties
Consider n + 1 real symmetric matrices
Fi = Fj G M m x m , < = 0 , l , . . . , n
and a vector
x = [xi,...,x n ] T eR n .
528
CONVEX OPTIMIZATION PROBLEMS
Then,
F(x) = F0 + x i F i + · · · +
xnFn
n
= F0 + J2XiFi
2=1
is an affine function of x, because F(x) is composed of a linear term Σ7=ι
and a constant term Fo.
Consider now an inequality constraint of the form
F(x) = F0 + xiFx + · · · + xnFn
x
i^i
> 0.
The inequality constraint above is to be interpreted as the set of vectors x
such that
zTF(x)z
> 0 for all z G R m ;
that is, F(x) is positive semidefinite [or, in the usual notation, F(x) > 0].
Recall that the terms Fi represent constant matrices, x is unknown, and
F(x) = F(x)T is an affine function x. The expression F(x) = F0 + X\F\ +
• · · + xnFn > 0 is referred to in the literature as a linear matrix inequality (LMI), although the term affine matrix inequality would seem to be more
appropriate. It is easy to verify that the set {x : F(x) > 0} is convex (see
Exercise 22.20).
We can speak similarly of LMIs of the form F(x) > 0, where the requirement is for F(x) to be positive definite (rather than just positive semidefinite).
It is again easy to see that the set {x : F{x) > 0} is convex.
A system of LMIs
F i ( x ) > 0 , F 2 ( x ) > 0 , ...,
Fk(x)>0
can be represented as one single LMI:
|>i(«)
1
F(x) =
> 0.
#
L
Fk(x)\
As an example, a linear inequality involving an m x n real constant matrix A
of the form
Ax <b
can be represented as m LMIs:
b{ — ajx
> 0,
i = 1,2,..., m,
SEMIDEFINITE PROGRAMMING
529
where aj is the ith row of the matrix A. We can view each scalar inequality
as an LMI. We then represent m LMIs as one LMI:
bi — ajx
a0 x
F(x) =
>0.
bm -
a^x
With the foregoing facts as background, we can now give an example of
semidefinite programming:
minimize
subject to
ex
F(x) > 0.
The matrix property that we discuss next is useful when converting certain
LMIs or nonlinear matrix inequalities into equivalent LMIs. We start with a
simple observation. Let P be a nonsingular n x n matrix and let x = Mz,
where M G R n X n such that det M φ 0. Then, we have
xTPx
that is,
Similarly,
> 0 if and only if zTMTPMz
> 0;
P > 0 if and only if MTPM
> 0.
P > 0 if and only if MTPM
> 0.
Suppose that we have a square matrix
A
L
B
Γ
D
Then, by the observation above,
A
BT
B\ > 0 if and only if
D
I
O
A
BT
B
D
0
1
I
O >o,
where / is an identity matrix of appropriate dimension. In other words,
A
BT
B\
> 0 if and only if
D
D
B
BT
A
>0.
We now introduce the notion of the Schur complement, useful in studying
LMIs. Consider a square matrix of the form
An
Ai2
A2i
A22
530
CONVEX OPTIMIZATION PROBLEMS
where An and A22 are square submatrices. Suppose that the matrix An is
invertible. Then, we have
I
-Α21ΛΓ11
O
/
An
A21
Let
A12
A22
I
-ΑϊΪΑ12
O
An
O
I
O
A2iAn1A12\
A22-
A n = A22 - A2i A n x Λ12,
which is called the Schur complement of A n . For the case when A12 = A 2 i ,
we have
/
-A21AU
O
I
where
An
A21
An
A22
-An1'AAT
21
I
I
O
An
O
O
An
A n = A22 - A 2 i A n A21.
Hence,
"An
^■21
A2i
A22
An
O
> 0 if and only if
O
>0;
An
that is,
An
A^
> 0 if and only if An
A22
A2I
Given
An
A21
> 0 and A n > 0.
A12
A22
we can similarly define the Schur complement of A22, assuming that A22 is
invertible. We have
I
0
-A12A2J
I
An
A2\
I
An
A22
~A22
A21
O
I
Δ22
O
O
A22
where Δ22 = A n — A12A221 A 2 i is the Schur complement of A22· So, for the
case where A12 = A 2 i,
An
A21
Aji
> 0 if and only if A22 > 0 and Δ22 > 0.
A22
Many problems of optimization, control design, and signal processing can
be formulated in terms of LMIs. To determine whether or not there exists a
point x such that F(x) > 0 is called a feasibility problem. We say that the
LMI is nonfeasible if no such solution exists.
SEMIDEFINITE PROGRAMMING
531
Example 22.8 We now present a simple example illustrating the LMI feasibility problem. Let A G R m x m be a given real constant square matrix.
Suppose that we wish to determine if A has all its eigenvalues in the open left
half-complex plane. It is well known that this condition is true if and only if
there exists a real symmetric positive definite matrix P such that
ATP + PA<0,
or, equivalently,
-ATP
-PA>0
(also called the Lyapunov inequality; see [16]). Thus, the location of all eigenvalues of A being in the open left half-complex plane is equivalent to feasibility
of the following matrix inequality:
|>
[O
O
1
-ATP-PA\
>0;
that is, the existence of P = PT > 0 such that ATP + PA < 0.
We now show that finding P = PT > 0 such that AT P + PA < 0 is
indeed an LMI. For this, let
where
X\
X2
%2
%m+l
yx<m
%2m—l
#2ra-l
' ' '
m(m + 1)
%n
J
532
CONVEX OPTIMIZATION PROBLEMS
We next define the following matrices:
Pi
P2 =
Pn
1 0 0
0 0 0
0 0 0
·· •
·· •
·· •
0
0
0
0 0 0
·· •
o
0 1 0
1 0 0
0 0 0
·· •
·· •
· •
0'
0
0
0
0
0
0
0
0···
0
0
0
0
··♦
0
0
0
0
···
0
[0
0 0 ···
1|
Note that Pi has only nonzero elements corresponding to Xi in P. Let
Fi = -ATPi
i = 1,2,..., n.
- PiA,
We can then write
ATP
+ PA = xx ( A T P i + ΡλΑ}
+ xn(ATPn
= -XlFi
+ x2 ( A T P
2
+ P 2 A ) + ·· ·
+ PnA)
- X2F2
XnFn
<0.
Let
Then,
if and only if
F(x) = x i F i 4- X2F2 + · · · +
P = P
T
>0
and ΑΎΡ +
xnFn.
ΡΑ<0
F(x) > 0.
Note that this LMI involves a strict inequality. Most numerical solvers do
not handle strict inequalities. Such solvers simply treat a strict inequality (>)
as a non-strict inequality (>).
I
SEMIDEFINITE PROGRAMMING
533
LMI Solvers
The inequality F(x) = Fo + xiFi + · · · + xnFn > 0 is called the canonical
representation of an LMI. Numerical LMI solvers do not deal directly with
LMIs in canonical form because of various inefficiencies. Instead, LMI solvers
use a structured representation of LMIs.
We can use MATLAB's LMI toolbox to solve LMIs efficiently. This toolbox
has three types of LMI solvers, which we discuss next.
Finding a Feasible Solution Under LMI Constraints
First, we discuss MATLAB's LMI solver for solving the feasibility problem
defined by a given system of LMI constraints. Using this solver, we can solve
any system of LMIs of the form
NTC(XU
. . . , Xk)N
< MTK(XU
...,
Xk)M,
where X\,...,
Xk are matrix variables, N is the left outer factor, M is the
right outer factor, C{X\,...,
Xk) is the left inner factor, and 7 £ ( X i , . . . , Xk)
is the right inner factor. The matrices £(·) and 7£(·) are, in general, symmetric
block matrices. We note that the term left-hand side refers to what is on the
"smaller" side of the inequality 0 < X. Thus in X > 0, the matrix X is still
on the right-hand side because it is on the "larger " side of the inequality.
We now provide a description of an approach that can be used to solve
the given LMI system feasibility problem. To initialize the LMI system description, we type s e t l m i s ( [ ] ) . Then we declare matrix variables using the
command lmivar. The command lmiterm allows us to specify LMIs that
constitute the LMI system under consideration. Next, we need to obtain an
internal representation using the command getlmis. We next compute a feasible solution to the LMI system using the command f easp. After that, we
extract matrix variable values with the command dec2mat. In summary, a
general structure of a MATLAB program for finding a feasible solution to the
set of LMIs could have the form
setlmis([])
lmivar
lmiterm
lmiterm
getlmis
feasp
dec2mat
We now analyze these commands in some detail so that the reader can write
simple MATLAB programs for solving LMIs after completing this section.
534
CONVEX OPTIMIZATION PROBLEMS
First, to create a new matrix-valued variable, say, X, in the given LMI system,
we use the command
X = lmivar(type,structure)
The input type specifies the structure of the variable X. There may be three
structures of matrix variables. When type=l, we have a symmetric block
diagonal matrix variable. The input type=2 refers to a full rectangular matrix
variable. Finally, type=3 refers to other cases. The second input s t r u c t u r e
gives additional information on the structure of the matrix variable X. For
example, the matrix variable X could have the form
|\Di
O
\ O
D2
···
···
[O
O
···
Ol
0\
Dr\
where each Di is a square symmetric matrix. For the example above we would
use type=l. The matrix variable above has r blocks. The input s t r u c t u r e
is then an r x 2 matrix whose iih row describes the ith block, where the first
component of each row gives the corresponding block size, while the second
element of each row specifies the block type. For example,
X = l m i v a r ( l , [ 3 1])
specifies a full symmetric 3 x 3 matrix variable. On the other hand,
X = l m i v a r ( 2 , [ 2 3])
specifies a rectangular 2 x 3 matrix variable. Finally, a matrix variable S of
the form
0
0
0"
" «1
0
0
0
Sl
0
_0
0
0
S2
S3
S3
s4 .
can be declared as follows:
S = l m i v a r ( l , [ 2 0;2 1])
Note above that the second component of the first row of the second input has
the value of zero; that is, s t r u c t u r e d , 2 ) =0. This describes a scalar block
matrix of the form
£>i = β ι / 2 .
Note that the second block is a 2 x 2 symmetric full block.
SEMIDEFINITE PROGRAMMING
535
We next take a closer look at a command whose purpose is to specify the
terms of the LMI system of interest. This command has the form
lmiterm(termid,A,B,flag)
We briefly describe each of the four inputs of this command. The first input,
termid, is a row with four elements that specify the terms of each LMI of
the LMI system. We have t e r m i d ( l ) = n to specify the left-hand side of the
nth LMI. We use t e r m i d ( l ) = - n to specify the right-hand side of the nth
LMI. The middle two elements of the input termid specify the block location.
Thus termid(2,3) = [i j ] refers to the term that belongs to the (i,j) block
of the LMI specified by the first component. Finally, termid(4) =0 for the
constant term, termid (4) =X for the variable term in the form AXB, while
termid(4)=-X for the variable term in the form ΑΧΎΒ.
The second and
third inputs of the command lmiterm give the values of the left and right
outer factors; that is, A and B give the values of the constant outer factors in
the variable terms AXB and AX B. Finally, the fourth input to lmiterm
serves as a compact way to specify the expression
AXB
(AXB)T.
+
Thus, f l a g = , s ' can be used to denote a symmetrized expression. We now
illustrate the command above on the following LMI:
PA + (PA)T
< 0.
We have one LMI with two terms. We could use the following description of
this single LMI:
lmiterm ([1 1 1 P],1,A)
lmiterm([l 1 1 -P],Α',1)
On the other hand, we can describe this LMI compactly using the f l a g as
follows:
lmiterm([l 1 1 Ρ ϋ , Ι , Α , ' β ' )
Now, to solve the feasibility problem we could have typed
[tmin,xfeas] = feas(lmis)
In general, for a given LMI feasibility problem of the form
find
such that
x
L(x) < R(x),
the command f easp solves the auxiliary convex problem
minimize
subject to
t
L(x) < R(x) -f 11.
536
CONVEX OPTIMIZATION PROBLEMS
The system of LMIs is feasible if the minimal t is negative. We add that the
current value of t is displayed by f easp at each iteration.
Finally, we convert the output of the LMI solver into matrix variables using
the command
P = dec2mat(lmis,xfeas,P).
Example 22.9 Let
Ai =
-1
0
0
-1
and
A2 =
-2
1
0
-1
We use the commands of the LMI Control Toolbox discussed above to write
a program that finds P such that P > O.5J2 and
A[P
+ PAX <O,
Ä%P + PA2
<0.
The program is as follows:
A_l = [-1 0;0 - 1 ] ;
A_2 = [-2 0;1 - 1 ] ;
setlmis( [] )
P = lmivar(l,[2,l])
lmiterm([l 1 1 P] , Α _ 1 ' , 1 , ' s ' )
lmiterm([2 1 1 P ] , A _ 2 ' , 1 , ' s ' )
lmiterm([3 1 1 0 ] , . 5 )
lmiterm([-3 1 1 P ] , l , l )
lmis=getlmis;
[tmin,xfeas] = feasp(lmis);
P = dec2mat(lmis,xfeas,P)
Minimizing a Linear Objective Under LMI Constraints
The next solver we discuss solves the convex optimization problem
minimize
subject to
cTx
A(x) < B(x).
The notation A(x) < B{x) is shorthand notation for a general structured
LMI system.
This solver is invoked using the function mincx. Thus, to solve a mincx
problem, in addition to specifying the LMI constraints as in the f easp problem, we also declare the linear objective function. Then we invoke the function
SEMIDEFINITE PROGRAMMING
537
mincx. We illustrate and contrast the f easp and mincx solvers in the following
example.
Example 22.10 Consider the optimization problem
minimize
subject to
cTx
Ax < 6,
where
4 5J
"l
5
's]
l"
1
3 ,
2
1
b =
18
U\
We first solve the feasibility problem; that is, we find an x such that Ax < 6,
using the f easp solver. After that, we solve the minimization problem above
using the mincx solver. A simple MATLAB code accomplishing these tasks is
shown below.
°/e Enter problem d a t a
A = [1 1;1 3 ; 2 1] ;
b = [8 18 14] ' ;
c = [-4 - 5 ] >;
setlmis([]) ;
X = lmivar(2,[2 1]);
lmiterm([l 1 1 X],A(1,:) l ) ;
lmiterm([l 1 1 0 ] , - b ( D )
lmiterm([l 2 2 X],A(2,:) l ) ;
lmiterm([l 2 2 0 ] , - b ( 2 ) )
lmiterm([l 3 3 X],A(3,:) 1);
lmiterm([l 3 3 0 ] , - b ( 3 ) )
lmis = getlmis;
disp('
feasp r e s u l t
')
[tmin,xfeas] = f e a s p ( l m i s ) ;
x_feasp = dec2mat(lmis,xfeas,X)
disp('
mincx r e s u l t
')
[objective,x_mincx] = mincx(lmis,c,[0.0001 1000 0 0 1])
The f easp function produces
•Efeasp —
-64.3996
-25.1712
538
CONVEX OPTIMIZATION PROBLEMS
The mincx function produces
Xri
3.0000
5.0000
In the next example, we discuss the function def ex, which we can use to
construct the vector c used by the LMI solver mincx.
Example 22.11 Suppose that we wish to solve the optimization problem
minimize
subject to
trace(P)
ATP
+ PA < 0
P>0
where trace(P) is the sum of the diagonal elements of P . We can use the
function mincx to solve this problem. However, to use mincx, we need a vector
c such that
cTx = trace (P).
After specifying the LMIs and obtaining their internal representation using,
for example, the command lmisys=getlmis, we can obtain the desired c with
the following MATLAB code,
q = decnbr(lmisys);
c = zeros(q,l);
for j = l:q
Pj = defcxClmisys,j,P);
c(j) = trace(Pj);
end
Having obtained the vector c, we can use the function mincx to solve the
optimization problem.
I
Minimizing a Generalized Eigenvalue Under LMI Constraints
This problem can be stated as
minimize
subject to
λ
C(x) < D(x)
0 < B{x)
A(x) < XB{x).
Here, we need to distinguish between standard LMI constraints of the form
C(x) < D(x) and linear-fractional LMIs of the form A(x) < XB(x), which
SEMIDEFINITE PROGRAMMING
539
are concerned with the generalized eigenvalue λ. The generalized eigenvalue
minimization problem under LMI constraints can be solved using the solver
gevp. The basic structure of the gevp solver has the form
[ l o p t , x o p t ] = gep{lmisys,nflc}
which returns lopt, the global minimum of the generalized eigenvalue, and
xopt, the optimal decision vector variable. The argument lmisys is the system of LMIs, C{x) < D(x), C(x) < D(x), and A(x) < XB(x) for λ = 1.
As in the previous solvers, the corresponding optimal values of the matrix
variables are obtained using dec2mat. The number of linear-fractional constraints is specified with nf l c . There are other inputs to gevp but they are
optional. For more information on this type of the LMI solver, we refer the
reader to the LMI Lab in MATLAB's Robust Control Toolbox user's guide.
E x a m p l e 22.12 Consider the problem of finding the smallest a such that
P>0
T
AP
+ PA<
-αΡ,
where
^-1.1853 0.9134
0.2785
0.9058
-1.3676
0.5469
0.1270
0.0975
-3.0000
This problem is related to finding the decay rate of the stable linear differential
equation x = Ax. Finding a that solves the optimization problem above can
be accomplished using the following LMIs:
A = [-1.1853
0.9134
0.2785
0.9058
-1.3676
0.5469
0.1270
0.0975
-3.0000];
setlmis( [] ) ;
P = lmivar(l,[3 1])
lmiterm([-l 1 1 P ] , 1,1)
°/0 P
lmiterm([l 1 1 0],.01)
7, P >= 0.01*1
lmiterm([2 1 1 Ρΐ,Ι,Α,'ε') °/0 linear fractional constraint—--LHS
lmiterm( [-2 1 1 P ] , 1,1)
°/0 linear fractional constraint—--RHS
lmis = getlmis;
[gamma,P_opt] = gevp(lmis,1);
P = dec2mat(lmis,P_opt,P)
alpha = -gamma
The result is
a = 0.6561
and
P =
0.6996
-0.7466
-0.0296
-0.7466
0.8537
-0.2488
-0.0296
-0.2488
3.2307
540
CONVEX OPTIMIZATION PROBLEMS
Notice that we used P > 0.011 in place of P > 0.
I
More examples of linear matrix inequalities in system and control theory
can be found in the book by Boyd et al. [16].
A quick introduction to MATLAB's LMI toolbox is the tutorial that can
be accessed with the command Imidem within MATLAB. In addition to the
MATLAB's LMI toolbox, there is another toolbox for solving LMIs called
LMITOOL, a built-in software package in Scilab toolbox, developed at INRIA
in Prance. Scilab offers free software for numerical optimization. There is a
version of LMITOOL for MATLAB that can be obtained from the website of
the Scilab Consortium.
Yet Another LMI Package, YALMIP, for solving LMIs was developed in
Switzerland in the Automatic Control Laboratory at ETH. YALMIP is an
"intuitive and flexible modelling language for solving optimization problems
in MATLAB."
LMIs are tools of modern optimization. The following quote on numerical linear algebra from Gill, Murray, and Wright [52, p. 2] applies as well to
the contents of this chapter: "At the heart of modern optimization methods
are techniques associated with linear algebra. Numerical linear algebra applies not simply in optimization, but in all fields of scientific computation, including approximation, ordinary differential equations, and partial differential
equations. The importance of numerical linear algebra to modern scientific
computing cannot be overstated. Without fast and reliable linear algebraic
building blocks, it is impossible to develop effective optimization methods;
without some knowledge of the fundamental issues in linear algebra, it is impossible to understand what happens during the transition from equations in
a textbook to actual computation."
EXERCISES
22.1 Find the range of values of the parameter a for which the function
/ ( x i , £ 2 , # 3 ) = 2χχχ3 — x\ — x\ — 5x\ — 2ax\X2 — 4x2X3
is concave.
22.2 Consider the function
f(x) =
-xTQx-xTb,
where Q = QT > 0 and x,b e R n . Define the function φ : R -► R by
φ(ά) = f(x + ad), where cc, d € R n are fixed vectors and d ^ O . Show that
φ{ά) is a strictly convex quadratic function of a.
EXERCISES
541
22.3 Show that f(x) = x\X2 is a convex function on Ω = {[a, ma\T : a G R},
where m is any given nonnegative constant.
22.4 Suppose that the set Ω = {x : ft(a?) = c} is convex, where h : R n —► R
and c G R. Show that /i is convex and concave over Ω.
22.5 Find all subgradients of
x G R,
f(x) = \x\,
at x = 0 and at x = 1.
22.6 Let Ω C R n be a convex set, and fa : Ω —> R, z = 1 , . . . ,£ be convex
functions. Show that m a x { / i , . . . , fa} is a convex function.
Note: The notation m a x { / i , . . . , fa} denotes a function from Ω to R such that
for each x G Ω, its value is the largest among the numbers fa(x), i = 1 , . . . , £.
22.7 Let Ω C R n be an open convex set. Show that a symmetric matrix
Q G R n is positive semidefinite if and only if for each #, y G Ω, (x — y)TQ(x —
y) > 0. Show that a similar result for positive definiteness holds if we replace
the ">" by ">" in the inequality above.
22.8 Consider the problem
minimize
subject to
- | | A x — 6|| 2
x\ +
h xn — 1
X\ , . . . , Χγι -^ U
(see also Exercise 21.9). Is the problem a convex optimization problem? If yes,
give a complete proof. If no, explain why not, giving complete explanations.
22.9 Consider the optimization problem
minimize
subject to
f(x)
x G Ω,
where f(x) = x\x\, where x = [xi,X2] T , and Ω — {x G R 2 : X\ = X2, x\ >
0}. (See also Exercise 21.8.) Show that the problem is a convex optimization
problem.
22.10 Consider the convex optimization problem
minimize
subject to
f(x)
x G Ω.
542
CONVEX OPTIMIZATION PROBLEMS
Suppose that the points y G Ω and z G Ω are local minimizers. Determine
the largest set of points G C Ω for which you can be sure that every point in
G is a global minimizer.
22.11 Suppose that we have a convex optimization problem on R 3 .
a. Consider the following three feasible points: [1,0,0] T , [0,1,0] T , [0,0,1] T .
Suppose that all three have objective function value 1. What can you say
about the objective function value of the point (1/3) [1,1,1] T ? Explain
fully.
b. Suppose we know that the three points in part a are global minimizers.
What can you say about the point (1/3)[1,1,1] T ? Explain fully.
22.12 Consider the optimization problem
. . .
minimize
1 τ Qx
^
-x
subject to
Ax = b,
where Q G R n x n , Q = QT > 0, A G R m X n , and rank A = m.
a. Find all points satisfying the Lagrange condition for the problem (in
terms of Q, A, and b).
b . Are the points (or point) global minimizers for this problem?
22.13 Let / : R n —► R, / G C1, be a convex function on the set of feasible
points
Ω = {x G R n :ajx + bi > 0, i = l , . . . , p } ,
where o i , . . . , ap G R n , and 6 i , . . . , bp G R. Suppose that there exist x* G 5,
and μ* G R p , μ* < 0, such that
Df(x*)+ £
μ>Τ=Ο τ ,
jeJ(x*)
where J(x*) = {i : a ^ « * -f 6» = 0}. Show that x* is a global minimizer of /
over Ω.
22.14 Consider the problem: minimize \\x\\2 (x G R n ) subject to aTx > &,
where a G R n is a nonzero vector and b G R, b > 0. Suppose that x* is a
solution to the problem.
a. Show that the constraint set is convex.
b . Use the KKT theorem to show that α τ χ * = b.
EXERCISES
543
c. Show that x* is unique, and find an expression for x* in terms of a and
b.
22.15 Consider the problem
minimize
subject to
cTcc,
x > 0.
a;Gln
For this problem we have the following theorem (see also Exercise 17.16).
Theorem: A solution to this problem exists if and only if c > 0. Moreover,
if a solution exists, 0 is a solution.
a. Show that the problem is a convex programming problem.
b . Use the first-order necessary condition (for set constraints) to prove the
theorem.
c. Use the KKT condition to prove the above theorem.
22.16 Consider a linear programming problem in standard form.
a. Derive the KKT condition for the problem.
b . Explain precisely why the KKT condition is sufficient for optimality in
this case.
c. Write down the dual to the standard form primal problem (see Chapter 17).
d. Suppose that x* and λ* are feasible solutions to the primal and dual,
respectively. Use the KKT condition to prove that if the complementary
slackness condition (c T — λ* Α)χ* = 0 holds, then x* is an optimal
solution to the primal problem. Compare the above with Exercise 21.15.
22.17 Consider two real-valued discrete-time signals, s^ and s^2\ defined
over the time interval [l,n]. Let s\ ' and s\ ' be the values at time i of the
signals s^ and s^2\ respectively. Assume that the energies of the two signals
are 1 [i.e., ( s ^ ) 2 + · · · + (s^)2 = 1 and (s^)2 + · · · + (ώ 2 ) ) 2 = 1].
Let Sa be the set of all signals that are linear combinations of s^ and s^
with the property that for each signal in 5 a , the value of the signal over all
time is no smaller than a e R. For each s G 5 a , if s = Xi8^ + # 2 ^ 2 \ we call
x\ and #2 the coefficients of s.
We wish to find a signal in Sa such that the sum of the squares of its
coefficients is minimized.
a. Formulate the problem as an optimization problem.
544
CONVEX OPTIMIZATION PROBLEMS
b . Derive the Karush-Kuhn-Tucker conditions for the problem.
c. Suppose that you have found a point satisfying the Karush-Kuhn-Tucker
conditions. Does this point satisfy the second-order sufficient condition?
d. Is this problem a convex optimization problem?
22.18 Let a probability vector be any vector p G R n satisfying pi > 0,
i = 1 , . . . , n, and p\ Λ
V pn = 1.
Let p e M n and q G R n be two probability vectors. Define
D(p, q) = Pl log ( V ) + · · · + Pn log {^j
,
where "log" is the natural logarithm function.
a. Let Ω be the set of all probability vectors (with fixed n). Show that Ω is
convex.
b . Show that for each fixed p, the function / defined by f(q) = D(p, q) is
convex over Ω.
c. Show the following: D{p,q) > 0 for any probability vectors p and q.
Moreover, D(p, q) = 0 if and only if p — q.
d. Describe an application of the result of part c.
22.19 Let Ω C R n be a nonempty closed convex set and z G R n be a given
point such that z 0 Ω. Consider the optimization problem
minimize
subject to
\\x — z\\
x G Ω.
Does this problem have an optimal solution? If so, is it unique? Whatever
your assertion, prove it.
Hint: (i) If X\ and #2 are optimal solutions, what can you say about X3 —
(xi + X2V2? (ii) The triangle inequality states that ||x-fi/||<||ic||H-||i/||,
with equality holding if and only if x = ay for some a > 0 (or x = 0 or
y = 0).
22.20 This exercise is about semidefinite programming.
a. Show that if A G R n X n and B G R n X n are symmetric and A > 0,
B > 0, then for any a G (0,1), we have a A + (1 — a)B > 0. As usual,
the notation "> 0" denotes positive semidefiniteness.
EXERCISES
545
Consider the following semidefinite programming problem, that is, an
optimization problem with linear objective function and linear matrix
inequality constraints:
minimize
subject to
cTx
P 0 + Y^ XjFj > 0>
where x = [xi,... , x n ] T € M.n is the decision variable, c G Mn, and
P 0 , -PI, . . . , Fn G R m x m are symmetric.
Show that this problem is a convex optimization problem.
c. Consider the linear programming problem
minimize
subject to
c x
Ax > 6,
where A G R m x n , b G R m , and the inequality Ax > b has the usual
elementwise interpretation. Show that this linear programming problem
can be converted to the problem in part b.
Hint: First consider diagonal Fj.
22.21 Suppose that you have a cake and you need to divide it among n
different children. Suppose that the ith child receives a fraction xi of the
cake. We will call the vector x = [ # i , . . . , x n ] T an allocation. We require
that every child receives at least some share of the cake, and that the entire
cake is completely used up in the allocation. We also impose the additional
condition that the first child (i = 1) is allocated a share that is at least twice
that of any other child. We say that the allocation is feasible if it meets all
these requirements.
A feasible allocation x is said to be proportionally fair if for any other
allocation y,
i=i
Xl
a. Let Ω be the set of all feasible allocations. Show that Ω is convex.
b . Show that a feasible allocation is proportionally fair if and only if it solves
the following optimization problem:
maximize
y^log(a^)
i=l
subject to
x G Ω.
546
CONVEX OPTIMIZATION PROBLEMS
22.22 Let Ui : R —> R, Ui G C1, i = 1 , . . . , n, be a set of concave increasing
functions. Consider the optimization problem
n
maximize
2_\ Ui (xi)
i=l
n
subject to
2_] χΐ — C->
i=\
where C > 0 is a given constant.
a. Show that the optimization problem above is a convex optimization problem.
b . Show that cc* = [#*,... , # * ] T is an optimal solution to the optimization problem if and only if there exists a scalar μ* > 0 such that
x* = argmax x (£/*(#) — μ*χ). [The quantity Ui(x) has the interpretation of the "utility" of x, whereas μ* has the interpretation of a "price"
per unit of x.]
c. Show that £ ? = i x* - C.
22.23 Give an example of a function / : R 2 —► R, a set Ω = {# : g(x) < 0},
and a regular point x* e Ω, such that the following all hold simultaneously:
1. x* satisfies the FONC for set constraint Ω (Theorem 6.1).
2. x* satisfies the KKT condition for inequality constraint g(x) < 0 (Theorem 21.1).
3. x* satisfies the SONC for set constraint Ω (Theorem 6.2).
4. x* does not satisfy the SONC for inequality constraint g{x) < 0 (Theorem 21.2).
Be sure to show carefully that your choice of/, Ω = {x : g(x) < 0}, and x*
satisfies all the conditions above simultaneously.
22.24 This question is on duality theory for nonlinear programming problems, analogous to the theory for linear programming (Chapter 17). (A version
for quadratic programming is considered in Exercise 17.24.)
Consider the following optimization problem:
minimize
subject to
f(x)
g(x) < 0,
where / : R n —> R is convex, each component of g : R n —> R m is convex, and
f,9 £ C1. Let us call this problem the primal problem.
EXERCISES
547
Define the dual of the problem above as
maximize
subject to
ς(μ)
μ > 0,
where q is defined by
ς(μ) = min / ( χ , μ ) ,
xeRn
Ύ
with l(x, μ) = f(x) + μ g(x) the Lagrangian at x, μ.
Prove the following results:
a. If XQ and μ 0 are feasible points in the primal and dual, respectively, then
/(#o) > #(Mo)· This *s the weak duality lemma for nonlinear programming, analogous to Lemma 17.1.
b . If Xo and μ 0 are feasible points in the primal and dual, and f(xo) =
#(μ 0 ), then XQ and μ 0 are optimal solutions to the primal and dual,
respectively.
c. If the primal has an optimal (feasible) solution, then so does the dual,
and their objective function values are equal. (You may assume regularity.) This is the duality theorem for nonlinear programming, analogous
to Theorem 17.2.
22.25 Consider the matrix
M =
1 7
7
1
- 1 2
-1
2
5
where 7 is a parameter.
a. Find the Schur complement of Af (1,1);
b . Find the Schur complement of M ( 2 : 3,2:3) (the bottom-right 2 x 2 submatrix of M , using MATLAB notation).
22.26 Represent the Lyapunov inequality
ATP + PA<0,
where
as a canonical LMI.
0
-1
1
-2
548
CONVEX OPTIMIZATION PROBLEMS
22.27 Let A, B, and R be given matrices such that R = R > 0. Suppose
that we wish to find a symmetric positive definite matrix P satisfying the
following quadratic inequality:
ATP + PA + PBR1BTP
< 0.
Represent this inequality in the form of LMIs. (This inequality should not
be confused with the algebraic Riccati inequality, which has a negative sign in
front of the third term.)
22.28 Let
A =
-0.9501
-0.2311
-0.6068
-0.4860
-0.8913
-0.7621
-0.4565
-0.0185
-0.8214
Write a MATLAB program that finds a matrix P satisfying 0.1 J3 < P < J3
and
ATP + PA < 0.
CHAPTER 23
ALGORITHMS FOR CONSTRAINED
OPTIMIZATION
23.1
Introduction
In Part II we discussed algorithms for solving unconstrained optimization
problems. In this chapter we present some simple algorithms for solving special constrained optimization problems. The methods here build on those of
Part II.
We begin our presentation in the next section with a discussion of projected
methods, including a treatment of projected gradient methods for problems
with linear equality constraints. We then consider Lagrangian methods. Finally, we consider penalty methods. This chapter is intended as an introduction
to ideas underlying methods for solving constrained optimization problems.
For an in-depth coverage of the subject, we refer the reader to [11].
23.2
Projections
The optimization algorithms considered in Part II have the general form
x(k+i)
=x(k)+akdW,
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
549
550
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
where <rk' is typically a function of V / ( x ^ ) . The value of x^ is not constrained to lie inside any particular set. Such an algorithm is not immediately
applicable to solving constrained optimization problems in which the decision
variable is required to lie within a prespecified constraint set.
Consider the optimization problem
minimize
subject to
f(x)
x G Ω.
If we use the algorithm above to solve this constrained problem, the iterates x^ may not satisfy the constraints. Therefore, we need to modify the
algorithms to take into account the presence of the constraints. A simple
modification involves the introduction of a projection. The idea is as follows.
If x^ + akSk) is in Ω, then we set χ^+^ = χ^ + akSk) as usual. If, on
the other hand, x^ + akd> ' is not in Ω, then we "project" it back into Ω
before setting x^k+1\
To illustrate the projection method, consider the case where the constraint
set Ω C l n is given by
Ω = {x : li < Xi < Ui, i = l , . . . , n}.
In this case, Ω is a "box" in R n ; for this reason, this form of Ω is called a box
constraint. Given a point a ; E E n , define y = H[x] G Mn by
(
Ui
if Xi > Ui
Xi
if U <Xi
U
\ixi < U.
< Ui
The point Π[χ] is called the projection of x onto Ω. Note that H[x] is actually
the "closest" point in Ω to x. Using the projection operator Π, we can modify
the previous unconstrained algorithm as follows:
x( f c + 1 >=n[x( f c >+a f e d ( f e >].
Note that the iterates x^ now all lie inside Ω. We call the algorithm above
a projected algorithm.
In the more general case, we can define the projection onto Ω:
H[x] = argmin||z — x||.
In this case, Π[χ] is again the "closest" point in Ω to x. This projection
operator is well-defined only for certain types of constraint sets: for example,
closed convex sets (see Exercise 22.19). For some sets Ω, the "arg min" above
is not well-defined. If the projection Π is well-defined, we can similarly apply
the projected algorithm
x(k+V=n[xW+akdW}.
PROJECTIONS
551
In some cases, there is a formula for computing H[x], For example, if Ω represents a box constraint as described above, then the formula given previously
can be used. Another example is where Ω is a linear variety, which is discussed
in the next section. In general, even if the projection Π is well-defined, computation of TL[x] for a given x may not be easy. Often, the projection H[x]
may have to be computed numerically. However, the numerical computation
of H[x] itself entails solving an optimization algorithm. Indeed, the computation of Tl[x] may be as difficult as the original optimization problem, as is
the case in the following example:
||cc||2
x G Ω.
minimize
subject to
Note that the solution to the problem in this case can be written as Π[0].
Therefore, if 0 0 Ω, the computation of a projection is equivalent to solving
the given optimization problem.
As an example, consider the projection method applied specifically to the
gradient algorithm (see Chapter 8). Recall that the vector — V / ( x ) points
in the direction of maximum rate of decrease of / at x. This was the basis
for gradient methods for unconstrained optimization, which have the form
_ x(k) _ a f c V / ( x ^ ) , where α& is the step size. The choice of the step
X(M)
size ctk depends on the particular gradient algorithm. For example, recall that
in the steepest descent algorithm, ot^ = argminQ>Q
The projected version of the gradient algorithm has the form
x(k+i)
= n[x(k)
_
ahVf(xw)].
We refer to the above as the projected gradient algorithm.
E x a m p l e 23.1 Consider the problem
. . .
minimize
subject to
1 τ Qx
^
-x
||cc||2 = 1,
where Q = Q > 0. Suppose that we apply a fixed-step-size projected gradient
algorithm to this problem.
a. Derive a formula for the update equation for the algorithm (i.e., write
down an explicit formula for x^k+1^ as a function of x^k\ Q, and the
fixed step size a). You may assume that the argument in the projection
operator to obtain x^ is never zero.
b . Is it possible for the algorithm not to converge to an optimal solution
even if the step size a > 0 is taken to be arbitrarily small?
c. Show that for 0 < a < 1/Amax (where A max is the largest eigenvalue of
Q), the fixed-step-size projected gradient algorithm (with step size a)
552
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
converges to an optimal solution, provided that x^ is not orthogonal to
the eigenvectors of Q corresponding to the smallest eigenvalue. (Assume
that the eigenvalues are distinct.)
Solution:
a. The projection operator in this case simply maps any vector to the closest
point on the unit circle. Therefore, the projection operator is given by
U[x] = x/||sc||, provided that x Φ 0. The update equation is
a;<fc+1> - &(*<*> - aQx{k))
= ßk(I -
aQ)x^k\
where ßk = l/||(i" — aQ)x^\\
(i.e., it is whatever constant scaling is
needed to make x^k+l^ have unit norm).
b . If we start with x^ being an eigenvector of Q, then χ^ = χ^ for all
k. Therefore, if the corresponding eigenvalue is not the smallest, then
clearly the algorithm is stuck at a point that is not optimal.
c. We have
xik+V = ßk(I - aCfixW
= ßk(I-*Q)(v[k)v1
= ßk(y[k)(I
+ '-. + yWvn)
- aQ)v! + . . . + y W ( J -
But (I — aQ)vi = (1 — a\i)vi,
to Vi. Hence,
x(k+1)
= ßk(y[k)(l
aQ)vn).
where λ^ is the eigenvalue corresponding
- aX^V! + ■ ■ ■ + y{k){\ -
a\n)vn),
which means that y\
= ßky\ (1 — α\).
In other words, y\
ß(k)yf\\ - a\i)k, where β^ = Tlk~oßk- We rewrite χΜ as
=
i=l
k
(
- ■VPU
J)
n
+ ~~
Σ
Jk)
Assuming that y\' φ 0, we obtain
yJV_ = ι/<0)(1-αΑ«)*
y[k) »ί 0) (1-αλ0*
yf_ /1 - «A«
y^U-^i
=
Using the fact that (1 — αλ*)/(1 — αλι) < 1 (because the λ* > λι for
i > 1 and a < 1/A max ), we deduce that
(k)
(k)
which implies that x^
υ
'
y\
—> V\, as required.
I
PROJECTED GRADIENT METHODS WITH LINEAR CONSTRAINTS
23.3
553
Projected Gradient Methods with Linear Constraints
In this section we consider optimization problems of the form
minimize
subject to
f{x)
Ax = fr,
where f : Rn -+R, A e Rmxn,
m < n, rank A = ra, b G R m . We assume
1
throughout that / G C . In the problem above, the constraint set is Ω = {x :
Ax = b}. The specific structure of the constraint set allows us to compute
the projection operator Π using the orthogonal projector (see Section 3.3).
Specifically, H[x] can be defined using the orthogonal projector matrix P
given by
P =
In-AT(AAT)-1A
(see Example 12.5). Two important properties of the orthogonal projector P
that we use in this section are (see Theorem 3.5):
1. P =
PT.
2. P2 = P.
Another property of the orthogonal projector that we need in our discussion
is given in the following lemma.
Lemma 23.1 Let v G Rn. Then, Pv = 0 if and only if v G ΊΖ(ΑΤ).
In
other words, λί(Ρ) = 1Z(AT). Moreover, Av = 0 if and only if v G TZ(P);
that is, λί(Α) =ΊΖ(Ρ).
□
Proof =>: We have
Pv - ( I n -
AT{AAT)-1A)v
=
v-AT(AAT)-1Av.
v =
AT(AAT)~1Av
If Pv = 0, then
and hence v G 1Z(AT).
<=: Suppose that there exists u G R m such that v = A u. Then,
Pv = (J n - ATu -
AT(AAT)-1A)ATu
AT(AAT)-1AATu
= 0.
Hence, we have proved that ftf(P) = 1Z(AT).
Using an argument similar to that above, we can show that N(A) = ΊΖ(Ρ).
■
554
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
Recall that in unconstrained optimization, the first-order necessary condition for a point x* to be a local minimizer is V/(x*) = 0 (see Section 6.2).
In optimization problems with equality constraints, the Lagrange condition
plays the role of the first-order necessary condition (see Section 20.4). When
the constraint set takes the form {x : Ax = 6}, the Lagrange condition can
be written as PVf(x*) = 0, as stated in the following proposition.
Proposition 23.1 Let x* G Rn be a feasible point
and only if x* satisfies the Lagrange condition.
Then, PV/(cc*) = 0 t /
Q
Proof By Lemma 23.1, PVf(x*)
= 0 if and only if we have V/(x*) G
K(AT).
This is equivalent to the condition that there exists λ* G R m such
that V/(a5*) + AT\* = 0, which together with the feasibility equation Ax =
6, constitutes the Lagrange condition.
I
Recall that the projected gradient algorithm has the form
k
X(
+V =U[x™
-akVf{xW)].
For the case where the constraints are linear, it turns out that we can express
the projection Π in terms of the matrix P as follows:
Π[χ<*> - a f c V/(xW)] = x{k) - a fc PV/(ai' fc )),
assuming that x^ G Ω. Although the formula above can be derived algebraically (see Exercise 23.4), it is more insightful to derive the formula using a
geometric argument, as follows. In our constrained optimization problem, the
vector — Vf(x) is not necessarily a feasible direction. In other words, if x^
is a feasible point and we apply the algorithm as(fc+1) = x^ — afcV/(a:^),
then £c(fc+*) need not be feasible. This problem can be overcome by replacing
—V/(as(fe)) by a vector that points in a feasible direction. Note that the set of
feasible directions is simply the nullspace λί(Α) of the matrix A. Therefore,
we should first project the vector — V / ( x ) onto λί(Α).
This projection is
equivalent to multiplication by the matrix P. In summary, in the projection
gradient algorithm, we update x^ according to the equation
X(*+V
akPVf(x{k)).
= XW -
The projected gradient algorithm has the following property.
Proposition 23.2 In a projected gradient algorithm, if x^
each x^ is feasible; that is, for each k > 0, Ax^ = b.
is feasible, then
G
Proof. We proceed by induction. The result holds for k = 0 by assumption.
Suppose now that AxW = b. We now show that Aaj(fc+1^ = b. To show this,
first observe that P V / ( x ( f c ) ) € Λί(Α). Therefore,
Ax(fc+1> = A{x{k)
= Ax^
= b,
-akP\7f{x{k)))
-
akAPVf{x{k))
PROJECTED GRADIENT METHODS WITH LINEAR CONSTRAINTS
which completes the proof.
555
I
The projected gradient algorithm updates x^
in the direction of
- P V / ( a i ^ ) . This vector points in the direction of maximum rate of decrease of / at x^ along the surface defined by Ax = 6, as described in the
following argument. Let x be any feasible point and d a feasible direction such
that ||d|| = 1. The rate of increase of / at x in the direction d is ( V / ( x ) , d).
Next, we note that because d is a feasible direction, it lies in Af(A) and hence
by Lemma 23.1, we have d G ΊΖ(Ρ) — ΊΖ(Ρ ). So, there exists v such that
d = Pv. Hence,
( V / ( x ) , d ) = (Vf(x),PTv)
= (PV/(x),«>.
By the Cauchy-Schwarz inequality,
(PVf(x),v)<\\PVf(x)\\\\v\\
with equality if and only if the direction of v is parallel with the direction of
P V / ( x ) . Therefore, the vector —PVf(x) points in the direction of maximum
rate of decrease of / at x among all feasible directions.
Following the discussion in Chapter 8 for gradient methods in unconstrained optimization, we suggest the following gradient method for our constrained problem. Suppose that we have a starting point χ(°\ which we
assume is feasible; that is, Ax^
= b. Consider the point x = x^ —
aPS/f(x^),
where a G R. As usual, the scalar a is called the step size.
By the discussion above, x is also a feasible point. Using a Taylor series
expansion of / about x^ and the fact that P — P2 = PT P, we get
/(a:<0> - a P V / ( x ( 0 ) ) ) = / ( x ( 0 ) ) - aVf(x{0))TPVf(x{0))
+ o(a)
2
= /(χ(°))-α||Ρν/(χ(°))|| +ο(α).
Thus, if P V / ( x ( ° ) ) Φ 0, that is, x^ does not satisfy the Lagrange condition,
then we can choose an a sufficiently small such that f(x) < f(x^),
which
means that x = x^ — aPVf(x^)
is an improvement over χ(°\ This is
the basis for the projected gradient algorithm a^fc+1) = x^ — ajkPV/(i' f c '),
where the initial point x^ satisfies Ax^ — b and α^ is some step size. As
for unconstrained gradient methods, the choice of α& determines the behavior
of the algorithm. For small step sizes, the algorithm progresses slowly, while
large step sizes may result in a zigzagging path. A well-known variant of the
projected gradient algorithm is the projected steepest descent algorithm, where
QLk is given by
ak = argmin/(£c (fc) aPVf(x{k))).
The following theorem states that the projected steepest descent algorithm
is a descent algorithm, in the sense that at each step the value of the objective
function decreases.
556
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
Theorem 23.1 If {x^} is the sequence of points generated by the projected
steepest descent algorithm and if P V / ( x ^ ) φ 0, then /(x^ fc+1 ^) <
f(x^).
D
Proof First, recall that
x (fc+i) =
x(k)
_ •a PV/(ajW),
fc
where α^ > 0 is the minimizer of
4>k{a) = /(x (fe)- a P V / ( x ( f e ) ) )
over all a > 0. Thus, for a > 0, we have
0fc(ajfe) < 0fc(a).
By the chain rule,
^(0) = ^f(O)
= -v/(x ( f e ) -0Pv/(x (fc) )) T PV/(x (fe) )
= -V/(x(fc))TPV/(x(fc)).
Using the fact that P = P2 = PT P, we get
44(0) = - V / ( * W ) T P T P V / ( x W ) = -||PV/(x< f c ))|| 2 < 0,
because PVf(x^)
Φ 0 by assumption. Thus, there exists ö > 0 such that
(j>k{0) > <t>k{ot) for all α G (0,a]. Hence,
/(x<fc+1>) = 0 f c K ) < 4>k{ä) < φφ) = /(x (fc) ),
which completes the proof of the theorem.
I
In Theorem 23.1 we needed the assumption that P V f ( x ^ ) φ 0 to prove
that the algorithm possesses the descent property. If for some k, we have
P V / ( a j ^ ) = 0, then by Proposition 23.1 the point x^ satisfies the Lagrange
condition. This condition can be used as a stopping criterion for the algorithm.
Note that in this case, x^1)
= x^k\ For the case where / is a convex
function, the condition PVf{x^)
= 0 is, in fact, equivalent to x^ being a
global minimizer of / over the constraint set {x : Ax = b}. We show this in
the following proposition.
Proposition 23.3 The point x* G W1 is a global minimizer of a convex
function f over {x : Ax = b} if and only if PVf(x*) = 0.
□
Proof We first write h{x) — Ax — b. Then, the constraints can be written
as h{x) = 0, and the problem is of the form considered in earlier chapters.
LAGRANGIAN ALGORITHMS
557
Note that Dh(x) = A. Hence, x* G Mn is a global minimizer of / if and only
if the Lagrange condition holds (see Theorem 22.8). By Proposition 23.1, this
is true if and only if PVf(x*) = 0, and this completes the proof.
I
For an application of the projected steepest descent algorithm to minimum
fuel and minimum amplitude control problems in linear discrete systems, see
[78].
23.4
Lagrangian Algorithms
In this section we consider an optimization method based on the Lagrangian
function (see Section 20.4). The basic idea is to use gradient algorithms to
update simultaneously the decision variable and Lagrange multiplier vector.
We consider first the case with equality constraints, followed by inequality
constraints.
Lagrangian Algorithm for Equality Constraints
Consider the following optimization problem with equality constraints:
minimize
subject to
f{x)
h{x) = 0
where h : R n —> M m . Recall that for this problem the Lagrangian function is
given by
l(x,\) = f(x) +
\Th(x).
Assume that f,h e C2; as usual, denote the Hessian of the Lagrangian by
L{x,X).
The Lagrangian algorithm for this problem is given by
x{k+i) = x(k)
_
a f c ( V /( x (
fc
)) +
Dh(xW)T\{k)),
Notice that the update equation for x^ is a gradient algorithm for minimizing
the Lagrangian with respect to its x argument, and the update equation for
is a gradient algorithm for maximizing the Lagrangian with respect to
its λ argument. Because only the gradient is used, the method is also called
the first-order Lagrangian algorithm.
The following lemma establishes that if the algorithm converges, the limit
must satisfy the Lagrange condition. More specifically, the lemma states
that any fixed point of the algorithm must satisfy the Lagrange condition. A
fixed point of an update algorithm is simply a point with the property that
when updated using the algorithm, the resulting point is equal to the given
point. For the case of the Lagrangian algorithm, which updates both x^ and
558
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
λ ' ^ vectors, a fixed point is a pair of vectors. If the Lagrangian algorithm
converges, the limit must be a fixed point. We omit the proof of the lemma
because it follows easily by inspection.
Lemma 23.2 For the Lagrangian algorithm for updating x^ and λ^ ', the
pair (x*,\*) is a fixed point if and only if it satisfies the Lagrange condition.
D
Below, we use (a?*, λ*) to denote a pair satisfying the Lagrange condition.
Assume that L(x*,A*) > 0. Also assume that x* is a regular point. With
these assumptions, we are now ready to state and prove that the algorithm is
locally convergent. For simplicity, we will take ctk and ßk to be fixed constants
(not depending on &), denoted a and /?, respectively.
Theorem 23.2 For the Lagrangian algorithm for updating x^ and λ ' \
provided that a and β are sufficiently small, there is a neighborhood o/(a?*, λ*)
such that if the pair (x^°\ λ^ ') is in this neighborhood, then the the algorithm
converges to (χ*,λ*) with at least a linear order of convergence.
□
Proof. We can rescale x and λ by appropriate constants (so that the assumptions are preserved) and effectively change the relative values of the step sizes
for the update equations. Therefore, without loss of generality, we can take
β = α.
We will set up our proof by introducing some convenient notation. Given a
pair ( χ , λ ) , let w = [ χ τ , λ τ ] τ be the (n + m)-vector constructed by concatenating x and λ. Similarly define w^ = [ χ ^ τ , λ ^ ] τ and w* =
k*TiT
Define the map U :
by
aT
U(w) =
x-a(Vf(x)
+
Dh{x)T\)
X + ah{x)
Then, the Lagrangian algorithm can be rewritten as
w{k+i)
=
U(w{k)).
We now write ||w(fc+1) — w*\\ in terms of \\w^ — tu*||, where || · || denotes
the usual Euclidean norm. By Lemma 23.2, w* = [x*T,\*T]T
is a fixed point
(k+i)
u(w^).
Therefore,
of w
=
llwifc+i) - ii;*|| = \\U(w^k)) -
U(w*)\\.
Let DU be the (matrix) derivative of U:
DU(w) = I + a
-L(x,\)
Dh(x)
-Dh(x)T
O
LAGRANGIAN ALGORITHMS
559
By the mean value theorem (see Theorem 5.9),
U(wW)
- U(w*) = G(w^k))(w^
- w*),
where G(w^) is a matrix whose rows are the rows of DU evaluated at points
that lie on the line segment joining w^ and w* (these points may differ from
row to row). Taking norms of both sides of the equation above,
\\U(wW) - U(w*)\\ < ||G(ti;<fe>)||||ti;<fc> - ti7*||.
Finally, combining the above, we have
l l ^ + i ) _ w*|| < \\G(w^)\\\\w^
- w*\\.
We now claim that for sufficiently small a > 0, ||DC/(it;*)|| < 1. Our
argument here follows [11, Section 4.4]. Let
Γ-£(**, λ*)
[ Dh(x*)
-£>h(**)Tl
J'
O
so that DU(w*) = I + aM. Hence, to prove the claim, it suffices to show
that the eigenvalues of M all lie in the open left-half complex plane.
For any complex vector y, let yH represent its complex conjugate transpose
(or Hermitian) and $i(y) its real part. Let λ be an eigenvalue of M and
w = [ χ τ , λ τ ] τ φ 0 be a corresponding eigenvector. Now, !R (wHMw)
=
Si(A)||tü||2. However, from the structure of Af, we can readily see that
3? (wHMw)
= -$l(xHL(x*,\*)x)
=
- $l(xHDh(x*)Tλ)
+
tt(\HDh{x*)x)
-M{xHL(x*,\*)x).
By the assumption that L(x*,A*) > 0, we know that $l(xHL(x*, X*)x) > 0
if x φ 0. Therefore, comparing the two equations above, we deduce that
9ί(λ) < 0, as required, provided that x is nonzero, as we now demonstrate.
Now, suppose that x — 0. Because w is an eigenvector of M", we have
Mw = Xw. Extracting the first n components, we have Dh(x*)TX = 0.
By the regularity assumption, we deduce that λ = 0. This contradicts the
assumption that w φ 0. Hence we conclude that x ^ 0 , which completes the
proof of our claim that for sufficiently small a > 0, ||£>J7(tu*)|| < 1.
The result of the foregoing claim allows us to pick constants η > 0 and
K < 1 such that for all w = [ccT,A ] T satisfying ||tu — w*\\ < 77, we have
||^(κ;)|| < K (this follows from the continuity of DU and norms).
To complete the proof, suppose that \\w^ — w*\\ < 77. We will show by
induction that for all k > 0, \\w^ —w*\\ <η and ||tt/ fc+1 ) —w*\\ < K\\W^ tu* ||, from which we conclude that w^ converges to w* with at least linear
order of convergence. For k = 0, the result follows because \\w^ — w*\\ < η
by assumption, and
||™(1) - u > l < ||G(u;<0>)||||fi;<0> -w*\\<
K\\W^ - w*\\,
560
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
which follows from
11^(0) -w*\\
<η
So
suppose that the result holds for k.
This implies that ||G(ii/ fc ))|| < κ. To show the k -f 1 case, we write
||„,(*+ΐ) _ w*\\ < ||G(«; (fc) )||||ti;W - w*|| < /*||™(/c) - ti7*|| < η.
This means that ||G(ti;( fc+1 ))|| < Ä, from which we can write
| | ^ + 2 ) _ ^*|| < ||G(u;(fc+1>)||||ii;(fc+1> - n;*|| < /*||κ/* +1 ) - w*||.
This completes the proof.
I
Lagrangian Algorithm for Inequality Constraints
Consider the following optimization problem with inequality constraints:
minimize
subject to
f{x)
g(x) < 0,
where g : R n —► R p . Recall that for this problem the Lagrangian function is
given by
1(χ,μ) = /(χ) +
μτ9{χ).
As before, assume that f,g G C2; as usual, denote the Hessian of the Lagrangian by Σ(χ,μ).
The Lagrangian algorithm for this problem is given by
x(k+i) = x(k)
1
_ afc(v/(a;(fc)) + Z?0(a:<*>)Vfc)),
μ(*+ ) = [μ(*)+Α β (χ(*))] + >
where [·] + = max{-,0} (applied componentwise). Notice that, as before, the
update equation for x^ is a gradient algorithm for minimizing the Lagrangian
with respect to its x argument. The update equation for μ^ is a projected
gradient algorithm for maximizing the Lagrangian with respect to its μ argument. The reason for the projection is that the KKT multiplier vector is
required to be nonnegative to satisfy the KKT condition.
The following lemma establishes that if the algorithm converges, the limit
must satisfy the KKT condition. As before, we use the notion of a fixed point
to state the result formally. The proof is omitted because the result follows
easily by inspection.
L e m m a 23.3 For the Lagrangian algorithm for updating x^ and μ^\ the
pair (χ*,μ*) is a fixed point if and only if it satisfies the KKT condition. □
As before, we use the notation (as*,/x*) to denote a pair satisfying the
KKT condition. Assume that Ζ(χ*,μ*) > 0. Also assume that x* is a
regular point. With these assumptions, we are now ready to state and prove
that the algorithm is locally convergent. As before, we will take α^ and ßk to
LAGRANGIAN ALGORITHMS
561
be fixed constants (not depending on A:), denoted a and /?, respectively. Our
analysis examines the behavior of the algorithm in two phases. In the first
phase, the "nonactive" multipliers decrease to zero in finite time and remain
at zero thereafter. In the second phase, the x^ iterates and the "active"
multipliers converge jointly to their respective solutions, with at least a linear
order of convergence.
Theorem 23.3 For the Lagrangian algorithm for updating x^ and μ^\
provided that a and β are sufficiently small, there is a neighborhood o/(a?*, μ*)
such that if the pair (x^°\ μ ^ ) is in this neighborhood, then (1) the nonactive
multipliers reduce to zero in finite time and remain at zero thereafter and (2)
the algorithm converges to (χ*,μ*) with at least a linear order of convergence.
D
Proof As in the proof of Theorem 23.2, we can rescale x and μ by appropriate
constants (so that the assumptions are preserved) and effectively change the
relative values of the step sizes for the update equations. Therefore, without
loss of generality, we can take β = a.
We set up our proof using the same vector notation as before. Given a pair
(χ,μ), let w = [ χ τ , μ τ ] τ be the (n + p)-vector constructed by concatenating
x and μ. Similarly define w^ = [ χ ^ τ , μ ^ τ ] τ and w* = [ # * τ , μ * τ ] τ .
Define the map U as
U{w)
x
a(Vf{x) +
Ό9(χ)τμ)
μ + ag(x)
Also, define the map Π by
n\w]
X
[μ}+
Then, the update equations can be rewritten as
!£,(*+!) =n[l/(ti;( f c ))].
Because Π is a projection onto the convex set {w = [ χ τ , μ τ ] τ : μ > 0}, it
is a nonexpansive map (see [12, Proposition 3.2]), which means that ||Π[ν] —
Π Η | | < ||u-iu||.
We now write ||κ;^ + 1 ) — w*\\ in terms of \\w^ —w*\\, where || · || denotes
the usual Euclidean norm. By Lemma 23.3, w* = [χ* τ , μ * τ ] τ is a fixed point
of w(k+i) = u(wW).
Therefore,
| | ^ + D _™*|| = \\U[U(w^)}
-U[U(w*)]\\
< ||I/(tü(fc))-E7(tO||
562
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
by the nonexpansiveness of Π. Let DU be the (matrix) derivative of U:
DU(w) = I + a
-Dg(x)T
O
-Σ(χ,μ)
Dg(x)
By the mean value theorem,
U(w^)
- U{w*) =
G(w^)(w{k) ■ t O ,
where G(w^) is a matrix whose rows are the rows of DU evaluated at points
that lie on the line segment joining w^ and w* (these points may differ from
row to row). Taking norms of both sides of the equation above yields
<\\G{w (*h \\wW-w*
\\U(wW)-U(w*)\\
Finally, combining the above, we obtain
Utflifc+i) _ w*\\ < \\G{wW)\\\\wW
- w*\\.
Let gA represent those rows of g corresponding to active constraints (at
x*) and gA represent the remaining rows of g. [Recall that by regularity,
DgA(x*) has full rank.] Given a vector μ, we divide it into two subvectors μΑ
and μΑ, according to active and nonactive components, respectively. (Note
that μΑ = 0, the zero vector.) Next, write wA = [χτ ,μΑ]τ and
UA(wA)
x
α(ν/(χ)
+
^A + OigA(x)
so that
DUA(wA)
ϋ9Α(χ)τμΑ)
=I +a
-Ώ(χ,μΑ)
DgA(x)
-DgA(x)T
O
Finally, let GA be such that UA (wA ') - UA(w*A) GA(w^)(w^
•wX)(by
the mean value theorem as before).
We organize the remainder of our proof into four claims.
Claim 1: For sufficiently small a > 0, ||DC7A(WA)II < lThe argument here parallels that of the proof of Theorem 23.2. So for the
sake of brevity we omit the details.
The result of claim 1 allows us to pick constants η > 0, δ > 0, and κΑ < 1
such that for all w = [ χ τ , μ τ ] τ satisfying ||u; — w*\\ < η, \\GA(WA)\\ < K>A,
and ΑΑ(2Β) < —ie, where e is the vector with all components equal to 1. The
first inequality follows from claim 1 and the continuity of DUA(-) and || · ||.
The second follows from the fact that the components of gA{x*) are negative.
Let K = max{||G(iu)|| : \\w — w*\\ < η}, which we assume to be at least
1; otherwise, set κ = 1. Now pick ε > 0 such that εκε^αδ^ < η. We can do
this because the left side of this inequality goes to 0 as ε —► 0. Assume for
LAGRANGIAN ALGORITHMS
563
convenience that ko = ε/(αδ) is an integer; otherwise, replace all instances of
ε/(αδ) by the smallest integer that exceeds it (i.e., round it up to the closest
integer).
For the remainder of this proof, let tt/°) satisfy ||κ/°) — w*\\ < ε.
Claim 2: For k = 0 , . . . , fe0, || w (/c) - w* || < 77.
To prove the claim, we show by induction that \\w^ — w*\\ < εκ* (which
is bounded above by η provided that k < ko). For k = 0, by assumption
||ty(°) — w* || < £ = SKP, as required. For the inductive step, suppose that
||ti,(*)-ti;*|| <eKkiork<
k0. Now, using ||™^ + 1 )-it;*|| < ||G(ti;<fc>)||||tf;<fc>ti7*|| and the fact that \\w^ —w*\\ < η,
Hu/fc+i) _ w *|| < ||G(ii7<*>)||||ti;(fc) -w*\\<
κ(εκ*) = ε«* + 1 ,
and the result now follows by induction.
Claim 3: For k = 0 , . . . , ko, μ^ ^ is monotonically nonincreasing in &, and
l~k =® ( w n i c n ls equal to μ^).
By claim 2, gA(x^)
< ~^e f° r all fc = 0 , . . . , &o. Hence, for k < ko,
μ(*+1) = [ μ « + α β ϊ ( χ ( « ) ]
< [ μ « -αδβ}
+
+
{k)
<u
which establishes nonincreasing monotonicity.
To show that μ^ = 0 , suppose that for some nonactive component Z, we
have μ\ °' > 0. By the monotonicity above, μ\ ' > 0 for k = 0 , . . . , k0. Hence,
^o)z=^o-l)+Qgi{xiko-1))
k=0
But by claim 2, gi(x( ) < -δ for all k = 0 , . . . ,fc0 - 1. Hence, μ ζ (Μ <
ε — koaö < 0, which is a contradiction.
Finally, we will state and prove claim 4, which completes the proof of the
theorem.
Claim 4: For k > k0, we have μχ' =0 = μ | , ||it;^ + — ΐϋ£|| < « A I I ^ A _
wX\\, and {{w^ -w*\\ < η.
We use induction. For k — ko, we have ||u>(fco) - 117*|| < η by claim 2,
μ*>> = 0 by claim 3. Hence,
„ Λ , + ΐ ) = U[UA(w{AkQ)) + a % ( ^ ) ) T 4 f c o ) ] =
n[UA(w^)].
k)
564
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
Because μ^ = 0, it is, similarly, also true that w\ —
~ «£11 = \\Tl[UA(w^)}
\\W(A°+1)
Π[17Α(^)]·
Thus,
- U[UA(w*A)}\\
<\\UA(w{Ako))-UA(wX)\\
<\\GA(wAko))\\\\wAko)-wX\\,
where \\GA(wAko))\\
< κΑ because \\w(k0) - w*\\ < η. Hence, \\wAk°+1)
-
wA\\ < KA\\WA °' — wA\\, as required.
For the inductive step, suppose that the result holds for k > ko- Now,
gA(x^)
< —Se and
4 f c + 1 ) = [4 fc> + <*9A(x{k))}+ < [0 - aSe}+ = 0,
which implies that μ4
=
— 0. It follows that
U[UA(wAk+1))},
and now using the same argument as in the case of k — ko above we get
rf+2)
- «ail < KA\\wAk+1) -wX\\. Finally,
||u,(fc+1) - to*|| = K f c + 1 ) - ιι£|| < KA\\wAk) - w\\\ < η.
Because KA < 1, claim 4 implies that w^
a linear order of convergence.
converges to κ;*, with at least
I
An application of Lagrangian algorithms to a problem in decentralized rate
control for sensor networks appears in [24], [25], and [93]. The proof above is
based on [25].
23.5
Penalty Methods
Consider a general constrained optimization problem
minimize
f(x)
subject to x e Ω
We now discuss a method for solving this problem using techniques from
unconstrained optimization. Specifically, we approximate the constrained optimization problem above by the unconstrained optimization problem
minimize
f(x)+/yP(x))
PENALTY METHODS
565
where 7 G R is a positive constant and P : R n —► R is a given function. We
then solve the associated unconstrained optimization problem and use the
solution as an approximation to the minimizer of the original problem. The
constant 7 is called the penalty parameter, and the function P is called the
penalty function. Formally, we define a penalty function as follows.
Definition 23.1 A function P : R n —> R is called a penalty function for
the constrained optimization problem above if it satisfies the following three
conditions:
1. P is continuous.
2. P(x) > 0 for all x G R n .
3. P(x) = 0 if and only if x is feasible (i.e., x G Ω).
|
Clearly, for the unconstrained problem above to be a good approximation
to the original problem, the penalty function P must be chosen appropriately.
The role of the penalty function is to "penalize" points that are outside the
feasible set.
To illustrate how we choose penalty functions, consider a constrained optimization problem of the form
minimize
subject to
f{x)
gi(x) < 0,
i = l,...,p,
where / : R n —> R, gi : R n —» R, i = l , . . . , p . Considering only inequality constraints is not restrictive, because an equality constraint of the form
h(x) = 0 is equivalent to the inequality constraint ||/i(x)|| 2 < 0 (however, see
Exercise 21.25 for a caveat). For the constrained problem above, it is natural that the penalty function be defined in terms of the constraint functions
p i , . . . , g p . A possible choice for P is
Ρ{χ) = Σ,9?(χ)>
where
gf{x) = max{0, <;,(*)} = l
\j9i\x)
*
"
11 9%[χ) > 0.
We refer to this penalty function as the absolute value penalty function, because it is equal to Σ | # ζ ( # ) | , where the summation is taken over all constraints that are violated at x. We illustrate this penalty function in the
following example.
566
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
P(x)
0
Figure 23.1
H-
2
g+ for Example 23.2.
E x a m p l e 23.2 Let <7i,#2 : R —► R be defined by <7i(x) — a; — 2, <72(#) =
— (x + 1) 3 . The feasible set defined by {x G R : 9\{x) < 0,g2(x) < 0} is simply
the interval [—1,2]. In this example, we have
x<2
x — 2 otherwise,
ot
fli(ic) = max{0,^2(a?)} =
and
P(x)=gZ(x)+gZ(x)=
3
-(x + 1)
if rc> - 1
otherwise,
x- 2
if a: > 2
if - 1 < x < 2
<0
(x + 1) 3 i f x < - l .
Figure 23.1 provides a graphical illustration of g+ for this example.
I
The absolute value penalty function may not be differentiable at points x
where gi(x) = 0, as is the case at the point x = 2 in Example 23.2 (notice,
though, that in Example 23.2, P is differentiable at x = —1). Therefore, in
such cases we cannot use techniques for optimization that involve derivatives.
A form of the penalty function that is guaranteed to be differentiable is the
Courant-Beltrami penalty function, given by
P{x) = J2{gt{x))2.
In the following discussion we do not assume any particular form of the
penalty function P. We only assume that P satisfies conditions 1 to 3 given
in Definition 23.1.
The penalty function method for solving constrained optimization problems
involves constructing and solving an associated unconstrained optimization
PENALTY METHODS
567
problem and using the solution to the unconstrained problem as the solution to
the original constrained problem. Of course, the solution to the unconstrained
problem (the approximated solution) may not be exactly equal to the solution
to the constrained problem (the true solution). Whether or not the solution
to the unconstrained problem is a good approximation to the true solution
depends on the penalty parameter 7 and the penalty function P. We would
expect that the larger the value of the penalty parameter 7, the closer the
approximated solution will be to the true solution, because points that violate
the constraints are penalized more heavily. Ideally, in the limit as 7 —> 00, the
penalty method should yield the true solution to the constrained problem. In
the remainder of this section, we analyze this property of the penalty function
method.
Example 23.3 Consider the problem
minimize
subject to
x
Qx
||x|| 2 = 1,
where Q = QT > 0.
a. Using the penalty function P(x) = (\\x\\2 — l ) 2 and penalty parameter
7, write down an unconstrained optimization problem whose solution χΊ
approximates the solution to this problem.
b . Show that for any 7, χΊ is an eigenvector of Q.
c. Show that ||cc7||2 - 1 — 0(1/7) as 7 —► 00.
Solution:
a. The unconstrained problem based on the given penalty function is
minimize xTQx
+ 7(||cc||2 — l ) 2 .
b . By the FONC, χΊ satisfies
2Qx1 + 47(||χ 7 || 2 - ΐ)χΊ = 0.
Rearranging, we obtain
where λ 7 is a scalar. Hence, χΊ is an eigenvector of Q. (This agrees with
Example 20.8.)
c. Now, λ 7 = 27(1 — ||a?7||2) < A max , where A max is the largest eigenvalue
of Q. Hence, ||α 7 || 2 - 1 = -A m a x /(27) = 0(1/7) as 7 -> 00.
|
568
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
We now analyze the penalty method in a more general setting. In our
analysis, we adopt the following notation. Denote by x* a solution (global
minimizer) to the problem. Let P be a penalty function for the problem.
For each k = 1,2,..., let 7^ £ R be a given positive constant. Define an
associated function 9(7^, ·) : R n —► R by
q(jk,x) =
f(x)+7kP(x).
For each A;, we can write the following associated unconstrained optimization
problem:
minimize (7(7*;, x).
Denote by x^ a minimizer of #(7&,χ). The following technical lemma describes certain useful relationships between the constrained problem and the
associated unconstrained problems.
Lemma 23.4 Suppose that {7^} is a nondecreasing sequence; that is, for each
k, we have 7^ < 7^+1· Then, for each k we have
1.
q(lk+ux^)>q(lk,x^).
2. Ρ(χ(* + 1 >)<Ρ(*<*>).
3. / ( x ( f c + 1 ) ) > / ( x ( / c ) ) .
I f(x*)>q(7k,x{k))>f(x{k)).
D
Proof We first prove part 1. From the definition of q and the fact that {7^}
is an increasing sequence, we have
9 ( 7 f c + i,aJ
(fc+1)
) - f(x{k+1))
Now, because x^
+ jk+1P(x(k+1))
f(x(k+V)+nP(x(k+1)).
>
is a minimizer of (7(7*;, x),
= /(* ( f c ) ) + 7 *P(* ( f c ) ) < f(x{k+1))
q(lk,x(k))
+
7 f c P(x
(fc+1)
).
Combining the above, we get part 1.
We next prove part 2. Because x ^ and x( fc+1 ) minimize (7(7*;,x) and
(/(7fc+i,x), respectively, we can write
q{lk,x(k))
{k+1)
q(lk+ux
< /(x ( f e + 1 ) ) +
= f(x{k))+jkP(x{k))
{k+1)
)
= f(x
)
{k+1)
+7k+iP(x
{k)
)
< f(x )
7fe P(*<
+
fc+1
>),
lk+iP(x{k)).
Adding the inequalities above yields
7fc P(x
(fe)
) + 7fc+1 P(*( fc+1 >) <
(k)
lk+1P{x
)
+ 7*Ρ(α^+1>).
Rearranging, we get
( 7fc+1 - 7 f c )P(*( f e + 1 >) < ( 7 Η ΐ - 7 θ Φ ( * ) ) ·
PENALTY METHODS
569
We know by assumption that 7^+1 > 7^. If 7^+1 > 7^, then we get
P(x( fc+1 )) < P(x^).
If, on the other hand, 7^+1 = 7^, then clearly
k
Therefore, in either case, we
x(fc+i) _ x(k) a n ( j s o p( x (fc+i)) = p(x( )).
arrive at part 2.
We now prove part 3. Because x^ is a minimizer of g(7fc>x)> w e obtain
e(7fc,x(fc)) = f(x{k)) + ikP(x{k))
f(x{k+1))+ikP(x{k+1)).
<
Therefore,
/(* ( f c + 1 ) ) > f(x{k))
From part 2 we have P(x^)
therefore, we get
- P(x ( / e + 1 ) )).
+ 7k(P(x{k))
> 0, and 7^ > 0 by assumption;
— Ρ(χ^^)
f(x{k+1))
>
f(x{k)).
Finally, we now prove part 4. Because x^ is a minimizer of ^(7^,05), we
get
/(**) + 7fcP(**) > <z(7fc, ^ (fc) ) = f(x(k)) + 7kP(x(k)).
Because x* is a minimizer for the constrained optimization problem, we have
P(x*) = 0. Therefore,
/(«*)>/(«(fc))+7ibP(*(fc))·
Because P ( a ^ ) > 0 and 7^ > 0,
/(**) > q{lk,x{k))
>
f(x{k)),
which completes the proof.
I
With the above lemma, we are now ready to prove the following theorem.
Theorem 23.4 Suppose that the objective function f is continuous and 7^ —►
00 as k —> 00. Then, the limit of any convergent subsequence of the sequence
{x^} is a solution to the constrained optimization problem.
□
Proof. Suppose that {x^™^} is a convergent subsequence of the sequence
{x^}.
(See Section 5.1 for a discussion of sequences and subsequences.) Let
x be the limit of {x^™^}. By Lemma 23.4, the sequence {</(7fc, x^)} is nondecreasing and bounded above by f(x*). Therefore, the sequence {#(7*;, x^)}
has a limit q* = lim^—oo 2(7*., a ? ^ ) such that q* < f(x*) (see Theorem 5.3).
Because the function / is continuous and f(x^rnk^) < f(x*) by Lemma 23.4,
we have
lim / (V m f c )) = / ( l i m x{mk))
= f(x) < /(**)·
Because the sequences {/(a^mfc))} and {g(7m/c,cc(mfc))} both converge, the
sequence {7m fc P(a^ m ^)} = {(K7mfc>a^mfc^) - f{x^mk^)} also converges, with
\\mlmkP{x^)
fc—>·οο
=
q*-f{x).
570
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
By Lemma 23.4, the sequence {P(x^)}
is nonincreasing and bounded from
below by 0. Therefore, {P(x^)}
converges (again see Theorem 5.3), and
hence so does {P(x^rnk^)}. Because 7mfc —>· co we conclude that
lim P(x ( m f c ) ) = 0.
k—>oo
By continuity of P , we have
0 = lim P(x ( m f c ) ) = P ( lim x (mfc) ] - P ( £ ) ,
fc—>·οο
\k~*°°
)
and hence x is a feasible point. Because /(x*) > f(x) from above, we conclude
that x must be a solution to the constrained optimization problem.
|
If we perform an infinite number of minimization runs, with the penalty
parameter 7^ —> oo, then Theorem 23.4 ensures that the limit of any convergent subsequence is a minimizer x* to the original constrained optimization
problem. There is clearly a practical limitation in applying this theorem. It is
certainly desirable to find a minimizer to the original constrained optimization
problem using a single minimization run for the unconstrained problem that
approximates the original problem using a penalty function. In other words,
we desire an exact solution to the original constrained problem by solving
the associated unconstrained problem [minimize f(x) + 7P(a?)] with a finite
7 > 0. It turns out that indeed this can be accomplished, in which case we
say that the penalty function is exact However, it is necessary that exact
penalty functions be nondifferentiable, as shown in [10], and illustrated in the
following example.
Example 23.4 Consider the problem
minimize
subject to
f(x)
x G [0,1],
where f(x) = 5 — 3x. Clearly, the solution is x* = 1.
Suppose that we use the penalty method to solve the problem, with a
penalty function P that is differentiable at x* = 1. Then, P'(x*) = 0, because
P(x) = 0 for all x G [0,1]. Hence, if we let g = f + 7 P , then g'{x*) =
f'(x*) + 7P / (x*) Φ 0 for all finite 7 > 0. Hence, x* = 1 does not satisfy the
first-order necessary condition to be a local minimizer of g. Thus, P is not an
exact penalty function.
I
Here, we prove a result on the necessity of nondifferentlability of exact
penalty functions for a special class of problems.
Proposition 23.4 Consider the problem
minimize
subject to
f(x)
x G Ω,
EXERCISES
571
with Ω C M.n convex. Suppose that the minimizer x* lies on the boundary of
Ω and there exists a feasible direction d at x* such that d V/(ic*) > 0. If P
is an exact penalty function, then P is not differentiable at x*.
□
Proof We use contraposition. Suppose that P is differentiable at x*. Then,
dT VP(x*) = 0, because P(x) = 0 for all x e Ω. Hence, if we let g = / + 7 P ,
then d Vg(x*) > 0 for all finite 7 > 0, which implies that Vg(x*) φ 0.
Hence, x* is not a local minimizer of g, and thus P is not an exact penalty
function.
I
Note that the result of Proposition 23.4 does not hold if we remove the
assumption that d Vf(x*) > 0. Indeed, consider a convex problem where
V/(x*) = 0. Choose P to be differentiable. Clearly, in this case we have
Vg(x*) = V/(x*) + 7VP(x*) = 0. The function P is therefore an exact
penalty function, although differentiable.
For further reading on the subject of optimization of nondifferentiable functions, see, for example, [38]. References [11] and [96] provide further discussions on the penalty method, including nondifferentiable exact penalty functions. These references also discuss exact penalty methods involving differentiable functions; these methods go beyond the elementary type of penalty
method introduced in this chapter.
EXERCISES
23.1 Consider the constrained optimization problem
maximize
subject to
f(x)
||x|| = 1,
where /(2c) = \χΊQx
and Q = QT. We wish to apply a fixed-step-size
projected gradient algorithm to this problem:
where a > 0 and Π is the usual projection operator defined by H[x] =
argmin z G i 2 \\z — x\\ and Ω is the constraint set.
a. Find a simple formula for Π[χ] in this problem (an explicit expression in
terms of x), assuming that x φ 0.
b . For the remainder of the question, suppose that
Q =
1 0
0 2
572
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
Find the solution(s) to this optimization problem.
· Derive an expression for y(fc+1) in terms of yW and
c. Let yW = x[ /x2
a.
d. Assuming that x2 φ 0, use parts b and c to show that for any a > 0, x^
converges to a solution to the optimization problem (i.e., the algorithm
works).
e. In part d, what if x2
— 0?
23.2 Consider the problem
minimize
subject to
f(x)
x G Ω,
where f(x) = cTx and c G R n is a given nonzero vector. (Linear programming is a special case of this problem.) We wish to apply a fixed-step-size
projected gradient algorithm
χ<*+1> = n [ a j ' * ' - V / ( x ( f c ) ) ] ,
where, as usual, Π is the projection operator onto Ω (assume that for any y,
U[y] = argmin a ; € Q \\y - x\\2 is unique).
a. Suppose that for some &, x^ is a global minimizer of the problem. Is it
necessarily the case that x( fc+1 ) = χ^Ί Explain fully.
b . Suppose that for some /c, x^k+1^ = x^k\ Is it necessarily the case that
x^ is a local minimizer of the problem? Explain fully.
23.3 Consider the optimization problem
minimize
subject to
f(x)
x G Ω,
where / : R2 -> R, / G C \ and Ω = [-1,1] 2 = {x : - 1 < Xi < 1, i = 1,2}.
Consider the projected steepest descent algorithm applied to this problem:
»(fc+1)=n[a?W-afcV/(x(fc))],
where Π represents the projection operator with respect to Ω and c*k =
argmin a > 0 /(ic( f c ) — aV/(ic' f e ')). Our goal is to prove the following statement:
x(fc+i)
tion.
_ x(k) ·£ a n ( j
o n jy
^ x(k)
sa tisfies
the first-order necessary condi-
EXERCISES
573
We will do this in two parts.
a. Prove the statement above for the case where x^
Ω.
is an interior point of
b . Prove the statement for the case where x^ is a boundary point of Ω.
Hint: Consider two further subcases: (i) x^ is a corner point, and (ii)
x^ is not a corner point. For subcase (i) it suffices to take x^ = [1,1] T .
For subcase (ii) it suffices to take x^ e {x : x\ = 1,-1 < X2 < 1}.
23.4 Let A G R m x n , m < n, rank A = m, and b G M m . Define Ω = {x :
Ax = b} and let x0 G Ω. Show that for any y G Mn,
n[x 0 + y] = χο + Py,
where P = I AT(AAT)-1A.
Hint: Use Exercise 6.7 and Example 12.5.
23.5 Let / : Rn -> R be given by /(a?) = \xTQx-xTc,
where Q = QT > 0.
We wish to minimize / over {x : Ax = 6}, where A G M m x n , m < n, and
rank A = ra. Show that the projected steepest descent algorithm for this case
takes the form
x(k+l)
where
_ „(k) _
n(k)T
9K
gW =Vf{x(k))
r>n(k)
P9K
= Qx{k)
p
(k)
-c,
AT(AAT)-1A.
and P = In-
23.6 Consider the problem
minimize
subject to
-||x||2
Ax = 6,
where A G R m X n , m < n, and rank A — ra. Show that if χ(°) G {# : Ax = 6},
then the projected steepest descent algorithm converges to the solution in one
step.
23.7 Show that in the projected steepest descent algorithm, we have that
for each k:
a. 9 (*+i)Tp fl (fc)
=
b . The vector χ^+^
o.
— χ^
is orthogonal to the vector x(k+2) — a?(fc+1).
574
ALGORITHMS FOR CONSTRAINED OPTIMIZATION
23.8 Consider the optimization problem
minimize
subject to
f(x)
x G Ω,
where Ω C R n . Suppose that we apply the penalty method to this problem,
which involves solving an associated unconstrained optimization problem with
penalty function P and penalty parameter 7 > 0.
a. Write down the unconstrained problem associated with penalty function
P and penalty parameter 7.
b . Let x* be a global minimizer of the given constrained problem, and let
x1 be a global minimizer of the associated unconstrained optimization
problem (in part a) with penalty parameter 7. Show that if χΊ 0 Ω, then
23.9 Use the penalty method to solve the following problem:
minimize
subject to
x\ + 2x\
x\ + X2 = 3.
Hint: Use the penalty function P(x) = (χχ + X2 — 3) 2 . The solution you find
must be exact, not approximate.
23.10 Consider the simple optimization problem
minimize
subject to
x
x > a,
where a G R. Suppose that we use the penalty method to solve this problem,
with penalty function
P(x) = (max{a - x, 0}) 2
(the Courant-Beltrami penalty function). Given a number ε > 0, find the
smallest value of the penalty parameter 7 such that the solution obtained
using the penalty method is no further than ε from the true solution to the
given problem. (Think of e as the desired accuracy.)
23.11 Consider the problem
minimize
subject to
2I|X|1
Ax = 6,
EXERCISES
575
where A G R m x n , b G R m , m < n, and rank A = m. Let x* be the solution.
Suppose that we solve the problem using the penalty method, with the penalty
function
P(x) =
\\Ax-bf.
Let X* be the solution to the associated unconstrained problem with the
penalty parameter 7 > 0; that is, x* is the solution to
minimize
- | | # | | 2 + 7||Acc — 6|| 2 .
Δ
a. Suppose that
A = 1
l],
&=[!]·
Verify that x* converges to the solution x* of the original constrained
problem as 7 —> 00.
b . Prove that #*—►#* as7—>oo holds in general.
Hint: Use the following result: There exist orthogonal matrices U G
R m x m and VT G Rnxn such that
A=
U[S,0}VT,
where
S = diag (J\1(AAT),...,
^\m(AAr))
is a diagonal matrix with diagonal elements that are the square roots of
the eigenvalues of AAT.
The result above is called the singular value decomposition (see, e.g., [62,
p. 411]).
CHAPTER 24
MULTIOBJECTIVE OPTIMIZATION
24.1
Introduction
When an optimization problem involves only one objective function, it is a
single-objective optimization. Most engineering problems require the designer
to optimize a number of conflicting objectives. The objectives are in conflict
with each other if an improvement in one objective leads to deterioration
in another. Multiobjective problems in which there is competition between
objectives may have no single, unique optimal solution. Multiobjective optimization problems are also referred to as multicriteria or vector optimization
problems. We can formulate a multiobjective optimization problem as follows:
Find a decision variable that satisfies the given constraints and optimizes a
vector function whose components are objective functions. Formally, the mul-
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
577
578
MULTIOBJECTIVE OPTIMIZATION
tiobjective optimization problem is stated as follows:
/l(xi,£2,..-,Zn)
minimize
subject to
f(x)
=
/2(Χΐ,£2,.·.,Ζη)
x G Ω,
where / : R n —► R^ and Ω C R n . For example, the constraint set Ω can have
the form
Ω = {x : h(x) = 0, g(x) < 0} ,
where
h : R n -> R m ,
g : R n -> R p ,
m < n.
In general, we may have three different types of multiobjective optimization
problems:
■ Minimize all the objective functions.
■ Maximize all the objective functions.
■ Minimize some and maximize others.
However, as usual, any of these can be converted into an equivalent minimization problem.
24.2
Pareto Solutions
Note that multiobjective function assigns to each decision variable a multiobjective vector function value in the objective function space. A graphical
illustration of this statement is illustrated in Figures 24.1 and 24.2. In Figure 24.1 the decision variable is a point x G R 2 while the vector of objective
functions is given by / : R 2 —> R 2 . In Figure 24.2 the decision variable is
a point x G R 2 while the vector of objective functions is / : R 2 —> R 3 . In
single-objective optimization problems our goal is to find a single solution,
where we focus mainly on the decision variable space. On the other hand, in
multiobjective problems we are usually more interested in the objective space.
As pointed out by Miettinen [92, p. 11], multiobjective problems are in a sense
ill-defined because there is no natural ordering in the objective space. Miettinen [92] illustrates this statement with the following simple example. One can
say that [1,1] T is less than [3,3] T . But how do we compare [1,3] T and [3,1] T ?
In general, in multiobjective optimization problems our goal is to find good
compromises. Roughly speaking, in a multiobjective optimization problem,
a solution is optimal if there exists no other solution, within the feasible set,
PARETO SOLUTIONS
579
objective
function
space
Figure 24.1
Two-dimensional illustration of a multiobjective vector function
assigning to each decision variable a multiobjective vector function value.
that gives improved performance with regard to all the objectives. A formal
definition of an optimal point for a multiobjective optimization problem was
proposed by Francis Y. Edgeworth in 1881 and generalized by Vilfredo Pareto
in 1896. It is customary now to refer to an optimal point of a multiobjective optimization problem as the Pareto minimizer, whose formal definition is
given next.
Definition 24.1 Let / :
problem
-> R* and x G Ω be given. For the optimization
minimize
subject to
f{x)
x GΩ
a point x* G Ω is called a Pareto minimizer if there exists no x G Ω such that
for i = 1 , 2 , . . . , ^
fi(x) < fi(xm)
and for at least one z,
Mx) < fi(x*)
In other words, the point x* is a Pareto minimizer, or a nondominated solution, if there exists no other feasible decision variable x that would decrease
some objectives without causing simultaneous increase in at least one other
variable.
The set of Pareto minimizers (optimizers) is called the Pareto front, as
illustrated in Figure 24.3. Most multiobjective optimization algorithms use
580
MULTIOBJECTIVE OPTIMIZATION
objective
function
space
Figure 24.2
Three-dimensional illustration of a multiobjective vector function
assigning to each decision variable a multiobjective vector function value.
objective
function
space
Pareto front
Figure 24.3
The Pareto front is marked with a heavy line.
COMPUTING THE PARETO FRONT
581
min-max
min-min
/Pareto
I front
\
\
Pareto front
max-max
Pareto front
l
V
Pareto )
front V max-min
Figure 24.4
Pareto fronts for four possible cases of two-objective optimization.
the concept of domination. A solution is said to be nondominated if it is
Pareto optimal.
In Figure 24.4 we show different combinations of two-objective optimization
and the corresponding Pareto fronts. In particular, in the upper left, we show
the Pareto front for the case when we are minimizing both components of the
objective function vector, which we represent by "min-min." Similarly, "minmax" represents the case when we are minimizing the first objective function
and maximizing the second; and so forth.
24.3
Computing the Pareto Front
When computing the Paret front, two solutions are compared and the dominated solution is eliminated from the set of candidates of Pareto optimizers.
Thus, the Pareto front consists of nondominated solutions.
To proceed, we need some notation. Let
*r _
*L·
r *r
— \dj^
*r
, Λ/2
J
· · ·
τ
J
x
*ηΤ
n J
582
MULTIOBJECTIVE OPTIMIZATION
be the rth candidate Pareto optimal solution, r = 1,2,..., Ä, where R is the
number of current candidate Pareto solutions. Let
/(** Γ ) = [/ι(χ* Γ ),/2(** Γ ),···,Λ(** Γ )] τ
be the corresponding value of the objective function vector. For any new
solution candidate a^, we evaluate the objective function vector f(x^). We
then compare the new solution candidate with the existing Pareto solutions.
We need to consider three cases:
■ xj dominates at least one candidate solution.
■ χΐ does not dominate any existing candidate solutions.
■ χί is dominated by a candidate solution.
If χί dominates at least one candidate solution, we delete the dominated
solutions from the set and add the new solution cc·7 to the set of candidates.
In the second case, when the new candidate solution x·7 does not dominate
any of the existing candidate Pareto solutions, add this new Pareto solution
to the set of candidate Pareto solutions. Finally, in the third case, when the
new candidate solution is dominated by at least one of the existing candidate
Pareto solutions, we do not change the set of the existing candidate Pareto
solutions.
Example 24.1 Consider the two-objective minimization problem whose data
are as follows:
as« T
[5,6]
[4,5]
[3,7]
[6,8]
[1,4]
[6,7]
[2,5]
[3,6]
[2,7]
[4,7]
/(*W)T
[30,45]
[22,29]
[19,53]
[41,75]
[13,45]
[42,55]
[37,46]
[28,37]
[12,51]
[41,67]
Suppose that we wish to find nondominated pairs for this problem. Recall
that a point x* is a nondominated point if for all i and all cc,
fi{x") <
fi(x),
and at least for one component j of the objective vector, we have
/,·(**) <
fj(x).
COMPUTING THE PARETO FRONT
583
To find the Pareto front, we start with the first pair as a candidate Pareto
optimal solution and then compare the other pairs against this first pair,
replacing the first pair as necessary. We then continue with the other pairs,
building up a set of candidate Pareto solutions and modifying this set when
appropriate. The result of the search gives the following Pareto optimal set:
X(J)T
[4,5]
[1,4]
[2,7]
/(X(0)T
[22,29]
[13,45]
[12,51]
■
We now discuss an algorithm for generating the Pareto front that implements the foregoing ideas. This algorithm is a minor modification of the
algorithm of Osyczka [98, pp. 100-101]. We use the following notation. Let J
be the number of candidate solutions to be checked for optimality, while R is
the number of current candidate Pareto solutions. Recall that £ is the number
of objective functions, the dimension of the objective function vector, and n
is the dimension of the decision space, that is, the number of components of
x. The algorithm consists of eight steps.
Algorithm for Generating a Pareto Front
1. Generate an initial solution x1 and evaluate f*1 = /(as 1 ). This first
solution generated is taken as a candidate Pareto solution. Set initial
indices R := 1 and j := 1.
2. Set j := j -h 1. If j < J, then generate solution χΐ and go to step 3.
Otherwise, stop, because all the candidate solutions have already been
considered.
3. Set r := 1 and q := 0 (q represents the number of eliminated solutions
from the existing set of Pareto solutions).
4. If for alH = 1,2,...,*,
/<(**) <
fi(x*r),
then set q := q -f 1, f*R := /(x· 7 ), remember the solution that should be
eliminated, and go to step 6.
5. If for alii = 1,2,...,£,
fi(xj)
>
then go to step 2.
6. Set r := r + 1. If r < R, go to step 4.
fi(x*r),
584
MULTIOBJECTIVE OPTIMIZATION
Level sets of ^ and f2> and Pareto optimal points
Figure 24.5 Pareto optimal points in the decision space along with the level sets
of the objective functions /i and ji7. If q φ 0, remove from the candidate Pareto set the solutions that are
eliminated in step 4, add solution χΐ as a new candidate Pareto solution,
and go to step 2.
8. Set R := R + 1, x*R := xj, f*R := f{xj),
and go to step 2.
Example 24.2 We apply the algorithm above to generate the Pareto front
for the multiobjective optimization problem
. . .
minimize
subject to
I -(ΧΙ+Χ2)1
v
9 ' \
2 < #i < 6
5 < x2 < 9.
We performed 100 iterations. At each iteration we randomly generated 50
feasible points. Then we applied the algorithm above to extract from this
set of feasible points candidate Pareto optimal solutions. In Figure 24.5 we
show Pareto optimal points obtained after 100 iterations of the algorithm.
We also show level sets of the objective functions in the (#i,#2)-space. In
Figure 24.6 we show the Pareto front in the objective function space after 100
FROM MULTIOBJECTIVE TO SINGLE-OBJECTIVE OPTIMIZATION
585
Pareto optimal front
90 r
I
1
1
1
X
Γ
"
80 l·
*
70
1
60 \
50h
40 r
30
-^5
X
*
*
*x
....*. x
I
-40
. : ...x.
I
-35
x-xv\,
I
-30
*χχ
-25
χχ
-20
v
x
„ „
x x x x x x x
-15
X x
X
-10
-5
Figure 24.6
Pareto front for the problem of Example 24.2. Also marked are
the objective vector values for the remaining candidate points generated in the last
iteration.
iterations of the algorithm. The Pareto optimal points are marked with x's.
The points marked with -'s are the candidate solutions generated randomly
at the beginning of the last iteration of the algorithm.
I
We have described a simple approach to computing the Pareto front. Alternative methods include those that apply genetic algorithms to solving multiobjective optimization problems, as discussed in Deb [37], Coello Coello et
al. [31], and Osyczka [98].
24.4
From Multiobjective to Single-Objective Optimization
In some cases it is possible to deal with a multiobjective optimization problem by converting the problem into a single-objective optimization problem, so that standard optimization methods can be brought to bear. Here,
we discuss four techniques to convert a multiobjective problem to a singleobjective problem. We assume throughout that an objective function vector
f(x) = [fi(x), · · ·, fe(x)\T is given.
The first method is to form a single objective function by taking a linear
combination, with positive coefficients, of the components of the objective
586
MULTIOBJECTIVE OPTIMIZATION
function vector. Equivalently, we form a convex combination of the components of the objective function vector. In other words, we use
/(*) =
cTf(x)
as the (single) objective function, where c is a vector of positive components.
This method is also called the weighted-sum method, where the coefficients of
the linear combination (i.e., the components of c) are called weights. These
weights reflect the relative importance of the individual components in the
objective vector. Of course, it might be difficult to determine suitable weight
values.
A second method is to form a single objective function by taking the maximum of the components of the objective vector:
f(x) = m a x { / i ( x ) , . . . , fe(x)}.
In other words, we convert the multiobjective minimization problem into one
of minimizing the maximum of the components. For this reason, it is also
called the minimax method. Note that this method applies to situations where
the components of the objective vector are comparable or compatible, in the
sense that they are in the same units (e.g., they are all lengths measured in
meters, or masses in kilograms). A limitation of this method is that the resulting single objective function might not be differentiable, thereby precluding
the use of optimization methods that rely on differentiability (e.g., gradient
algorithms). However, as we show in the following, a minimax problem with
linear objective vector components and linear constraints can be reduced to
a linear programming problem.
E x a m p l e 24.3 Given vectors v i , . . . , vp G R n and scalars u\,...,
sider the minimax problem
minimize
subject to
max{i;7# + v>i, · · ·, vjx
it p , con-
+ up}
Ax < 6,
where A G R m x n and 6 G M m . Call this problem P I .
a. Consider the optimization problem
minimize
subject to
y
Ax < b
y > vJx + Ui,
i = l,...,p,
where the decision variable is the vector [ χ τ , τ / ] τ . Call this problem P2.
Show that x* solves P I if and only if [x* T , y*]T with y* = max{v[a:* +
u i , . . . , vjx* + up} solves P2.
Hint: y > max{a, 6, c} if and only if y > a, y > 6, and y > c.
FROM MULTIOBJECTIVE TO SINGLE-OBJECTIVE OPTIMIZATION
587
b . Use part a to derive a linear programming problem
minimize
subject to
c z
Az < b
that is equivalent to P I (by "equivalent" we mean that the solution to
one gives us the solution to the other). Explain how a solution to the
linear programming problem above gives a solution to P I .
Solution:
a. First suppose that x* is optimal in P I . Let y* = max{i;7#* +
ιζι,... ,VpX* + Up}. Then, [x*Tyy*]T is feasible in P2. Let [ x T , y ] T
be any feasible point in P2. Then (by the hint)
y > maxima? + i&i,..., vjx + up}.
Moreover, x is feasible in P I , and hence
y > max{i;7ic + w i , . . . ,v^x + up}
> max{v[a;* + ui,...,
vjx* + up}
= y*.
Hence, [x* T ,i/*] T is optimal in the linear programming problem.
To prove the converse, suppose that x* is not optimal in P I . Then, there
is some x' that is feasible in P I such that
y' = m a x ^ ^ c c ' + H i , . . . , vjx' + up}
< max{v7ic* + u\,...,
vjx* -h up}
= y ·
fT
T
But [x ,y']
is evidently feasible in P2, and has objective function value
{y') that is lower than that of [x* T , 2/*]T. Hence, [x* T , 2/*]T is not optimal
inP2.
b . Define
A
vj
0
-1
b 1
-ui
-uP\
Then the equivalent problem can be written as
minimize
subject to
c z
Az < 6.
588
MULTIOBJECTIVE OPTIMIZATION
By part a, if we obtain a solution to this linear programming problem,
then the first n components form a solution to the original minimax
problem.
|
A third method to convert a multiobjective problem to a single-objective
problem, assuming that the components of the objective vector are nonnegative, is to form a single objective function by taking the p-norm of the objective
vector:
f{x) = \\f(x)\\p.
The minimax method can be viewed as a special case of this method, with
p = oo. The weighted-sum method with uniform weights can be viewed as
this method with p = 1. To make the objective function differentiable in the
case where p is finite (so that we can apply gradient methods, for example),
we replace it by its pih power:
fix) = \\f(xwp = (h(x)y + ■■■ + (Mx)y.
A fourth method is to minimize one of the components of the objective
vector subject to constraints on the other components. For example, given / ,
we solve
minimize
subject to
f\ (x)
/2(#) < &2,
fi(x) < fc,
where 6 2 , . . . , be are given constants that reflect satisfactory values for the
objectives / 2 , . . . , fe, respectively. Of course, this approach is suitable only in
situations where these satisfactory values can be determined.
24.5
Uncertain Linear Programming Problems
In this section we show how multiobjective optimization methods can be used
to solve linear programming problems with uncertain coefficients, including
uncertain constraints and uncertain objective functions.
Uncertain Constraints
We first consider a generalization of linear programming to problems with
uncertain constraints. Our exposition is based on a discussion of fuzzy linear
programming by Wang [131, Chapter 30]. We consider the following general
UNCERTAIN LINEAR PROGRAMMING PROBLEMS
589
linear programming problem:
minimize
subject to
cTx
Ax < b
x>0.
We can represent the constraints in the form
(Ax)i <bi,
i = l,2,...,ra.
Suppose that the constraints' bounds are uncertain in the sense that they can
vary within given tolerance values and can be represented as
(Ax)i <bi + 9ti,
i = 1,2,..., ra,
where Θ G [0,1] and U > 0, i = 1,2,..., ra.
We now discuss a method to solve the problem above. First, solve the
following two linear programming problems:
cTx
(Ax)i < &i,
x>0
minimize
subject to
i = 1,2,..., ra
and
cTx
{Ax)i <bi + U,
minimize
subject to
i = 1,2,..., m
x>0.
Denote the solution to the two programs as x^ and χ(°\ respectively, and let
Z\ = cTx^
and ZQ = cTx^°\ Using these definitions, we construct a function
that characterizes the "degree of the penalty" associated with the uncertain
constraints in the linear programming problem
0
if cTx < ZQ
1
if cTx > z\.
μ0(χ)
A plot of this function is given in Figure 24.7. Note that when cTx < z 0 ,
then μο(χ) = 0, which represents minimum degree of penalty. On the other
hand, when cTx > zi, then μο(χ) = 1, and we have a maximum degree of
penalty. When ZQ < cTx < zi, the degree of penalty varies from 0 to 1.
Next, we introduce a function that describes the degree of penalty for
violating the iih constraint:
0
if (Ax)i
-bi<0
1
if (Ax)i
-bi>U.
ßi{x)
590
MULTIOBJECTIVE OPTIMIZATION
Μχ)
Figure 24.7
Plot of the function μο(χ).
Mi(x)
(Ax),.
Figure 24.8
Plot of the function μ%{χ).
A plot of this function is shown in Figure 24.8.
Using the definitions above we can reformulate the original linear programming problem as a multiobjective optimization problem, with the goal
of minimizing the functions that penalize constraint violations:
minimize
μ0(χ)
μι(χ)
_μτη(χ)
subject to
x > 0.
We can employ the minimax method to solve the multiobjective optimization problem as a single-objective problem
minimize
subject to
max {μο {x) > Mi (x)? · · · ? Mm (x)}
x > 0.
UNCERTAIN LINEAR PROGRAMMING PROBLEMS
591
As shown in Example 24.3, the problem above can be stated equivalent ly as
minimize Θ
subject to μο(χ) < Θ
μι{χ) < Θ, i = 1,2,... , ra
<9e [0,1], x>o.
Using now the definitions of μο and μ^, i = 1 , . . . , ra, we restate the optimization problem above as
minimize Θ
subject to
cTx < ZQ + Q{z\ — ZQ)
(Ax)i <bi + 6ti,
θβ [0,1], x>0.
i = 1,2,..., ra
E x a m p l e 24.4 Consider the following linear programming problem:
. . .
minimize
subject to
1
— -X\ — X2
x\ + #2 < 5
X2 < 3
X\ > 0, #2 > 0,
where the tolerances are t\ = 2 and £2 = 1·
a. Solve the two linear programming problems to obtain x^
the data above. Then find z\ and z$.
and x^
using
b. Construct the equivalent optimization problem (involving Θ) using the
data above.
c. Express the optimization problem as a linear programming problem in
standard form.
Solution:
a. We can solve these problems graphically to obtain
x ^ = [2,3] T
and *<°> = [3,4] T .
Hence,
ζχ = cTx{1)
= -4
and z0 = c T x ( 0 ) = - 5 ^ .
592
MULTIOBJECTIVE OPTIMIZATION
b. T h e optimization problem has the form
minimize
Θ
subject to
<Θ
μο(χ)
μι(χ)<θ
μ2(χ)
< θ
θβ [0,1], x > 0 ,
where
(0
ßo(x) = I
if — \χ\
if — 5 | < -\χλ-χ2
1
if —\x\
0
μι(χ)
0
χ
< -4
— Χ2 > —4,
if χι + #2 — 5 < 0
3?ι+Χ2~5
2
if 0 < ζ ι + χ2 - 5 < 2
1
μ2(χ)
— χ2 < — 5 |
-^χι-χ2+5|
3/2
if #1 + £ 2 — 5 > 2,
if x2 - 3 < 0
2
-3
if 0 < χ2 - 3 < 1
1
if x
2
- 3 > 1.
c. We have
minimize
Θ
subject to
cTx
{Ax)i
< zo + B{z\ — ZQ)
< h + (1 - 6)U,
i = l,2
0 e [0,1], χ > ο .
Using our data, we obtain
minimize
subject to
Θ
1
-x\
+ x2 >5-
χι+χ2<5
χ2<3
θβ
1 3
— -Θ
+ 2θ
+θ
[0,1],
x>0.
UNCERTAIN LINEAR PROGRAMMING PROBLEMS
593
Write xs = Θ. Then, the above problem can be represented as
minimize X3
subject to
x\ + 2x2 + 3x3 > 11
X\ + X2 — 2X3 < 5
%2 - #3 < 3
*3<1
i = l,2,3.
Xi>0,
The above linear program expressed in the form of a linear programming
problem in standard form is
minimize X3
subject to x\ + 2x2 + 3x3 — X4 = 11
X\+
X2 — 2X3 + X5 = 5
#2 — #3 + #6 = 3
X3 + #7 = 1
Xi>0,
i = 1,2,... 7.
Uncertain Objective Function Coefficients
We now consider a linear programming problem with uncertain objective function coefficients. We assume that uncertainties of the objective coefficients are
modeled by the following triangular function:
/
1 \
μ { χ ; α Μ
=
0
(x — a)/(b ~ a)
<(ο-χ)/(ο-ο)
0
J
if x < a
if a < x <b
if
b<x<c
if x > c.
A plot of this function for a = 1, b = 2, and c = 6 is shown in Figure 24.9.
In other words, the uncertain objective coefficients will be represented by the
triangular functions of the form given above. Following Wang [131, p. 386],
we use the notation c* = (c~\c®,cl) to denote the uncertain coefficient c*
represented by the triangular function μ (χ; c^~, c?, c+). Then the linear programming problem
minimize
subject to
cTx
Ax < b
x > 0,
594
MULTIOBJECTIVE OPTIMIZATION
becomes
C
X
minimize
c°x
subject to
Ax < b
x > 0,
where
k
[*
This is a multiobjective optimization problem. Wang [131] suggests that instead of minimizing the three values c~x, c°x, and c+x simultaneously, the
center, c°£C, be minimized; the left leg, (c° — c~) x, be maximized; and the
right leg, ( c + — c°) sc, be minimized. This results in pushing the triangular
functions to the left in the minimization process. Thus, the multiobjective
optimization problem above can be changed to the following multiobjective
optimization problem:
minimize
~-(c°-c-)x~
c°x
(c+ - c°) x
subject to Ax<b
X >0.
Uncertain Constraint Coefficients
We may be faced with solving a linear programming problem with uncertain
constraint coefficients. In this case the coefficients of the constraint matrix A
would be represented by triangular functions of the form given in the preceding section. That is, the coefficient α^ of the constraint matrix A would be
modeled by the function α^ = μ (χ;α^, α^, α+). Then, the linear programming problem with uncertain constraint coefficients would take the form
minimize
cTx
A x
subject to
A°x
A+x
~bi
< b
b\
x>0,
where A~ - [ a r.], A0 = [a&], and A+ = [a+].
UNCERTAIN LINEAR PROGRAMMING PROBLEMS
Figure 24.9
595
Plot of the triangular function μ(χ; α, 6, c) for a = 1, b = 2, and c = 6.
General Uncertainties
Finally, we may be faced with solving an uncertain linear programming problem that is a combination of the basic uncertain linear programming problems
discussed above. For example, suppose that we are asked to solve the following
quite general uncertain linear programming problem:
minimize
subject to
c x
Ax < b
x > 0,
where the tilde symbols refer to the uncertain data; that is, we have
c=(c-,c°,c+),
A=
( A ~ , A ° , A + ) , b= ( 6 " , 6 ° , & + ) .
We can represent the uncertain linear programming problem above as a multiobjective optimization problem of the form
minimize
ubject to
-(cP — c )x
c°x
1
(c+ - c°) x \
A~x
A°x
A+x
x >0.
<
V]
6°
_6+j
596
MULTIOBJECTIVE OPTIMIZATION
EXERCISES
24.1 Write a MATLAB program that implements the algorithm for generating a Pareto front, and test it on the problem in Example 24.1.
24.2 Consider the multiobjective problem
minimize
subject to
f(x)
x £ Ω,
where / : R n -> R€.
a. Suppose that we solve the single-objective problem
minimize
subject to
cTf(x)
x £ Ω,
where c € R n , c > 0 (i.e., we use the weighted-sum approach). Show
that if x* is a global minimizer for the single-objective problem above,
then x* is a Pareto minimizer for the given multiobjective problem. Then
show that it is not necessarily the case that if x* is a Pareto minimizer
for the multiobjective problem, then there exists a c > 0 such that x* is
a global minimizer for the single-objective (weighted-sum) problem.
b . Assuming that for all x £ Ω, f(x)
objective problem
minimize
subject to
> 0, suppose that we solve the single-
(fi(x))p
x £ Ω,
+ ···+
(Mx))p
where p £ R, p > 0 (i.e., we use the minimum-norm approach). Show
that if x* is a global minimizer for the single-objective problem above,
then x* is a Pareto minimizer for the given multiobjective problem. Then
show that it is not necessarily the case that if x* is a Pareto minimizer
for the multiobjective problem, then there exists a p > 0 such that x* is
a global minimizer for the single-objective (minimum-norm) problem.
c. Suppose that we solve the single-objective problem
minimize
subject to
max{/i(x),...,
x £Ω
fi(x)}
(i.e., we use the minimax approach). Show that it is not necessarily the
case that if x* is a Pareto minimizer for the given multiobjective problem,
then x* is a global minimizer for the single-objective (minimax) problem.
Then show that it also is not necessarily the case that if x* is a global
EXERCISES
597
minimizer for the single-objective problem, then x* is a Pareto minimizer
for the multiobjective problem.
24.3 Let / : R n —► R^ be given. Consider the following multiobjective
problem with equality constraints:
minimize
subject to
f(x)
x G Ω.
Suppose that / G C1, all the components of / are convex, and Ω is convex.
Suppose that there exists x* and c* > 0 such that for any feasible direction
d at x*, we have
c*TDf(x*)d > 0.
Show that £C* is a Pareto minimizer.
24.4 Let / : R n -+ R£ and h : R n -► R m be given. Consider the following
multiobjective problem with equality constraints:
minimize
subject to
f{x)
h(x) = 0.
Suppose that / , h G C1, all the components of / are convex, and the constraint
set is convex. Show that if there exists #*, c* > 0, and λ* such that
c*TDf(x*)
+ λ* τ £>/ι(χ*) = 0 T
h(x*) = 0,
then x* is a Pareto minimizer. We can think of the above as a Lagrange
condition for the constrained multiobjective function.
24.5 Let / : R n -+ Re and g : R n -> Rp be given. Consider the following
multiobjective problem with inequality constraints:
minimize
subject to
f{x)
g(x) < 0.
Suppose that / , g G C1, all the components of / are convex, and the constraint
set is convex. Show that if there exists #*, c* > 0, and μ* such that
μ*>0,
T
T
c* D/(x*) + ß* Dg(x*)
- 0T,
μ* τ 0(**) = 0,
g(x*) < o,
598
MULTIOBJECTIVE OPTIMIZATION
then x* is a Pareto minimizer. We can think of the above as a KKT condition
for the constrained multiobjective function.
24.6 Let / : R n -* R', h : R n -> R m , and g : R n -> W be given. Consider
the general constrained multiobjective problem
minimize
subject to
f(x)
h(x) = 0
g(x) < 0.
Suppose that / , #, h e C1, all the components of / are convex, and the constraint set is convex. Show that if there exists x*, c* > 0, λ*, and μ* such
that
μ*>0,
T
c* Df(x*)
T
τ
+ A* DÄi(x*) + μ* £>0(χ*) = 0 T ,
μ*τ<?(χ*) - 0,
h(x*) = 0,
<7(**) < 0,
then x* is a Pareto minimizer.
24.7 Let / i : R n -> R and f2 : R n -> R, / i , / 2 € C1. Consider the minimax
problem
minimize max{/i(x),/2(x)}.
Show that if x* is a local minimizer, then there exist μ{,μ2 £ R such that
μϊ,^2 > 0,
μ ί ν / ι ( χ * ) + μ^ν/ 2 (χ*) = 0,
μί + μ*2 = 1,
and μ* = 0 if /<(«*) < max{/i(x*), / 2 ( * * ) } .
ffini: Consider the following problem: minimizes subject to 2; > fi(x),
1,2.
i =
REFERENCES
1. J. S. Arora, Introduction to Optimum Design. New York: McGraw-Hill Book
Co., 1989.
2. R. G. Bartle, The Elements of Real Analysis, 2nd ed. New York: Wiley, 1976.
3. M. S. Bazaraa, H. D. Sherali, and C. M. Shetty, Nonlinear Programming:
Theory and Algorithms, 2nd ed. New York: Wiley, 1993.
4. A. Bhaya and E. Kaszkurewicz, Control Perspectives on Numerical Algorithms
and Matrix Problems. Philadelphia: Society for Industrial and Applied Mathematics, 2006.
5. B. Beliczynski, A. Dzielinski, M. Iwanowski, and B. Ribeiro, Eds., Adaptive
and Natural Computing Algorithms, vol. 4431 of Lecture Notes in Computer
Science. Berlin: Springer, 2007.
6. A. Ben-Israel and T. N. E. Greville, Generalized Inverses: Theory and Applications. New York: Wiley-Interscience, 1974.
7. L. D. Berkovitz, Convexity and Optimization in E n . Hoboken, NJ: Wiley,
2002.
8. C. C. Berresford, A. M. Rockett, and J. C. Stevenson, "Khachiyan's algorithm,
Part 1: A new solution to linear programming problems," Byte, vol. 5, no. 8,
pp. 198-208, Aug. 1980.
9. C. C. Berresford, A. M. Rockett, and J. C. Stevenson, "Khachiyan's algorithm,
Part 2: Problems with the algorithm," Byte, vol. 5, no. 9, pp. 242-255, Sept.
1980.
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
599
600
REFERENCES
10. D. P. Bertsekas, "Necessary and sufficient conditions for a penalty method to
be exact," Mathematical Programming, vol. 9, no. 1, pp. 87-99, Aug. 1975.
11. D. P. Bertsekas, Nonlinear Programming:
Scientific, 1999.
2nd ed. Belmont, MA: Athena
12. D. P. Bertsekas and J. N. Tsitsiklis, Parallel and Distributed
Numerical Methods. Belmont, MA: Athena Scientific, 1997.
Computation:
13. K. G. Binmore, Calculus. Cambridge, England: Cambridge University Press,
1986.
14. R. G. Bland, D. Goldfarb, and M. J. Todd, "The ellipsoid method: A survey,"
Operations Research, vol. 29, pp. 1039-1091, 1981.
15. V. G. Boltyanskii, Mathematical Methods of Optimal Control. New York: Holt,
Rinehart and Winston, 1971.
16. S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory. Philadelphia, PA: SIAM, 1994.
17. R. P. Brent, Algorithms for Minimization
Cliffs, NJ: Prentice Hall, 1973.
without Derivatives.
Englewood
18. L. Brickman, Mathematical Introduction to Linear Programming and Game
Theory. New York: Springer-Verlag, 1989.
19. C. G. Broyden, "Quasi-Newton methods," in Optimization Methods in Electronics and Communications (K. W. Cattermole and J. J. O'Reilly, Eds.),
vol. 1 of Mathematical Topics in Telecommunications. New York: Wiley, 1984,
pp. 105-110,
20. A. E. Bryson and Y.-C. Ho, Applied Optimal Control: Optimization, Estimation, and Control, rev. print. Washington, DC: Hemisphere Publishing
Corporation, 1975.
21. B. D. Bunday, Basic Optimization Methods. London: Edward Arnold, 1984.
22. J. Campbell, The Improbable Machine. New York: Simon and Schuster, 1989.
23. S. L. Campbell and C. D. Meyer, Jr., Generalized Inverses of Linear Transformations. New York: Dover Publications, 1991.
24. E. K. P. Chong and B. E. Brewington, "Distributed communications resource management for tracking and surveillance networks," in Proceedings of
the Conference on Signal and Data Processing of Small Targets 2005 (SPIE
Vol. 5913), part of the SPIE Symposium on Optics & Photonics, San Diego,
California, July 31-Aug. 4, 2005, pp. 280-291.
25. E. K. P. Chong and B. E. Brewington, "Decentralized rate control for tracking
and surveillance networks," Ad Hoc Networks, special issue on Recent Advances
in Wireless Sensor Networks, vol. 5, no. 6, pp. 910-928, Aug. 2007.
26. E. K. P. Chong, S. Hui, and S. H. Zak, "An analysis of a class of neural
networks for solving linear programming problems," IEEE Transactions on
Automatic Control, special section on Neural Networks in Control, Identification, and Decision Making, vol. 44, no. 11, pp. 1995-2006, Nov. 1999.
27. E. K. P. Chong and S. H. Zak, "Single-dimensional search methods," in Wiley
Encyclopedia of Operations Research and Management Science, 2011, ISBN:
978-0-470-40063-0.
REFERENCES
601
28. A. Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal
Processing. Chichester, England: Wiley, 1993.
29. M. Clerc, "The swarm and the queen: Towards a deterministic and adaptive
particle swarm optimization," in Proceedings of the Congress of Evolutionary
Computation, Washington, DC, July 1999, pp. 1951-1957.
30. M. Clerc and J. Kennedy, "The particle swarm: Explosion, stability and convergence in a multidimensional complex space," IEEE Transactions on Evolutionary Computation, vol. 6, pp. 58-73, Feb. 2002.
31. C. A. Coello Coello, D. A. Van Veldhuizen, and G. B. Lamont, Evolutionary
Algorithms for Solving Multi-Objective Problems. New York: Kluwer Academic/Plenum Publishers, 2002.
32. S. D. Conte and C. de Boor, Elementary Numerical Analysis: An
Approach, 3rd ed. New York: McGraw-Hill Book Co., 1980.
Algorithmic
33. M. A. Dahleh and I. J. Diaz-Bobillo, Control of Uncertain Systems: A Linear
Programming Approach. Upper Saddle River, NJ: Prentice Hall, 1995.
34. G. B. Dantzig, Linear Programming and Extensions. Princeton, NJ: Princeton
University Press, 1963.
35. G. B. Dantzig and M. N. Thapa, Linear Programming, vol. 1,
New York: Springer-Verlag, 1997.
Introduction.
36. L. Davis, Ed., Genetic Algorithms and Simulated Annealing, Research Notes
in Artificial Intelligence. London: Pitman, 1987.
37. K. Deb, Multi-objective Optimization
ester, England: Wiley, 2001.
Using Evolutionary Algorithms.
38. V. F. Dem'yanov and L. V. Vasil'ev, Nondifferentiable Optimization.
York: Optimization Software, Inc., Publications Division, 1985.
ChichNew
39. J. E. Dennis, Jr. and R. B. Schnabel, Numerical Methods for Unconstrained
Optimization and Nonlinear Equations. Englewood Cliffs, NJ: Prentice Hall,
1983.
40. J. Dongarra and F. Sullivan, "The top 10 algorithms," Computing in Science
and Engineering, pp. 22-23, Jan./Feb. 2000.
41. V. N. Faddeeva, Computational Methods of Linear Algebra. New York: Dover
Publications, 1959.
42. S.-C. Fang and S. Puthenpura, Linear Optimization and Extensions:
and Algorithms. Englewood Cliffs, NJ: Prentice Hall, 1993.
Theory
43. R. Fletcher, Practical Methods of Optimization, 2nd ed. Chichester, England:
Wiley, 1987.
44. F. R. Gantmacher, The Theory of Matrices, vol. 1. New York: Chelsea Publishing Co., 1959.
45. F. R. Gantmacher, The Theory of Matrices, 2nd ed. Moscow: Nauka, revised
1966. In Russian.
46. S. I. Gass, An Illustrated Guide to Linear Programming. New York: McGrawHill Book Co., 1970.
602
REFERENCES
47. I. M. Gel'fand, Lectures on Linear Algebra. New York: Interscience Publishers,
1961.
48. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distribution, and the
Bayesian restoration of images," IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 6, pp. 721-741, 1984.
49. P. E. Gill and W. Murray, "Safeguarded steplength algorithms for optimization
using descent methods," Tech. Rep. NPL NAC 37, National Physical Laboratory, Division of Numerical Analysis and Computing, Teddington, England,
Aug. 1974.
50. P. E. Gill, W. Murray, M. A. Saunders, and M. H. Wright, "Two step-length
algorithms for numerical optimization," Tech. Rep. SOL 79-25, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, CA, Dec. 1979.
51. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization.
Academic Press, 1981.
London:
52. P. E. Gill, W. Murray, and M. H. Wright, Numerical Linear Algebra and
Optimization. Redwood City, CA: Addison-Wesley, 1991.
53. G. H. Golub and C. F. Van Loan, Matrix Computations,
MD: The Johns Hopkins University Press, 1983.
3rd ed.. Baltimore,
54. R. E. Gomory, "Outline of an algorithm for integer solutions to linear programs," Bulletin of the American Mathematical Society, vol. 64, no. 5, pp. 275278, Sep. 1958.
55. C. C. Gonzaga, "Path-following methods for linear programming," SI AM Review, vol. 34, no. 2, pp. 167-224, June 1992.
56. B. Hajek, "Cooling schedules for optimal annealing," Mathematics of Operations Research, vol. 13, no. 2, pp. 311-329, 1988.
57. J. Hannig, E. K. P. Chong, and S. R. Kulkarni, "Relative frequencies of generalized simulated annealing," Mathematics of Operations Research, vol. 31,
no. 1, pp. 199-216, Feb. 2006.
58. R. L. Harvey, Neural Network Principles. Englewood Cliffs, NJ: Prentice Hall,
1994.
59. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Upper
Saddle River, NJ: Prentice Hall, 1999.
60. J. Hertz, A. Krogh, and R. G. Palmer, Introduction to the Theory of Neural
Computation, vol. 1 of Santa Fe Institute Studies in the Sciences of Complexity. Redwood City, CA: Addison-Wesley, 1991.
61. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence.
Cambridge, MA: MIT Press, 1992.
62. R. A. Horn and C. R. Johnson, Matrix Analysis. Cambridge, England: Cambridge University Press, 1985.
63. A. S. Householder, The Theory of Matrices in Numerical Analysis. New York:
Dover Publications, 1975.
REFERENCES
603
64. S. Hui and S. H. Zak, "The Widrow-Hoff algorithm for McCulloch-Pitts type
neurons," IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 924-929,
Nov. 1994.
65. D. R. Hush and B. G. Home, "Progress in supervised neural networks: What's
new since Lippmann," IEEE Signal Processing Magazine, pp. 8-39, Jan. 1993.
66. S. Isaak and M. N. Manougian, Basic Concepts of Linear Algebra. New York:
W. W. Norton & Co., 1976.
67. J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper
Saddle River, NJ: Prentice Hall, 1997.
68. W. E. Jenner, Rudiments of Algebraic Geometry. New York: Oxford University Press, 1963.
69. E. M. Johansson, F. U. Dowla, and D. M. Goodman, "Backpropagation learning for multi-layer feed-forward neural networks using the conjugate gradient
method," International Journal of Neural Systems, vol. 2, no. 4, pp. 291-301,
1992.
70. S. Kaczmarz, "Approximate solution of systems of linear equations," International Journal of Control, vol. 57, no. 6, pp. 1269-1271, 1993. A reprint of the
original paper: S. Kaczmarz, "Angenäherte Auflösung von Systemen linearer
Gleichunger," Bulletin International de VAcademie Polonaise des Sciences et
des Lettres, Serie A, pp. 355-357, 1937.
71. N. Karmarkar, "A new polynomial-time algorithm for linear programming,"
Combinatorica, vol. 4, no. 4, pp. 373-395, 1984.
72. M. F. Kelly, P. A. Parker, and R. N. Scott, "The application of neural networks
to myoelectric signal analysis: A preliminary study," IEEE Transactions on
Biomedical Engineering, vol. 37, no. 3, pp. 221-230, Mar. 1990.
73. J. Kennedy and R. C. Eberhart, with Y. Shi, Swarm Intelligence. San Francisco: Morgan Kaufmann, 2001.
74. L. G. Khachiyan, "A polynomial algorithm in linear programming," Soviet
Mathematics Doklady, vol. 20, no. 1, pp. 191-194, 1979.
75. S. Kirkpatrick, C. D. Gelatt, Jr., and M. P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, 1983.
76. V. Klee and G. J. Minty, "How good is the simplex algorithm?" in InequalitiesIll (O. Shisha, Ed.), New York: Academic Press, 1972, pp. 159-175.
77. D. E. Knuth, The Art of Computer Programming, vol. 1, Fundamental Algorithms, 2nd ed. Reading, MA: Addison-Wesley, 1973.
78. L. Kolev, "Iterative algorithm for the minimum fuel and minimum amplitude
problems for linear discrete systems," International Journal of Control, vol. 21,
no. 5, pp. 779-784, 1975.
79. J. R. Koza, Genetic Programming: On the Programming of Computers by
Means of Natural Selection. Cambridge, MA: MIT Press, 1992.
80. T. Kozek, T. Roska, and L. O. Chua, "Genetic algorithm for CNN template
learning," IEEE Transactions on Circuits and Systems, I: Fundamental Theory and Applications, vol. 40, no. 6, pp. 392-402, June 1993.
604
REFERENCES
81. K. Kuratowski, Introduction to Calculus, 2nd ed., vol. 17 of International
Series of Monographs in Pure and Applied Mathematics. Warsaw, Poland:
Pergamon Press, 1969.
82. J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, "Convergence
properties of the Neider-Mead simplex method in low dimensions," SI AM
Journal on Optimization, vol. 9, no. 1, pp. 112-147, 1998.
83. S. Lang, Calculus of Several Variables, 3rd ed. New York: Springer-Verlag,
1987.
84. J. M. Layton, Multivariable Control Theory. Stevenage, England: Peter Peregrinus on behalf of the Institution of Electrical Engineers, 1976.
85. E. B. Lee and L. Markus, Foundations of Optimal Control Theory. Malabar,
FL: Robert E. Krieger Publishing Company, 1986.
86. G. Leitmann, The Calculus of Variations and Optimal Control: An Introduction. New York: Plenum Press, 1981.
87. D. G. Luenberger, Optimization by Vector Space Methods. New York: Wiley,
1969.
88. D. G. Luenberger and Y. Ye, Linear and Nonlinear Programming, 3rd ed. New
York, NY: Springer Science + Business Media, 2008.
89. I. J. Maddox, Elements of Functional Analysis, 2nd ed. Cambridge, England:
Cambridge University Press, 1988.
90. O. L. Mangasarian, Nonlinear Programming. New York: McGraw-Hill Book
Co., 1969.
91. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, H. Teller, and E. Teller,
"Equation of state calculations by fast computing machines," Journal of
Chemical Physics, vol. 21, no. 6, pp. 1087-1092, 1953.
92. K. M. Miettinen, Nonlinear Multiobjective Optimization. Norwell, MA: Kluwer
Academic Publishers, 1998.
93. S. A. Miller and E. K. P. Chong, "Flow-rate control for managing communications in tracking and surveillance networks," in Proceedings of the Conference
on Signal and Data Processing of Small Targets 2007 (SPIE Vol. 6699), part
of the SPIE Symposium on Optics & Photonics, San Diego, California, Aug.
26-30, 2007.
94. M. Mitchell, An Introduction to Genetic Algorithms.
Press, 1996.
Cambridge, MA: MIT
95. A. Mostowski and M. Stark, Elements of Higher Algebra. Warsaw, Poland:
PWN—Polish Scientific Publishers, 1958.
96. S. G. Nash and A. Sofer, Linear and Nonlinear Programming.
McGraw-Hill Book Co., 1996.
New York:
97. J. A. Neider and R. Mead, "A simplex method for function minimization,"
Computer Journal, vol. 7, no. 4, pp. 308-313, 1965.
98. A. Osyczka, Evolutionary Algorithms for Single and Multicriteria Design Optimization. Heidelberg, Germany: Physica-Verlag, 2002.
REFERENCES
605
99. D. H. Owens, Multivariable and Optimal Systems. London: Academic Press,
1981.
100. T. M. Ozan, Applied Mathematical Programming for Production and Engineering Management. Englewood Cliffs, NJ: Prentice Hall, 1986.
101. C. H. Papadimitriou and K. Steiglitz, Combinatorial Optimization:
rithms and Complexity. Englewood Cliffs, NJ: Prentice Hall, 1982.
102. P. C. Parks, "S. Kaczmarz (1895-1939)," International
vol. 57, no. 6, pp. 1263-1267, 1993.
Algo-
Journal of Control,
103. R. J. Patton and G. P. Liu, "Robust control design via eigenstructure assignment, genetic algorithms and gradient-based optimisation," IEE Proceedings
on Control Theory and Applications, vol. 141, no. 3, pp. 202-208, May 1994.
104. A. L. Peressini, F. E. Sullivan, and J. J. Uhl, Jr., The Mathematics of Nonlinear Programming. New York: Springer-Verlag, 1988.
105. A. Pezeshki, L. L. Scharf, M. Lundberg, and E. K. P. Chong, "Constrained
quadratic minimizations for signal processing and communications," in Proceedings of the Joint 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC05), Seville, Spain, Dec. 12-15, 2005,
pp. 7949-7953.
106. A. Pezeshki, L. L. Scharf, and E. K. P. Chong, "The geometry of linearly
and quadratically constrained optimization problems for signal processing and
communications," Journal of the Franklin Institute, special issue on Modelling
and Simulation in Advanced Communications, vol. 347, no. 5, pp. 818-835,
June 2010.
107. M. J. D. Powell, "Convergence properties of algorithms for nonlinear optimization," SIAM Review, vol. 28, no. 4, pp. 487-500, Dec. 1986.
108. S. S. Rangwala and D. A. Dornfeld, "Learning and optimization of machining
operations using computing abilities of neural networks," IEEE Transactions
on Systems, Man and Cybernetics, vol. 19, no. 2, pp. 299-314, Mar./Apr.
1989.
109. G. V. Reklaitis, A. Ravindran, and K. M. Ragsdell, Engineering Optimization:
Methods and Applications. New York: Wiley-Interscience, 1983.
110. A. M. Rockett and J. C. Stevenson, "Karmarkar's algorithm: A method for
solving large linear programming problems," Byte, vol. 12, no. 10, pp. 146-160,
Sept. 1987.
111. H. L. Royden, Real Analysis, 3rd ed. New York: Macmillan Company, 1988.
112. W. Rudin, Principles of Mathematical Analysis, 3rd ed. New York: McGrawHill Book Co., 1976.
113. D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Parallel
Distributed Processing: Explorations in the Micro structure of Cognition, vol. 1,
Foundations. Cambridge, MA: MIT Press, 1986.
114. D. Russell, Optimization
Theory. New York: W. A. Benjamin, 1970.
115. S. L. Salas and E. Hille, Calculus: One and Several Variables, 4th ed. New
York: Wiley, 1982.
606
REFERENCES
116. L. L. Scharf, L. T. McWhorter, E. K. P. Chong, J. S. Goldstein, and M.
D. Zoltowski, "Algebraic equivalence of conjugate direction and multistage
Wiener filters," in Proceedings of the Eleventh Annual Workshop on Adaptive Sensor Array Processing (ASAP), Lexington, Massachusetts, Mar. 11-13,
2003.
117. L. L. Scharf, E. K. P. Chong, and Z. Zhang, "Algebraic equivalence of matrix conjugate direction and matrix multistage filters for estimating random
vectors," in Proceedings of the 43rd IEEE Conference on Decision and Control (CDC'04), Atlantis Resort, Paradise Island, Bahamas, Dec. 14-17, 2004,
pp. 4175-4179.
118. L. L. Scharf, E. K. P. Chong, M. D. Zoltowski, J. S. Goldstein, and I. S.
Reed, "Subspace expansion and the equivalence of conjugate direction and
multistage Wiener filters," IEEE Transactions on Signal Processing, vol. 56,
no. 10, pp. 5013-5019, Oct. 2008.
119. A. Schrijver, Theory of Linear and Integer Programming. New York: Wiley,
1986.
120. R. T. Seeley, Calculus of Several Variables: An Introduction.
Scott, Foresman and Co., 1970.
Glenview, IL:
121. J. R. Silvester, "Determinants of block matrices," The Mathematical
vol. 48, no. 51, pp. 460-467, Nov. 2000.
Gazette,
122. W. Spendley, G. R. Hext, and F. R. Himsworth, "Sequential application of
simplex designs in optimization and evolutionary operation," Technometrics,
vol. 4, pp. 441-461, 1962.
123. W. A. Spivey, Linear Programming: An Introduction.
Company, 1963.
New York: Macmillan
124. R. E. Stone and C. A. Tovey, "The simplex and projective scaling algorithms
as iteratively reweighted least squares methods," SIAM Review, vol. 33, no. 2,
pp. 220-237, June 1991.
125. G. Strang, Introduction to Applied Mathematics.
Cambridge Press, 1986.
126. G. Strang, Linear Algebra and Its Applications.
1980.
Wellesley, MA: WellesleyNew York: Academic Press,
127. T. W. Then and E. K. P. Chong, "Genetic algorithms in noisy environments,"
in Proceedings of the 9th IEEE Symposium on Intelligent Control, pp. 225-230,
Aug. 1994.
128. L. Vandenberghe and S. Boyd, "Semidefinite programming," SIAM
vol. 38, no. 1, pp. 49-95, Mar. 1996.
129. P. P. Varaiya, Notes on Optimization.
Co., 1972.
Review,
New York: Van Nostrand Reinhold
130. D. J. Velleman, How To Prove It: A Structured Approach. Cambridge, England: Cambridge University Press, 1994.
131. L.-X. Wang, A Course in Fuzzy Systems and Control. Upper Saddle River,
NJ: Prentice Hall, 1999.
REFERENCES
607
132. B. Widrow and M. A. Lehr, "30 years of adaptive neural networks: Perceptron,
madaline, and backpropagation," Proceedings of the IEEE, vol. 78, no. 9,
pp. 1415-1442, Sept. 1990.
133. D. J. Wilde, Optimum Seeking Methods. Englewood Cliffs, NJ: Prentice Hall,
1964.
134. R. E. Williamson and H. F. Trotter, Multivariable Mathematics, 2nd ed. Englewood Cliffs, NJ: Prentice Hall, 1979.
135. W. I. Zangwill, Nonlinear Programming:
Cliffs, NJ: Prentice Hall, 1969.
A Unified Approach.
Englewood
136. G. Zoutendijk, Mathematical Programming Methods. Amsterdam, The Netherlands: North-Holland, 1976.
137. J. M. Zurada, Introduction to Artificial Neural Systems. St. Paul, MN: West
Publishing Co., 1992.
Index
Absolute value, 19
Absolute value penalty function, 561
Activation function, 254
Active constraint, 483
Adaline, 257, 271
Adaptive linear element, 257
Additivity, 19
Affine function, 62, 63, 247, 524
Affine matrix inequality, 524
Affine scaling, 406, 407
Affine scaling method, 406-411
artificial problem, 410
stopping criterion, 409
strictly interior feasible point, 406
Algebraic Riccati inequality, 544
Algorithm
affine scaling, 406-411
backpropagation, 253, 258-269
BFGS, 207-211
Broyden-Fletcher-GoldfarbShanno, see BFGS algorithm
complexity of, 306, 402
conjugate gradient, see Conjugate
gradient algorithm
convergence of, see Convergence
Davidon-Fletcher-Powell, see D F P
algorithm
DFP, 202-207
ellipsoid, see Khachiyan's method
exponential complexity, 402
fixed step size, 146, 151, 261
for constrained optimization, 545567
genetic, 285-298
globally monotone, 157
gradient, 131-153
Gram-Schmidt, 177, 189
interior-point, 307, 403, 406, 411,
423
iterative, 124, 159. See also Search
methods
Kaczmarz's, 232-236, 257
Karmarkar's,
see
Karmarkar's
method
Khachiyan's, 306, 402-405, 418
Lagrangian, 553-560
naive random search, 279
Nelder-Mead, 274-278
particle swarm optimization, 282285
polynomial complexity, 402
An Introduction to Optimization, Fourth Edition.
By E. K. P. Chong and S. H. Zak. Copyright © 2013 John Wiley & Sons, Inc.
609
610
INDEX
probabilistic search, 278
projected, 546, 556
projected gradient, 407, 547, 549553
projected steepest descent, 551
quasi-Newton, see Quasi-Newton
methods
randomized search, 278
rank one, 197-202
rank two, 202
RLS, 227-232, 250
secant method, 120-122, 172
simplex, see Simplex method
simulated annealing, 278-282
single-rank symmetric, 197
SRS, 197
steepest descent, 133-141
symmetric Huang family, 215
variable metric, 202
Widrow-Hoff, 257, 271
zero finding, 118, 155
Allocation, 541
Alphabet in genetic algorithm, 286
Argmin, 82
Armijo backtracking algorithm, 126
Armijo condition, 125
Armijo-Goldstein condition, 125
Artificial neural networks, see Feedforward neural networks
Artificial problem
in affine scaling method, 410
in Karmarkar's method, 418
in simplex method, 361
Associative, 8, 9
Asymmetric duality, 379
Augmented matrix, 325
Backpropagation algorithm, 253, 258269
as a gradient algorithm, 261
forward pass, 263
reverse pass, 263
Ball, 50
Banana (Rosenbrock's) function, 68, 159,
173, 191, 215
Basic columns, 325
Basic feasible solution, 305, 325, 327,
331, 349
Basic solutions, 324-327
Basic variables, 325, 347
Basis
definition of, 11
entering, 351
in linear equations, 325, 347, 348
leaving, 351
natural, 11
orthogonal, 29
Beltrami, 562
Best-so-far, 280, 283, 290
BFGS algorithm, 207-211
Big-oh notation, 74, 149
Bisection method, 116
Bland's rule, 360, 375
Boltzmann, 281
Bolzano-Weierstrass theorem, 58, 234
Boundary, 51
Boundary point, 51
Bounded above, 57
Bounded below, 57
Bounded sequence, 56, 57
Bounded set, 51
Box constraint, 546
Bracketing, 123, 129
Brent's method, 122
Broyden, 207
Broyden-Fletcher-Goldfarb-Shanno algorithm, see BFGS algorithm
Canonical augmented matrix, 346-348
Canonical form, 346
Canonical representation, 347
Canonical representation of LMI, 529
Canonical tableau, 358
Carrier of polyhedron, 53
Cauchy-Schwarz inequality, 20, 132, 207,
551
Center of gravity, 102, 275
Centroid, 102, 274
Chain rule, 67
Characteristic equation, 26
Characteristic polynomial, 26
Chromosome in genetic algorithm, 286
Circuit, 121, 253, 254, 311, 487
Citation style, 6
Clairaut's theorem, 65
Closed set, 51
Column vector, 7
Combinatorial optimization, 273, 282,
297
Commutative, 8
Compact set, 51
Compatible matrix noi>m, 36
Complementarity, 207
Complementary slackness, 390, 539
Complex inner product, 21
Complex vector space, 12
Complexity of algorithm, 306, 402
exponential, 402
INDEX
polynomial, 402
Component of vector, 7
Composite function, 67, 85, 90, 459
Concave function, 512, see Convex function
Condition number, 148
Conjugate direction methods, 175-188
Conjugate gradient algorithm
Fletcher-Reeves formula, 187
Hestenes-Stiefel formula, 187
nonquadratic problems, 186-188
Polak-Ribiere formula, 187
Powell formula, 188
quadratic problems, 182-186
stopping criterion, 188
Consistent linear inequalities, 404
Constrained optimization, 82, 305, 449
Constraint
active, 483
box, 546
convex, 517
equality, 450, 504
functional, 82
inactive, 483
inequality, 483, 561
set, 82
Constraint set, 81. See also Feasible set
Continuity, 21, 60, 453, 565
Continuous function, 21, 60, 453, 565
Continuously different iable function, 65,
451, 513
Contradiction, proof, 5
Contraposition, proof, 4
Contrapositive, 4
Control system, 102, 472, 474, 475, 481,
553
Convergence
fixed-step-size gradient algorithm,
146
globally convergent, 141
gradient algorithms, 143
Kaczmarz's algorithm, 233
linear, 148
locally convergent, 141, 554, 556
Newton's method, 165
of sequence of matrices, 59
order of, 148, 149, 152, 153, 157,
165
penalty method, 565
quadratic (second-order), 148
rate of, 141, 148
ratio, 148
steepest descent algorithm, 145
sublinear, 148
611
superlinear, 148
Convergent sequence, 56
Convex combination, 48, 297, 582
Convex constraint, 517
Convex function, 508-517
definition of, 509
different iable, 513
equivalent definition of, 509
minimizers of, 517
optimization of, 517-536
quadratic, 512
strict, 512
twice differentiable, 515
Convex optimization, 517-536
Convex programming, see Convex optimization
Convex set, 48-50
definition of, 48, 330, 509
extreme point, 50, 331, 332, 335
in definition of convex function, 509
polyhedron, 52, 317
polytope, 52, 316
properties of, 49
supporting hyperplane, 52, 316
Cooling schedule, 281
Coordinates, 11
Cost function, 81
Courant-Beltrami penalty function, 562,
570
Cramer's rule, 33
Crossing site, 287
Crossover in genetic algorithm, 287
crossing site, 287
multiple-point crossover, 287, 300
one-point crossover, 287
Cubic fit, 122
Curve, 453
Cutting-plane method, 435
Cycling in simplex method, 360, 375
Dantzig, 306
Davidon, 202
Davidon-Fletcher-Powell algorithm, see
D F P algorithm
Decision variable, 81, 305, 541
Decomposition
direct sum, 30, 458
orthogonal, 30, 226
Decreasing sequence, 55
Degenerate basic feasible solution, 325,
328, 360
DeMorgan's law, 3
Derivative, 62, 83
partial, 64
612
INDEX
Derivative descent search, 154
Derivative matrix, 64
Descent property, 135, 141, 144, 167,
168, 193, 552
Determinant, 14
D F P algorithm, 202-207
Diagonal matrix, 27, 407, 421, 541, 571
Diet problem, 308, 381, 388
Differentiable curve, 453
Differentiable function, 62, 63, 453
Dimension, 11, 452
Direct sum decomposition, 30, 458
Directional derivative, 84
Discrete Fourier series, 225
Discrete-time linear system, 102, 248,
474, 481
Distributive, 9
Domination, 575
Dual linear program, 378, 403
Dual nonlinear program, 543
Dual quadratic program, 399
Duality
asymmetric, 379
dual nonlinear program, 543
dual problem, 378, 403
dual quadratic program, 399
dual vector, 378
duality theorem, 387, 543
in quasi-Newton methods, 207
Karush-Kuhn-Tucker
conditions,
395, 539
linear programming, 377, 539
nonlinear programming, 542
primal nonlinear program, 542
primal problem, 378, 403
primal quadratic program, 399
quadratic programming, 399
symmetric, 378, 403
weak duality lemma, 385, 399, 543
Duality theorem, 387, 543
Dyadic product, 197
Eberhart, Russell, 282
Edge of polyhedron, 53
Eigenvalue
definition of, 26
maximal, 144
minimal, 144
of symmetric matrix, 28, 35
Eigenvector
definition of, 26
of symmetric matrix, 28
orthogonal, 28
relation to Q-conjugacy, 189
Electric circuit, 311, 487
Elementary matrix
elementary row operation, 341
first kind, 340
second kind, 340
third kind, 341
Elementary row operation, 341
Elitism in genetic algorithm, 290
Ellipsoid, 405
Ellipsoid algorithm, see Khachiyan's
method
Encoding in genetic algorithm, 285, 290,
297
Entry of matrix, 12
Epigraph, 508
Equality constraint, 450, 504
Estimation, 121, 223, 244
Euclidean inner product, 19
Euclidean norm, 20
Evolution in genetic algorithm, 287
Exact penalty function, 566
Exclusive OR, see XOR
Expanding subspace theorem, 181
Exponential complexity, 402
Extreme point, 50, 331, 332, 334
Extremizer, 82
Face of polyhedron, 53, 316
Farkas's transposition theorem, 396
Feasibility problem, 398, 526, 529
Feasible direction, 84, 519, 566
Feasible point, 305, 450
Feasible set, 81, 450
Feedforward neural networks, 253-269
activation function, 254
Adaline, 257
backpropagation algorithm, 253,
258-269
function approximation, 255
hidden layer, 254
input layer, 254
learning, 253
neuron, 253
output layer, 254
single-neuron training, 256-258
supervised learning, 256
training, 253
training set, 255
unsupervised learning, 256
weights, 253, 254
Fibonacci method, 108-115
Fibonacci sequence, 109
First-order Lagrangian algorithm, 553
First-order necessary condition
INDEX
equality constraint
(Lagrange),
460, 461
in convex optimization, 518
inequality constraint (KKT), 484
interior case, 86
set constraint, 85
Fitness in genetic algorithm, 286
Fitting straight line, 101, 221, 227, 246,
247
Fixed point, 553
Fixed step size, 146, 151, 261
Fletcher, 187, 202, 207
Fletcher-Reeves formula, 187
Floor, 435
FONC, see First-order necessary condition
Fourier series, 225
Frobenius norm, 36
Full-rank factorization, 236
Function
affine, 62, 63, 247, 524
banana, 68, 159, 173, 191, 215
composite, 67, 85, 90, 459
concave, 512, see Convex function
continuous, 21, 60, 453, 565
continuously differentiable, 65, 451,
513
convex, 508-517
cost, 81
derivative matrix of, 64
derivative of, 62, 83
differentiable, 62, 63, 453
directional derivative of, 84
gradient of, 65, 71, 83, 131
graph of, 71, 508
Jacobian matrix of, 64
Lagrangian, 464, 468, 543, 553
linear, see Linear transformation
matrix-valued, 60
maximum rate of decrease, 132
maximum rate of increase, 71, 131
notation, 5
objective, 81
partial derivative of, 64
penalty, 560
Powell, 162
Rosenbrock's, 68, 159, 173, 191,
215
sigmoid, 266
twice continuously differentiable,
65, 515
twice differentiable, 65, 454
uniformly continuous, 23
unimodal, 104
613
utility, 542
Function approximation, 255
Functional constraint, 82
Fundamental theorem of algebra, 26
Fundamental theorem of linear algebra,
41
Fundamental theorem of LP, 328
Fuzzy linear programming, 585
Gale's transposition theorem, 397
Gauss-Newton method, 171
Generalized eigenvalue, 468, 534
Generalized inverse, 236, 238
Genetic algorithm, 285-298
alphabet, 286
analysis of, 291-297
best-so-far chromosome, 290
chromosome, 286
crossover, 287
elitism, 290
encoding, 285, 290, 297
evolution, 287
fitness, 286
initial population, 285
length of schema, 294
mating pool, 286
mutation, 288
offspring, 287
order of schema, 294
parents, 287
population size, 286
real-number, 297-298
representation scheme, 286
roulette-wheel scheme, 286
schema, 292
selection, 286
stopping criterion, 290
tournament scheme, 286
Gibbs, 281
Global minimizer, 82, 94, 473, 517-519,
521, 537, 552
Globally convergent, 141
Globally monotone algorithm, 157
Golden section, 106
Golden section search, 104-108
Goldfarb, 207
Goldstein condition, 125
Gomory cut, 435
Gomory cutting-plane method, 435
Gordan's transposition theorem, 397
Gradient, 65, 71, 83, 131
Gradient descent algorithm, see Algorithm, gradient
Gradient methods, 131-153
614
INDEX
backpropagation algorithm, 253,
258-269
constrained optimization, see Projected gradient method
convergence of, 141-147
convergence rate of, 147-153
descent property, 135, 141, 144
equality constraints,
see
Lagrangian algorithms
fixed step size, 145
inequality constraints, see Lagrangian algorithms
Lagrangian, 553-560
order of convergence, 152
projected, 547, 549-553
stopping criterion, 135
Gram matrix, 220
Gram-Schmidt, 177, 189
Grammian, 220
Graph, 71, 508
Greatest lower bound, 57
Hacijan, see Khachiyan
Hadamard product, 283
Hajek, 282
Half-space, 46, 316
negative, 46
positive, 46
Hessian, 65, 468, 515
Hessian matrix, 83
Hestenes, Magnus, 187, 188
Hestenes-Stiefel formula, 187
Hidden layer in neural network, 254
Hoff, 257
Holland, John, 285
Homogeneity, 19, 21
Huang family, 215
Hyperplane
definition of, 46
supporting, 52, 316
tangent to graph, 71
Identity matrix, 16
ILP, see Integer linear programming
Image of matrix, see Range of matrix
Implicit function theorem, 457
Impulse response, 248
Inactive constraint, 483
Inconsistent system of equations, 217
Increasing sequence, 55
Indefinite matrix, 35
Induced matrix norm, 36, 480
Induction, principle of, 5
Inequality constraint, 483, 561
Infimum, see Greatest lower bound
Inner product
complex, 21
Euclidean, 19
properties of, 19
Innovation, 229
Input layer in neural network, 254
Integer linear programming, 427-444
Integer programming, see Integer linear
programming
Interior, 51
Interior point, 51
Interior-point method, 307, 403, 406,
411, 423
Inverse
continuity of, 60
matrix, 16
Inverse Hessian, 194
Inverse parabolic interpolation, 122
Invertible matrix, see Nonsingular matrix
Iterative algorithm, 124, see Search
methods, 159
Jacobian matrix, 64
Jordan form, 59
Kaczmarz's algorithm, 232-236, 257
Kantorovich, 306
Karmarkar, 306, 403
Karmarkar's method, 306, 403, 411-423
artificial problem, 418
complexity, 403
Karmarkar's canonical form, 4 1 1 413, 415
Karmarkar's restricted problem,
414-415
projective transformation, 416, 424
simplex, 412
stopping criterion, 415, 420
strictly interior feasible point, 407,
416
Karush-Kuhn-Tucker
condition,
see
K K T condition
Karush-Kuhn-Tucker
multiplier,
see
K K T multiplier
Karush-Kuhn-Tucker theorem, 484
Kennedy, James, 282
Kernel of matrix, see Nullspace of matrix
Khachiyan, 306, 402
Khachiyan's method, 306, 402-405, 418
K K T condition, 484, 485, 489, 521, 556,
594
KKT multiplier, 484, 490
INDEX
K K T theorem, 484
Klee-Minty problem, 401
Koopmans, 306
Krylov subspace, 188
Kuhn-Tucker condition, see KKT condition
Lagrange condition, 460, 464, 520, 550,
553, 593
Lagrange multiplier, 460, 463
Lagrange's theorem, 460, 461
Lagrangian algorithms, 553-560
Lagrangian function, 464, 468, 543, 553
Lanczos, Cornelius, 188
Leading principal minor, 31
Learning in neural network, 253
Least squares, 217-227, 238
nonlinear, 169
Least upper bound, 57
Left pseudoinverse, 238
Level set, 68, 131, 134
Levenberg-Marquardt algorithm, 171
Levenberg-Marquardt modification, 168
Limit of sequence, 55
Line fitting, 101, 221, 227, 246, 247
Line search, 103, 124, 133, 167, 186, 188,
194, 209
Line segment, 45, 48
Linear combination, 10
Linear convergence, 148
Linear dynamical system, see Discretetime linear system
Linear equations
augmented matrix, 325
basic solution, 325
basis, 325, 347, 348
canonical augmented matrix, 347
canonical form, 346
degenerate basic solutions, 325
existence of solution, 17
inconsistent, 217
Kaczmarz's algorithm, 232-236
least-squares solution, 217, 218, 221
minimum-norm solution, 231, 241,
257, 473
overdetermined, 217
particular solution, 346
pivot, 349, 352, 364
solving in general, 217-243
solving using row operations, 339346
Linear function, see Linear transformation
Linear inequalities
615
consistent, 404
in linear programming, 305, 307,
316
Linear least squares, 217-227, 238
Linear matrix inequality, 524, 541
Linear programming
affine scaling method, 406-411
artificial problem in affine scaling
method, 410
artificial problem in Karmarkar's
method, 418
artificial
problem
in
simplex
method, 361
artificial variables in simplex
method, 361
as constrained problem, 450
asymmetric duality, 379
basic columns, 325
basic feasible solution, 305, 325,
327, 331, 349
basic solutions, 324-327
basic variables, 325, 347
Bland's rule, 360, 375
brief history of LP, 305
canonical augmented matrix, 347
canonical tableau, 358
complementary slackness, 390, 539
cycling, 360, 375
degenerate basic feasible solution,
325, 328, 360
dual problem, 378, 403
duality, see Duality
duality theorem, 387
examples of, 100, 307-314
extreme point, 331, 332, 334
feasible solution, 325
fundamental theorem of LP, 328
fuzzy, 585
geometric view of, 330
integer linear programming, 427444
interior-point method, 307, 403,
406, 411, 423
Karmarkar's method, see Karmarkar's method
Karush-Kuhn-Tucker
condition,
395, 501, 539
Khachiyan's method, 306, 403-405,
418
Klee-Minty problem, 401
optimal basic feasible solution, 328
optimal feasible solution, 328
primal problem, 378, 403
616
INDEX
reduced cost coefficient, 353, 357,
358, 391
revised simplex method, 364-368
sensitivity, 398
simplex method, 306, 339-368
slack variable, 319
standard form, 318, 324
surplus variable, 319
symmetric duality, 378, 403
tableau, 358
two-dimensional, 314
two-phase affine scaling method,
409
two-phase simplex method, 360364
uncertain, 584-591
weak duality lemma, 385, 399
Linear quadratic regulator, 474
Linear regression, see Line fitting
Linear space, see Vector space
Linear transformation, 25, 63
Linear variety, 47
Linear-fractional LMIs, 534
Linearly dependent, 10
Linearly independent, 9, 176, 324, 451,
484
Little-oh notation, 74, 85
LMI, see Linear matrix inequality
LMI solvers, 529
LMI toolbox for MATLAB, 529, 536
LMITOOL, 536
Local minimizer, 82, 83, 85, 90, 92, 461,
469, 471, 484, 494, 517
Locally convergent, 141, 554, 556
Location parameter, 267
Lower bound, 57
LP, see Linear programming
LQR, 474
Lyapunov inequality, 527, 543
MacDuffee, 241
Markov chain, 397
Mating pool in genetic algorithm, 286
MATLAB, xiii, 127, 128, 136, 139, 159,
191, 215, 271, 290, 291, 299,
301, 337, 376, 424, 543
LMI toolbox, 529, 536
Matrix
affine matrix inequality, 524
compatible norm, 36
condition number, 148
continuous, 60
convergence of sequence, 59
definition of, 12
derivative, 64
determinant, 14
diagonal, 27, 407, 421, 541, 571
eigenvalue of, see Eigenvalue
eigenvector of, see Eigenvector
elementary, see Elementary matrix
entry of, 12
full-rank factorization, 236
function, matrix-valued, 60
game theory, 314
generalized inverse, 236, 238
Gram, 220
Hadamard product, 283
Hessian, 65, 83, 468, 515
identity, 16
image of, see Range of matrix
indefinite, 35
induced norm, 36, 480
inverse, 16
invertible, see Nonsingular matrix
Jacobian, 64
Jordan form, 59
kernel of, see Nullspace of matrix
leading principal minor of, 31
left pseudoinverse, 238
linear matrix inequality, 524, 541
minor of, 15, 428
Moore-Penrose inverse, 236, 237
negative definite, 35
negative semidefinite, 35
nonsingular, 16, 208, 218, 229, 325,
342
notation, 12
nullspace of, 30, 41, 227, 372, 406,
454
orthogonal, 29, 571
orthogonal projector, 30, 226, 406,
408, 423, 549
Penrose generalized inverse, 243
positive definite, 35
positive semidefinite, 35
principal minor of, 31
pseudoinverse, 236, 238
range of, 30, 41
rank of, 13-16
representation of linear transformation, 25
right pseudoinverse, 238
Schur complement, 526
Schur product, 283
sequence of, 59
series of, 60
similar, 26
square, 14
INDEX
stochastic, 397
submatrix of, 432
Sylvester's criterion, 31
symmetric, 28, 35, 139
totally unimodular, 432
trace, 534
transformation, 26
transpose of, 12
unimodular, 428
Matrix norm, 35-39
Matrix-valued function, 60
Max, 21
Maximizer, 82
Mean value theorem, 76, 554, 558
MILP, see Mixed integer linear programming
Min, 15, 82
Minimax, 582, 587, 592
Minimizer
description of, 81
global, 82, 94, 473, 517-519, 521,
537, 552
local, 82, 83, 85, 90, 461, 469, 484,
494, 517
Pareto, 575
strict global, 82
strict local, 82, 92, 102, 471, 494
Minimum norm, 231, 242, 257, 584, 592
Minor
definition of, 15, 428
leading principal, 31
principal, 31
Minty, 401
Mixed integer linear programming, 444
Monotone sequence, 55, 57
Moore-Penrose inverse, 236, 237
Morrison, 208, 229
Multicriteria optimization, 573
Multiobjective optimization, 573, 586
Mutation in genetic algorithm, 288
Naive random search, 279
Natural basis, 11
Negative definite
matrix, 35
quadratic form, 31
Negative half-space, 46
Negative semidefinite
matrix, 35
quadratic form, 31
Neighborhood, 50
Nelder-Mead algorithm, 274-278
centroid, 274
contraction, 276
617
expansion, 275
Neural networks, see Feedforward neural
networks
Neuron, 253
Newton's method
convergence of, 165
descent direction, 167
descent property, 167
for nonlinear least squares, 168-171
Gauss-Newton method, 171
general, 161-171
Levenberg-Marquardt modification
of, 168
modification of, 167
of tangents, 119
one-dimensional, 116-119
order of convergence, 165
Newton-Raphson method, see Newton's
method
Non-strict inequality, 528
Nondecreasing sequence, 55
Nondifferentiable optimization, 523
Nondifferentiable penalty function, 566
Nonincreasing sequence, 55
Nonlinear least squares, 169
Nonsingular matrix, 16, 208, 218, 229,
325, 342
Norm
compatible, 36
Euclidean, 20
Frobenius, 36
general vector norm, 21
induced, 36, 480
matrix, 35-39
p-norm, 21, 584
properties of, 21
Normal, 47, 70
Normal plane, 458
Normal space, 457, 458
Notation, 5
Nullspace of matrix, 30, 41, 227, 372,
406, 454
Objective function, 81
Offspring in genetic algorithm, 287
One-dimensional search methods, 103126
Open set, 51
Optimal basic feasible solution, 328
Optimal control, 472, 475, 481, 482, 553
Optimal feasible solution in LP, 328
Optimization
combinatorial, 273, 282, 297
constrained, 82, 305, 449
618
INDEX
convex, 517-536
linear, see Linear programming
multicriteria, 573
multiobjective, 573, 586
nondifferentiable, 523
semidefinite, 523
unconstrained, see Unconstrained
optimization
vector, 573
with equality constraints, 449, 553
with inequality constraints, 483,
556
with set constraint, 82, 502
Optimization algorithm, see Search
methods
Order of convergence, 148, 149, 152, 153,
157, 165
Order symbol, 73, 149
Orthant, 416
Orthogonal, 70
Orthogonal basis, 29
Orthogonal complement, 29, 226, 458
Orthogonal decomposition, 30, 226
Orthogonal matrix, 29, 571
Orthogonal projection, 30, 219, 406, 408,
423
Orthogonal projector, 30, 226, 406, 408,
423, 549
Orthogonal vectors, 20
Outer product, 197
Output layer in neural network, 254
Overdetermined system of equations, 217
Parents in genetic algorithm, 287
Pareto front, 575
Pareto minimizer, 575
Partial derivative, 64
Particle swarm optimization, 282-285
Particular solution, 346
Penalty function, 560
Penalty method, 560-567
absolute value penalty function,
561
convergence, 565
Courant-Beltrami penalty function,
562, 570
exact penalty function, 566
nondifferentiable penalty function,
566
penalty function, 560
penalty parameter, 560
Penalty parameter, 560
Penrose, see Moore-Penrose inverse
Penrose generalized inverse, 243
Perp, see Orthogonal complement
Pivot, 349, 352, 364
Polak-Ribiere formula, 187
Polyhedron
carrier of, 53
definition of, 52
edge of, 53
face of, 53, 316
in linear programming, 316-318
vertex of, 53
Polynomial, characteristic, 26
Polynomial complexity, 402
Polytope
definition of, 52
in linear programming, 316
Population in genetic algorithm, 285, 286
Positive definite
matrix, 35
quadratic form, 31
relation to eigenvalues, 35
Sylvester's criterion, 31
Positive half-space, 46
Positive orthant, 416
Positive semidefinite
matrix, 35
quadratic form, 31
relation to eigenvalues, 35
relation to principal minors, 35
Positivity, 19, 21
Powell, 162, 188, 202
Powell formula, 188
Powell function, 162
Primal linear program, 378, 403
Primal nonlinear program, 542
Primal quadratic program, 399
Primal-dual method, 377
Principal minor, 31
Principle of induction, 5
Probabilistic search, 278
Probability vector, 397, 540
Product
dyadic, 197
inner, 19, 21
outer, 197
Product rule, 68
Projected algorithm, 546, 556
Projected gradient method, 407, 547,
549-553
stopping criterion, 552
Projected steepest descent algorithm,
551
Projection, 297, see Orthogonal projection, 546
Projective transformation, 416, 424
INDEX
Proof
contradiction (reductio ad absurdum), 5
contraposition, 4
direct method, 4
methods of, 3-5
principle of induction, 5
Proportional fairness, 541
Pseudoinverse, 236, 238
Pythagorean theorem, 21
Q-conjugate
definition of, 176
linear independence, 176
relation to eigenvectors, 189
relation to orthogonality, 189
Quadratic convergence, 148
Quadratic fit, 122, 128
Quadratic form
convex, 512
definition of, 31
maximizing, 467, 471
negative definite, 31
negative semidefinite, 31
positive definite, 31
positive semidefinite, 31, 35
Sylvester's criterion, 31
Quadratic programming, 399, 472, 481,
500
Quasi-Newton methods, 193-211
approximating inverse Hessian, 194
BFGS algorithm, 207-211
complementarity, 207
conjugate direction property, 196
descent property, 193
D F P algorithm, 202-207
duality, 207
rank one formula, 197-202
rank two update, 202
single-rank symmetric, 197
symmetric Huang family, 215
variable metric algorithm, 202
Randomized search, 278
Range of matrix, 30, 41
Rank of matrix, 13-16
Rank one formula, 197-202
Rank two update, 202
Rate of convergence, 141, 148
Ratio of convergence, 148
Rayleigh's inequality, 38, 92, 144, 146,
152
Real vector space, 7
619
Recursive least-squares, see RLS algorithm
Reduced cost coefficient, 353, 357, 358,
391
Reductio ad absurdum, 5
Reeves, 187
Regular point, 451, 456, 461, 464, 484,
554, 556
Relative cost coefficient, see Reduced
cost coefficient
Representation scheme in genetic algorithm, 286
Revised simplex method, 364-368
Revised tableau, 365
Ribiere, 187
Riccati inequality, 544
Right pseudoinverse, 238
RLS algorithm, 227-232, 250
Rosenbrock's function, 68, 159, 173, 191,
215
Roulette-wheel scheme, 286
Row operations, 339-346
Row vector, 7
Scalar, 9
Scale parameter, 267
Schema in genetic algorithm, 292
length of, 294
order of, 294
Schmidt, see Gram-Schmidt
Schur complement, 526
Schur product, 283
Schwarz, see Cauchy-Schwarz inequality
Schwarz's theorem, 65
Scilab Consortium, 536
Search direction, 124, 128, 167, 168
Search methods
bisection method, 116
conjugate direction methods, 175188
conjugate gradient algorithm, 182188
constrained optimization, 545-567
derivative descent search, 154
Fibonacci, 108-115
general algorithm, 211
genetic algorithm, 285-298
Golden section, 104-10§
gradient methods, 131-153
Kaczmarz's algorithm, 232-236,
257
Lagrangian, 553-560
line search, 103, 124, 133, 167, 186,
188, 194, 209
620
INDEX
naive random search, 279
Nelder-Mead algorithm, 274-278
neural network training, 255
Newton's method, 116-119, 1 6 1 171
Newton- Raphson
method,
see
Newton's method
one-dimensional, 103-126
particle swarm optimization, 282285
penalty method, 560-567
probabilistic, 278
projected, 546, 556
projected gradient methods, 547,
549-553
quasi-Newton methods, 193-211
randomized, 278
secant method, 120-122, 172
simulated annealing
algorithm,
278-282
steepest descent method, 133-141
Secant method, 120-122, 172
Second-order necessary condition
equality constraints, 469
inequality constraints, 494
interior case, 90
set constraint, 90
Second-order sufficient condition
equality constraints, 470
inequality constraints, 494
interior case, 92
set constraint, 102
Selection in genetic algorithm, 286
Semidefinite programming, 523
Sensitivity, 398
Sequence
bounded, 56, 57
bounded above, 57
bounded below, 57
convergent, 56
decreasing, 55
Fibonacci, 109
greatest lower bound, 57
increasing, 55
least upper bound, 57
limit, 55
lower bound, 57
monotone, 55, 57
nondecreasing, 55, 564
nonincreasing, 55
of matrices, 59
of real numbers, 55
order of convergence, 148, 149, 152,
153, 157, 165
Set
subsequence of, 58
upper bound, 57
boundary of, 51
bounded, 51
closed, 51
compact, 51
constraint, 81, see Feasible set
convex, see Convex set
feasible, 81, 450
interior of, 51
minus, 5
notation, 5
open, 51
simplex, 274, 412
subset of, 5
Set constraint, 82, 502
Shanno, 207
Sherman-Morrison formula, 208, 229
Sherman-Morrison-Woodbury
formula,
229
Shift parameter, 267
Sigmoid, 266
Signal-to-interference ratio, 88, 98
Similar matrices, 26
Simplex, 274, 412
Simplex algorithm, see Simplex method
Simplex method, 306, 339-368
algorithm, 349-356
artificial problem, 361
artificial variables, 361
Bland's rule, 360, 375
canonical augmented matrix, 346348
canonical tableau, 358
complexity, 402
cycling, 360, 375
exponential complexity, 402
integer linear programming, 427444
matrix form, 356-360
reduced cost coefficient, 353, 357,
358, 391
revised simplex method, 364-368
revised tableau, 365
row operations, 339-346
stopping criterion, 351, 372
tableau, 358
two-phase, 360-364
updating augmented matrix, 348349
updating canonical tableau, 358
Simulating annealing algorithm, 278-282
INDEX
Simultaneous equations, see Linear
equations
Single-rank symmetric algorithm, 197
Singular value decomposition, 571
Slack variable, 319
SONC, see Second-order necessary condition
SOSC, see Second-order sufficient condition
Span, 10
Sphere, 425
Square matrix, 14
SRS algorithm, 197
Standard form linear program, 318, 324
Statement
biconditional, 4
conditional, 3
Steepest ascent, 71
Steepest ascent method, see Steepest descent method
Steepest descent
order of convergence, 152
Steepest descent method, 133-141
for constrained optimization, 551
for quadratic, 139
projected, 551
Step response, 248
Step size, 132, 146, 156, 168, 233, 261,
547, 551
Stiefel, Eduard, 187, 188
Stochastic matrix, 397
Stopping criterion
affine scaling method, 409
conjugate gradient method, 188
genetic algorithm, 290
gradient method, 135
Karmarkar's method, 415, 420
line search, 128
projected gradient method, 552
simplex method, 351, 372
Strict inequality, 528
Strictly interior feasible point, 406, 407,
416
Strong Wolfe condition, 126
Structured representation of LMI, 529
Subgradient, 515, 537
Sublinear convergence, 148
Submatrix, 432
Subsequence, 58
Subset, 5
Subspace, 10
Superlinear convergence, 148
Supervised learning, 256
Supporting hyperplane, 52, 316
621
Supremum, see Least upper bound
Surface, 451, 454
Surplus variable, 319
SVD, see Singular value decomposition
Sylvester's criterion, 31
Symmetric duality, 378, 403
Symmetric Huang family, 215
Symmetric matrix, 28, 35, 139
Symmetry, 19
Tableau in linear programming, 358
Tangent line, 70
Tangent plane, 70, 454
Tangent space, 454, 456, 458
Tangent vector, 65, 70, 454, 456
Taylor series, 72-76, 162, 165, 551. See
also Taylor's theorem
Taylor's formula, 72, 75. See also Taylor's theorem
Taylor's theorem, 72, 85, 90, 92, 132,
515, 516
Temperature schedule, 281
Termination criterion, see Stopping criterion
Third-order necessary condition, 94
Third-order sufficient condition, 94
Threshold, 267
Totally unimodular, 432
Tournament scheme, 286
Trace, 534
Training of neural network, 253
Training set, 255
Transformation
affine scaling, 407
linear, 25, 63
matrix, 26
matrix representation of, 25
projective, 416, 424
Transportation problem, 306, 310
Transpose
matrix, 12
vector, 8
Transposition theorems, 396
Traveling salesperson problem, 282
Triangle inequality, 21
Truth table, 3
Tucker, see K K T condition
Twice continuously differentiate function, 65, 515
Twice differentiable function, 65, 454
Two-dimensional linear program, 314
Two-phase affine scaling method, 409
Two-phase simplex method, 360-364
622
INDEX
Uncertainty range, 106
Unconstrained optimization
basics of, 82
conditions for, 83-92
Uniform continuity, 23
Uniformly continuous function, 23
Unimodal, 104
Unimodular, 428
Unimodular, totally 432
Unsupervised learning, 256
Upper bound, 57
Utility function, 542
Variable metric algorithm, 202
Variety, linear, 47
Vector
column, 7
complex, 12
component of, 7
convex combination, 48, 297, 582
definition of, 7
difference, 8
field, 65
linear combination, 10
linearly dependent, 10
linearly independent, 9, 176, 324.
451, 484
normal, 47
orthogonal, 20
probability, 397, 540
row, 7
tangent, 65, 70, 454, 456
transpose of, 8
zero vector, 8
Vector field, 65
Vector optimization, 573
Vector space
basis for, 11
complex, 12
definition of, 7
dimension of, 11
real, 7
subspace of, 10
Vertex, 53, 274, 406
Weak duality lemma, 385, 399, 543
Weierstrass theorem, 36, 51
Weighted sum, 474, 582, 592
Weights in neural network, 253, 254
Widrow, 257
Widrow-HofT algorithm, 257, 271
Wiener filter, 188, 245
Wolfe condition, 125
Woodbury, 229
XOR, 268, 271
YALMIP, 536
Yet Another LMI Package (YALMIP),
536
Zero finding, 118, 155
Zero matrix, 22, 30, 36
Zero vector, 8
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