ADVANCED
ENGINEERING MATHEMATICS
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ADVANCED
ENGINEERING MATHEMATICS
[For the Students of M.E., B.E. and other Engineering Examinations]
H.K. DASS
M.Sc.
Diploma in Specialist Studies (Maths.)
University of Hull
(England)
Secular India Award - 98 for National Integration and Communal Harmony
given by Prime Minister Shri Atal Behari Vajpayee on 12th June 1999.
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© 1988, H.K. Dass
All rights reserved. No part of this publication may be reproduced or copied in any material form (including photo copying or storing
it in any medium in form of graphics, electronic or mechanical means and whether or not transient or incidental to some other use
of this publication) without written permission of the copyright owner. Any breach of this will entail legal action and prosecution
without further notice.
Jurisdiction : All desputes with respect to this publication shall be subject to the jurisdiction of the Courts, tribunals and forums of
New Delhi, India only.
First Edition 1988
Subsequent Editions and Reprints 1990, 92, 93, 94, 96, 97, 98, 99, 2000 (Twice), 2001 (Twice),
2002, 2003 (Twice), 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012
Twentyfirst Revised Edition 2013
ISBN : 81-219-0345-9
Code : 10A 110
PRINTED IN INDIA
By Rajendra Ravindra Printers Pvt. Ltd., 7361, Ram Nagar, New Delhi -110 055
and published by S. Chand & Company Ltd., 7361, Ram Nagar, New Delhi -110 055.
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PREFACE TO THE TWENTYFIRST REVISED EDITION
I am happy to be able to bring out this revised edition.
Misprints and errors which came to my notice have been corrected.
Suggestions and healthy criticism from students and teachers to improve the book shall be
personally acknowledged and deeply appreciated to help me to make it an ideal book for all.
We are thankful to the Management Team and the Editorial Department of S. Chand & Company
Ltd. for all help and support in the publication of this book.
D-1/87, Janakpuri
New Delhi-110 058
Tel. 28525078, 32985078, 28521776
Mob. 9350055078
hk_dass@yahoo.com
H.K. DASS
Disclaimer : While the author of this book have made every effort to avoid any mistake or omission and have used their skill,
expertise and knowledge to the best of their capacity to provide accurate and updated information. The author and S. Chand do not
give any representation or warranty with respect to the accuracy or completeness of the contents of this publication and are selling
this publication on the condition and understanding that they shall not be made liable in any manner whatsoever. S.Chand and the
author expressly disclaim all and any liability/responsibility to any person, whether a purchaser or reader of this publication or not,
in respect of anything and everything forming part of the contents of this publication. S. Chand shall not be responsible for any
errors, omissions or damages arising out of the use of the information contained in this publication.
Further, the appearance of the personal name, location, place and incidence, if any; in the illustrations used herein is purely
coincidental and work of imagination. Thus the same should in no manner be termed as defamatory to any individual.
(v)
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PREFACE TO THE FIRST EDITION
It gives me great pleasure to present this textbook of Mathematics to the students pursuing
I.E.T.E and various engineering courses.
This book has been written according to the new revised syllabus of Mathematics of I.E.T.E.
and includes topics from the syllabi of the other engineering courses. There is not a single textbook
which entirely covers the syllabus of I.E.T.E. and the students have all along been facing great
difficulties. Endeavour has been made to cover the syllabus exhaustively and present the subject
matter in a systematic and lucid style. More than 550 solved examples on various topics have been
incorporated in the textbook for the better understanding of the students. Most of the examples have
been taken from previous question papers of I.E.T.E. which should make the students familiar with
the standard and trend of questions set in the examinations. Care has been taken to systematically
grade these examples.
The author possesses very long and rich experience of teaching Mathematics to the students
preparing for I.E.T.E. and other examinations of engineering and has first hand experience of the
problems and difficulties that they generally face.
This book should satisfy both average and brilliant students. It would help the students to get
through their examination and at the same time would arouse greater intellectual curiosity in them.
I am really thankful to my Publishers, Padamshree Lala Shyam Lal Gupta, Shri Ravindra Kumar
Gupta for showing personal interest and his General Manager, Shri P.S. Bhatti and Km. Shashi Kanta
for their co-operations. I am also thankful to the Production Manager, Shri Ravi Gupta for bringing
out the book in a short period.
Suggestions for the improvement of the book will be gratefully acknowledged.
D-1/87, Janakpuri
New Delhi-110 058
H.K. DASS
(vi)
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FOREWORD
On my recent visit to India, I happened to meet Prof. H.K. Dass, who has written quite a number
of successful books on Mathematics for students at various levels.
During my meeting, Prof. H.K. Dass presented me with the book entitled
‘‘Advanced Engineering Mathematics” I am delighted to write this Foreword, as I am highly
impressed on seeing the wide variety of its contents. The contents includes many key topics, for
examples, advanced calculus, vector analysis, tensor analysis, fuzzy sets, various transforms and
special functions, probability (curiously some tests of significance are given under that chapter),
numerical methods; matrix algebra and transforms. In spite of this breadth , the development of the
material is very lucid, simple and in plain English.
I know of quite a number of other textbooks on Engineering Mathematics but the material that
has been included in this textbook is so comprehensive that the students of all the engineering streams
will find this textbook useful. It contains problems, questions and their solutions which are useful both
to the teachers and students, and I am not surprised that it has gone through various editions.The
style reminds me of the popular books of Schaum’s Series. I believe that this book will be also helpful
to non-engineering students as a quick reference guide.
This book is a work of dedicated scholarship and vast learning of Mr. Dass, and I have no
hesitation in recommending this book to the students for any Engineering degree world-wide.
Prof. K.V. Mardia
M.Sc. (Bombay), M.Sc.(Pune)
Ph.D. (Raj.), Ph.D. (N’cle),D.Sc.(N’cle)
Senior Research Professor
University of Leeds,
LEEDS (England)
(vii)
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CONTENTS
Chapter
Pages
1. Partial Differentiation
1–90
1.1 Introduction (1); 1.2 Limit (1); 1.3 Working Rule to Find the Limit (1); 1.4 Continuity (3); 1.5
Working Rule for Continuity at a Point (a, b) (4) 1.6 Types of Discontinuity (4); 1.7 Partial
Derivatives (6); 1.8 Partial Derivatives of Higher Orders (8); 1.9 Which Variable is to be Treated as
Constant (13); 1.10 Homogeneous Function (16); 1.11 Euler’s Theorem on Homogeneous Function
(16); 1.12 Total Differential (26); 1.13 Total Differential Co-efficient (26); 1.14 Change of two
Independent Variables x and y by any other Variable t. (26); 1.15 Change in the Independent
Variables x and y by other two Variables u and v. (27); 1.16 Change in both the Independent and
Dependent Variables, (Polar Coordinates) (31); 1.17 Important Deductions (37); 1.18 Typical
z
z
Cases (41); 1.19 Geometrical Interpretation of x and y (44); 1.20 Tangent Plane to a Surface
(44); 1.21 Error Determination (46); 1.22 Jacobians (53); 1.23 PRoperties of Jacobians (56); 1.24
Jacobian of Implicit Functions (60); 1.25 Partial Derivatives of Implicit Functions By Jacobian
(64) 1.26 Taylor’s series of two Variables (67); 1.27 Maximum Value (74); 1.28 Conditions for
Extremum Values (75); 1.29 Working rule to find Extremum Values (76); 1.30 Lagrange Method
of Undetermined Multipliers (81).
2 . Multiple Integral
91–137
2.1 Double Integration (91); 2.2 Evaluation Of Double Integral (91); 2.3 Evaluation of
double Integrals in Polar Co-ordinates (96); 2.4 Change of order of Integration (99);
2.5 Change of Cariables (103); 2.6 Area in Cartesian Co-ordinates (105); 2.7 Area in polar
Co-ordinates (106); 2.8 Volume of solid by rotation of an area (double integral) (109);
2.9 Centre of Gravity (110); 2.10 Centre of Gravity of an arc (112); 1.11 Triple Integration
(114); 2.12 Integration by change of Cartesian Coordinates into Spherical Coordinates
(117) 2.13 Volume =
dx dy dz. (120); 2.14 Volume of Solid bounded by Sphere or by
Cylinder (121); 2.15 Volume of Solid bounded by Cylinder or Cone (123); 2.16 Surface
Area (128); 2.17 Calculation of Mass (131) 2.18 Centre of Gravity (132); 2.19 Moment of
inertia of a Solid (133); 2.20 Centre of Pressure (135).
3. Differential Equations
138 – 222
3.1 Definition (138); 3.2 Order and Degree of a Differential Equation (138); 3.3 Formation
of Differential Equations (138); 3.4 Solution of a Differential Equation (140); 3.5 Differential
Equations of the First Order and First Degree (140); 3.6 Variables Separable (140);
3.7 Homogeneous Differential Equations (142); 3.8 Equations Reducible to Homogeneous
Form (144); 3.9 Linear Differential Equations (147); 3.10 Equations Reducible To The
Linear Form (Bernoulli Equation) (150); 3.11 Exact Differential Equation (154);
3.12 Equations Reducible to the Exact Equations (157); 3.13 Equations of First order and
Higher Degree (161); 3.14 Orthogonal Trajectories (163); 3.15 Polar Equation of the Family
of Curves (165); 3.16 Electrical Circuit Kirchhoff’s Laws (166); 3.17 Vertical Motion
(168); 3.18 Linear Differential Equations of Second order with Constant Coefficients (174);
3.19 Complete Solution = Complementary Function + Particular Integral (174); 3.20 Method
for finding the Complementary Function (175); 3.21 Rules to find Particular Integral (177);
1
1
1 ax
x n [ f ( D)]1 x n .
e ax
e
3.22
(178); 3.23
(180);
f (D)
f ( D)
f (a)
(viii)
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3.24
3.25
1
2
f (D )
sin ax
1
sin ax
2
2
f (D )
f (a )
cos ax
cos ax
f (–a2 )
(181);
1 ax
1
1
.e (x) eax .
.(x) (184); 3.26 To find the Value of
x n sin ax.
f (D)
f (D)
f (D a)
(187); 3.27 General Method of Finding the Particular Integral of any Function f (x) (188);
3.28 Cauchy Euler Homogeneous Linear Equations (189); 3.29 Legendre's Homogeneous
Differential Equations (190); 3.30 Method of Variation of Parameters (193);
dn y
3.31 Simultaneous Differential Equations (195); 3.32 Equation of the Type
f ( x)
n
dx n
d y
(202); 3.33 Equation of the Type
f ( y) (203); 3.34 EQUATION WHICH DO
dxn
NOT CONTAIN ‘y’ DIRECTLY (205); 3.35 EQUATION WHICH DO NOT CONTAIN
‘x’ DIRECTLY (207); 3.36 EQUATION WHOSE ONE SOLUTION IS KNOWN (208);
3.37 NORMAL FORM (REMOVAL OF FIRST DERIVATIVE) (213); 3.38 Method of solving
linear differential equations by changing the independent variable (216); 3.39 Application
of Differential Equations of Second Order (220);
4. Determinants and Matrices
223–371
4.1. Introduction (223); 4.2. Determinant (223); 4.3. Determinant as Eliminant (224);
4.4. Minor (225); 4.5. Cofactor (225); 4.6 Rules of Sarrus (230); 4.7. Properties of
Determinants (231); 4.8. Factor Theorem (248); 4.9 Pivotal Condensation Method (250);
4.10 Conjugate Elements (253); 4.11. Special Types of Determinants (254); 4.12 Laplace
Method For The Expansion of A Determinant In Terms of First Two Rows (255);
4.13.Application of Determinants (256); 4.14. Solution of Simultaneous Linear Equations
By Determinants (Cramer’s Rule) (257); 4.15 Rule for multiplication of two Determinants
(262); 4.16. Condition for Consistency of a System of Simultaneous Homogeneous equations
(263); 4.17. For A System of Three Simultaneous Linear Equations with Three Unknowns
(264); 4.18 Matrices (269); 4.19 Various types of matrices (269); 4.20 Addition of Matrices (272);
4.21 Properties of matrix Addition (274); 4.22 Subtraction of matrices (274); 4.23 Scalar Multiple
of a matrix (274); 4.24 Multiplication (275); 4.25 (AB)´ = B´A´ (275); 4.26 Properties of Matrix
Multiplication (275); 4.27 Mathematical Induction (282) 4.28. Adjoint of a square matrix (283);
4.29 Property of Adjoint matrix (283); 4.30 Inverse of a matrix (284); 4.31 Elementary
Transformations (287) 4.32 Elementry matrices (288); 4.33 Theorem (288); 4.34 To compute the
inverse of a matrix from elementary matrices (Gauss Jordan method) (289); 4.35 The Inverse of a
Symmetric Matrix (289); 4.36 Rank of a matrix (292); 4.37 Normal Form (Canomical Form) (292);
4.38 Rank of Matri by triangular form (297); 4.39 Solution of simultaneous equations (301);
4.40 Gauss-Jordan Method (302); 4.41 Types of Linear Equations (304); 4.42 Consistency of a
system of Linear equations (304); 4.43 Homogeneous equations (309); 4.44 Cramer’s Rule (311);
4.45 Linear Dependence and independence of vectors (313); 4.46 Linearly Dependence and
Independence of Vectors by Rank Method (315); 4.47 Another Method (Adjoining Method) to solve
Linear Equation (317); 4.48 Partitioning of matrices (320); 4.49 Multiplication by Sub-Matrices
(321); 4.50 Inverse by Partitioning (321); 4.51 Eigen Values (325); 4.52 Cayley Hamilton Theorem
(329); 4.53 Power of matrix (Cayley Hamilton Theorem) (333); 4.54 Characteristic Vectors or Eigen
Vectors (335); 4.55 Properties of Eigen Vectors (336); 4.56 Non Symmetric matrices with nonrepeated eigen values (336); 4.57 Non Symmetric matrices with repeated eigen values (338);
4.58 Symmetric matrices with non-repeated eigen values (340); 4.59 Symmetric matrices with repeated
eigen values (342); 4.60 Diagonalisation of a matrix (344); 4.61. Theorem on diagonalisation of a
matrix (344); 4.62 Powers of a matrix (by diagonalisation) (348); 4.63 Sylvester’s Theorem (350);
(ix)
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4.64 Quadratic forms (351); 4.65 Quadratic form expressed in matrices (351); 4.66 Linear
transformation of Quadratic form (353); 4.67 Conical Form of the Sum of the Squares form using
Linear ransformation (353); 4.68 Canonical Form of Sum of the Square for m using orthogonal
Transformation (353); 4.69 Classification of definiteness of a Quadratic form A (354);
4.70 Differentiation and integration of matrices (357); 4.71 Complex Matrices (362); 4.72 Theorem
(362); 4.73 Transpose of Conjugate of a Matrix (363); 4.74 Hermitian Matrix (363); 4.75 SkewHermitian Matrix (365); 2.76 Periodic Matrix (367); 2.77 Idempotent Matrix (367); 4.78 Unitary
Matrix (368) 4.79 The Modules of each Characteristic Roots of a Unitary Matrix is Unity 370
5. Vectors
372–466
5.1 Vectors (372); 5.2 Addition of Vectors (372); 5.3 Rectangular resolution of a vector
(372); 5.4 Unit Vector (372); 5.5 Position vector of a point (373); 5.6 Ratio formula (373);
5.7 Product of two vectors (374); 5.8 Scalar, or dot product (374); 5.9 Useful Results
(374); 5.10 Work Done as a scalar product (374); 5.11 Vector Product or cross product
(375); 5.12 Vector product expressed as a determinant (375); 5.13 Area of parallelogram
(375); 5.14 Moment of a force (376); 5.15 Angular velocity (376); 5.16 Scalar triple product
(376); 5.17 Geometrical interpretation (377); 5.18 Coplanarity questions (378); 5.19 Vector
product of three vectors (379); 5.20 Scalar product of four vectors (381); 5.21 Vector product
of four vectors (381); 5.22 Vector Function (383); 5.23 Differentiation of vectors (383);
5.24 FormulaE of differentiation (383); 5.25 Scalar and Vector point functions (385); 5.26
Gradient of a Scalar Function (386); 5.27 Geometrical meaning of gradient, Normal (386);
5.28 Normal and directional derivative (387); 5.29 Divergence of a vector function (398);
5.30 Physical interpretation of Divergence (398); 5.31 Curl (403); 5.32 Physical meaning
of curl (403); 5.33 Line integral (421); 5.34 Surface integral (428); 5.35 Volume integral
(430); 5.36 Green’s Theorem (for a plane) (431); 5.37 Area of the plane region by Green’s
Theorem (434); 5.38 Stoke’s theorem (Relation between Line Integral and Surface Integral)
(436); 5.39 Another method of proving stoke’s theorem (437); 5.40 Gauss’s theorem of
divergence (452).
6. Complex Numbers
467–505
6.1 Introduction (467); 6.2 Complex Numbers (467); 6.3 Geometrical Representation of
Imaginary Numbers (467); 6.4 Argand Diagram (467); 6.5 Equal Complex Numbers (467);
6.6 Addition of complex numbers (468); 6.7 Addition of Complex Numbers by Geometry
(468); 6.8 Subtraction (468); 6.9 Powers of i (468); 6.10 Multiplication (469); 6.11 i (Iota)
as an operator (470); 6.12 Conjugate of a complex number (470); 6.13 Division (470); 6.14
Division of Complex numbers by Geometry (471); 6.15 Modulus and argument (474); 6.16
Polar form (479); 6.17 Types of Complex Numbers (479); 6.18 Square root of a complex
number (480); 6.19 Exponential and circular functions of complex variables (481); 6.20
De moivre’s theorem (By Exponential Function) (482); 6.21 De moivre’s theorem (by
induction) (482); 6.22 Roots of a complex number (486); 6.23 Circular functions of
complex Numbers (489); 6.24 Hyperbolic Functions (489); 6.25 Relation between circular
and Hyperbolic Functions (490); 6.26 Formulae of hyperbolic functions (490); 6.27
Separation of Real and Imaginary parts of circular functions (493); 6.28 Separation of Real
and Imaginary Parts of Hyperbolic Functions (494); 6.29 logarithmic function of a complex
variable (498); 6.30 Inverse functions (500); 6.31 Inverse Hyperbolic Functions (500);
6.32 Some other inverse functions (502).
7. Functions of a Complex Variable
506–617
7.1 Introduction (506); 7.2 Complex variable (506); 7.3 Functions of a complex variable
(506); 7.4 Neighbourhood of Z0 (506); 7.5Limit of a function of a complex variable (507);
7.6 Continuity (508); Continuity in terms of Real and imaginary parts (508);
(x)
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7.8 Differentiability (509); 7.9 Analytic function (512); 7.10 The necessary condition for f
(z) to be analytic (512); 7.11 Sufficient condition for f (z) to be analytic (513); 7.12 C–R
Equations in Polar Form (520); 7.13 Derivative of w or f (z) in polar form (521);
7.14 Orthogonal Curves (522); 7.15 Harmonic function (523); 7.16 Application to flow
problems (525); 7.17 Velocity Potential Function (526); 7.18 Method to find the conjugate
function (526); 7.19 Milne thomson method (To construct an Analytic function) 533;
7.20 Working Rule: to construct an analytic function by Milne Thomson Method (533);
7.21 Partial differentiation of function of complex variable (539); 7.22Introduction (line
integral) (544); 7.23 Important Definitions (547); 7.24 Cauchy’s integral theorem (548);
7.25 Extension of cauchy’s theorem to multiple connected region (550); 7.26 Cauchy integral
formula (550); 7.27 Cauchy integral formula for the derivative of an analytic function
(551); 7.28 Geometrical representation (558); 7.29 Transformation (558); 7.30 Conformal
transformation (559); 7.31 Theorem. If f (z) is analytic, mapping is conformal 560;
7.32 Theorem (561); 7.33 Translation w = z + C, (562); 7.34 Rotation
w = zeiq (563);
7.35 magnification (563); 7.36 Magnification and rotation (564); 7.37 Inversion and
reflection (566); 7.38 Bilinear transformation (Mobius Transformation) (569); 7.39 Invariant
points of bilinear transformation (569); 7.40 Cross-ratio (570); 7.41 Theorem (570);
7.42 Properties of bilinear transformation (570); 7.43 Methods to find bilinear transformation
(570); 7.44 Inverse point with respect to a circle (575); 7.45 Transformation: w = z2 (580);
7.46 Transformation: w = zn (581); 7.49 Transformation: (584); 7.50 Zero of analytic
Function (585); 7.51Principal Part (585); 7.52 Singular point (585); 7.53 Removable
Singularity (586); 7.54Working Rule to find singularity (586); 7.55Theorem (589);
7.56 Definition of the residue at a pole (589); 7.57 Residue at infinity (590); 7.58 Method
of finding residues (590); 7.59 Residue by definition (591); 7.60 Formula: Residue (592);
7.61 Formula: Residue of (593); 7.62 Formula: Res. (at z = a) (594); 7.63 Formula: Residue
= Coefficient of (594); 7.64 Cauchy’s Residue theorem (596); 7.65 Evaluation of real
definite integrals by contour integration (600); 7.66 Integration round unit circle of the type
(600); 7.67 Evaluation of where are polynomials in x. (609)
8. Special Functions
618–670
8.1 Special functions (618); 8.2 Power series solution of Differential equations (618);
8.3 Ordinary point (618); 8.4 Solution about singular point (622); 8.5 Frobenius Method
(623); 8.6 Bessel’s Equation (632); 8.7 Solution of Bessel’s Equation (632); 8.8 Bessel’s
functions, Jn (x) (633); 8.9 Recurrence Formulae (635); 8.10 Equations Reducible to
Bessel’s Equation (640); 8.11 Orthogonality of Bessel Functions (641); 8.12 A Generating
Function of Jn (x) (642); 8.13 Trigonometric Expansion involving Bessel functions (643);
8.14 Bessel Integral (645); 8.15 Fourier-Bessel Expansion (647); 8.16 Ber and Bei
Functions (649); 8.17 Legendres Equation (651); 8.18 Legendre’s polynomial Pn (x) (653);
8.19 Legendre’s function of the second kind (653); 8.20 General solution of Legendre’s
Equation (654); 8.21 Rodrigue’s Formula (654); 8.22 Legendre Polynomials (656); 8.23
A generating function of Legendre’s polynomial (657); 8.24 Orthogonlity of Legendre
polynomials (659); 8.25 Recurrence Formulae for Pn (x) (662); 8.26 Fourier-Legendre
Expansion (666); 8.27 Laguerres Differential Equation (668); 8.28 Strum Liouville Equation (668); 8.29 Orthogonality (669); 8.30 Orthogonality of Eigen Functions (669).
9. Partial Differential Equations
671–734
9.1 Partial Differential Equations (671); 9.2 Order (671); 9.3 Method of forming Partial
Differential Equations (671); 9.4 Solution of Equation by Direct Integration (672);
9.5 Lagrange’s Linear equation (674); 9.6 Working Rule (675); 9.7 Method of Multipliers
(677); 9.8 Partial Differential Equations non-Linear in p, q (683); 9.9 Charpits Method
(xi)
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(688); 9.10 Linear Homogeneous Partial Diff. Eqn. (691); 9.11 Rules for finding the
complementary function (691); 9.12 Rules for finding the particular integral (692);
9.13 Non-Homogeneous Linear Equations (700); 9.14 Monge’s Method (704); 9.15 Introduction (707); 9.16 Method of Separation of Variables (707); 9.17 Equation of vibrating
string (710); 9.18 Solution of Wave equation by D’Almbert’s method (718); 9.19 One
dimensional Heat flow (720); 9.20 Two dimensional Heat Flow (725); 9.21 Laplace Equation in polar co-ordinates (729); 9.22 Transmission line Equations (732).
10. Statistics
735–762
10.1 Statistics (735); 10.2 Frequency distribution (735); 10.3 Graphical Representation
(735); 10.4 Average or Measures of Central Tendency (736); 10.5 Arithmetic Mean (736);
10.6 Median (737); 10.7 Mode (738); 10.8 Geometric Mean (739); 10.9 Harmonic Mean
(739); 10.10 Average Deviation or Mean Deviation (740); 10.11 Standard Deviation (740);
10.12 Shortest method for calculating Standard Deviation (740); 10.13 Moments (742);
10.14 Moment generating function (743); 10.15 Skewness (743); 10.16 Correlation (745);
10.17 Scatter diagram or Dot-diagram (746); 10.18 Karl Pearson’s Coefficient of Correlation (746); 10.19 Short cut Method (748); 10.20 Spearman’s Rank Correlation (750);
10.21 Spearman’s Rank Correlation Coefficient (750); 10.22 Regression (752); 10.23
Line of Regression (752); 10.24 Equations to the lines of Regression (753); 10.25 Error of
Prediction (759).
11. Probability
763–849
11.1 Probability (763); 11.2 Definitions (763); 11.3 Addition law of Probability (765);
11.4 Multiplication law of Probability (767); 11.4 (b) Baye’s Theorem (779); 11.5 Binomial
Distribution (781); 11.6 Mean of Binomial Distribution (787); 11.7 Standard Deviation of
Binomial Distribution (787); 11.8 Central Moments (790); 11.9 Moment Generating
Functions (791); 11.10 Recurrence Relation for Binomial Distribution (792); 11.11 Poisson
Distribution (794); 11.12 Mean of Poisson Distribution (794); 11.13 Standard deviation of
Poisson Distribution (795); 11.14 Mean Deviation (796); 11.15 Moment Generating
Function (797); 11.16 Cumulants (797); 11.17 Recurrence Formulae (798); 11.18
Continuous Distribution (806); 11.19 Moment Generating Function (808); 11.20 Normal
Distribution (809); 11.21 Normal Curve (809); 11.22 Mean for Normal Distribution (810);
11.23 Standard Deviation for Normal Distribution (810); 11.24 Median of the Normal
Distribution (811); 11.25 Mean Deviation (811); 11.26 Mode of the Normal Distribution
(811); 11.27 Moment of Normal Distribution (812); 11.28 Area under the normal curve
(815); 11.29 Other Distributions (823); 11.30 Population (824); 11.31 Sampling (824);
11.32 Parameters and statistics (824); 11.33 Aims of a sample (825); 11.34 Types of
sampling (825); 11.35 Sampling Distribution (825); 11.36 Standard error (825); 11.37
Sampling Distribution of Means (825); 11.38 Sampling Distribution of Variance (827);
11.39 Testing a Hypothesis (827); 11.40 Null Hypothesis (827); 11.41 Errors (827); 11.42
Level of significance (827); 11.43 Test of significance (828); 11.44 Confidence limits (828);
11.45 Test of significance of Large samples (828); 11.46 Sampling Distribution of the
proportion (829); 11.47 Estimation of the parameters of the population (829); 11.48
Comparison of Large Samples (830); 11.49 The t Distribution (small sample) (831); 11.50
Working Rule (832); 11.51 Testing for Difference between two t samples (836); 11.52 The
Chi-square Distribution (839); 11.53 Degree of freedom (839); 11.54 x2 curve (840); 11.55
Goodness of fit (840); 11.56 Steps for testing (840); 11.57 F-Distribution (846); 11.58
Fisher z Distribution (847).
(xii)
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12. Fourier Series
850–884
12.1 Periodic Functions (850); 12.2 Fourier series (850); 12.3 Dirichlet’s Conditions (851);
12.4 Advantages of Fourier Series (851); 12.5 Useful Integrals (851); 12.6 Determination
of Fourier constants (Euler’s Formulae) (851); 12.7 Functions defined in two or more sub
spaces (855); 12.8 Even Functions (861); 12.9 Half Range’s series (864); 12.10 Change of
Interval (866); 12.11 Parseval’s Formula (874); 12.12 Fourier series in Complex Form (879);
12.13 Practical Harmonic Analysis (880).
13. Laplace Transformation
885–932
13.1 Introduction (885); 13.2 Laplace Transform (885); 13.3 Important Formulae (885);
13.4 Properties of Laplace Transforms (888); 13.5 Laplace Transform of the Derivative of
f (t) (889); 13.6 Laplace Transform of Derivative of order n (890); 13.7 Laplace Transform
of Integral of f (t) (890); 13.8 Laplace Transform of t · f (t) (Multiplication by t) (891);
13.9 Laplace Transform of
1
f (t) (Division by t) (893); 13.10 Unit step function (895);
t
13.11 Second shifting theorem (896); 13.12 Theorem (896); 13.13 Impulse Function (898);
13.14 Periodic Functions (899); 13.15 Convolution Theorem (903); 13.16 Laplace
Transform of Bessel function (903); 13.17 Evaluation of Integral (904); 13.18 Formulae of
Laplace Transform (905); 13.19 Properties of Laplace transform (906); 13.20 Inverse of
Laplace Transforms (906); 13.21 Important Formulae (907); 13.22 Multiplication by s (908);
13.23 Division by s (Multiplication by
1
(909); 13.24 First shifting property (910);
s
13.25 Second shifting property (911); 13.26 Inverse Laplace transformation of Derivatives
(913); 13.27 Inverse Laplace Transform of Integrals (913); 13.28 Partial Fraction Method
(914); 13.29 Inverse Laplace transformation (915); 13.30 Solution of Differential Equations
(916); 13.31 Solution of simultaneous equations (924); 13.32 Inversion Formula for the
Laplace transform (927).
14. Integral Transforms
933–981
14.1 Introduction (933); 14.2 Integral Transforms (933); 14.3 Fourier Integral Theorem
(934); 14.4 Fourier sine and cosine Integrals (935); 14.5 Fourier’s Complex Integral (936);
14.6 Fourier Transforms (938); 14.7 Fourier sine and cosine Transforms (939);
14.8 Properties of Fourier Transform (947); 14.9 Convolution (951); 14.10 Parseval’s
Identity for Fourier Transform (951); 14.11 Parseval’s identity cosine Transform (952);
14.12 Parseval’s identity for sine Transform (952); 14.13 Fourier Transforms of Derivative
of a function (958); 14.14 Relationship Between Fourier and Laplace Transforms (959);
14.15 Solution of Boundary value problems by using integral transform (959); 17.16 Fourier
Transforms of Partial Derivative of a Function (965); 14.17 Finite Fourier Transforms (969);
14.18 Finite Fourier sine and Cosine transforms of Derivatives (976).
15. Numerical Techniques
982–1025
15.1 Introduction (982); 15.2 Solution of the equations graphically (982); 15.3 NewtonRaphson Method or Successive substitution method (984); 15.4 Rule of False position
(Regula False) (989); 15.5 Iteration Method (993); 15.6 Solution of Linear systems (994);
15.7 Crout’s Method (996); 15.8 Iterative Methods or Indirect Methods (1000);
15.9 Jacobi’s Method (1000); 15.10 Gauss-Seidel Method (1002); 15.11 Solution of
Ordinary Differential Equations (1006); 15.12 Taylor’s Series Method (1006); 15.13 Picard’s
(xiii)
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method of successive approximations (1010); 15.14 Euler’s method (1014); 15.15 Euler’s
Modified formula (1016); 15.16 Runge’s Formula (1017); 15.17 Runge’s Formula (Third
order) (1018); 15.18 Runge’s Kutta Formula (Fourth order) (1019); 15.19 Higher order
Differential Equations (1023).
16. Numerical Method for Solution of Partial Differential Equation
1026–1041
16.1 General Linear partial differential equations (1026); 16.2 Finite-Difference
Approximation to Derivatives (1026); 16.3 Solution of Partial Differential equation
(Laplace’s method) (1027); 16.4 Jacobi’s Iteration Formula (1029); 16.5 Gauss-Seidel
method (1029); 16.6 Successive over-Relaxation or S.O.R. Method (1029); 16.7 Poisson
Equation (1034); 16.8 Heat equation (Parabolic Equations) (1036); 16.9 Wave equation
(Hyperbolic Equation) (1039).
17. Calculus of Variations
1042–1054
17.1 Introduction (1042); 17.2 Functionals (1042); 17.3 Definition (1042); 17.4 Euler’s
Equation (1043); 17.5 Extremal (1045); 17.6 Isoperimetric Problems (1049); 17.7
Functionals of second order derivatives (1053).
18. Tensor Analysis
1055–1084
18.1 Introduction (1055); 18.2 Co-ordinate Transformation (1055); 18.3 Summation
Convention (1056); 18.4 Summation of co-ordinates (1056); 18.5 Relation between the
direction cosines of three mutually perpendicular straight lines (1057); 18.6 Transformation
of velocity components on change from one system of rectangular axes to another (1057);
18.7 Rank of a tensor (1058); 18.8 First order tensors (1058); 18.9 Second order tensors
(1058); 18.10 Tensors of any order (1059); 18.11 Tensor of zero order (1059); 18.12
Algebraic operations on tensors (1059); 18.13 Product of two tensors (1059); 18.14 Quotient
law of tensors (1060); 18.15 Contraction theorem (1060); 18.16 Symmetric and
antisymmetric tensors (1061); 18.17 Symmetric and skew symmetric tensors (1061); 18.18
Theorem (1062); 18.19 A fundamental property of tensors (1062); 18.20 Zero tensor
(1062); 18.21 Two special tensors (1063); 18.22 Kronecker tensor (1063); 18.23 Isotropic
Tensor (1064); 18.24 Relation between alternate and kronecker tensor (1064); 18.25
Matrices and tensors of first and second order (1065); 18.26 Scalar and vector products of
two vectors (1065); 18.27 The three scalar Invariants of a second order tensor (1065);
18.28 Singular and non-singular tensors of second order (1066); 18.29 Reciprocal of a
Non-singular tensor (1066); 18.30 Eigen values and Eigen Vectors of a tensor of second
order (1067); 18.31 Theorem (1067); 18.32 Reality of the eigen values (1068); 18.33
Association of a skew symmetric tensors of order two and vectors (1068); 18.34 Tensor
fields (1069); 18.35 Gradient of tensor fields:gradient of a scalar function (1069); 18.36
Gradient of vector (1069); 18.37 Divergence of vector point function (1069); 18.38 U curl
of a vector point fuion (1069); 18.39 Second order differential operators (1071); 18.40
Tensorial form of Gauss’s and Stoke’s theorem (1071); 18.41 Stoke’s theorem (1071); 18.42
Relation between alternate and kronecker tensor (1072); 18.43 The three scalar invariants
of a second order tensor (1073); 18.44 Tensor analysis (1073); 18.45 Conjugate or
reciprocal tensors (1078); 18.46 Christoffel symbols (1078); 18.47 Transformation law
for second kind (1079); 18.48 Contravariant, covariant and mixed tensor (1082).
19. Z-transforms
1085–1118
19.1 Introduction (1085); 19.2 Sequence (1085); 19.3 Representation of Sequence (1085);
19.4 Basic Operations on Sequences (1086); 19.5 Z-Transforms (1086); 19.6 Properties
(xiv)
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of Z-Transforms (1087); 19.7 Theorem (1094); 19.8 Change of Scale (1095); 19.9 Shifting
Property (1096); 19.10 Inverse Z-Transform (1096); 19.11 Solution of Difference Equations
(1096); 19.12 Multiplication by K (1097); 19.13 Division by K (1097); 19.14 Initial Value
(1098); 19.15 Final Value (1098); 19.16 Partial Sum (1098); 19.17 Convolution (1099); 19.18
Convolution Property of Casual Sequence (1099); 19.19 Transform of Important Sequences
(1100); 19.20 Inverse of Z-Transform by division (1102); 19.21 By Binomial Expansion and
Partial Fraction (1104); 19.22 Partial Fractions (1105); 19.23 Inversion by Residue Method
(1111); 19.24 Solution of Difference Equations (1114).
20. Infinite Series
1119–1157
20.1 Sequence (1119); 20.2 Limit (1119); 20.3 Convergent Sequence (1119); 20.4 Bounded
Sequence (1119); 20.5 Monotonic Sequence (1119); 20.6 Remember the following limits
(1120); 20.7 Series (1120); 20.8 Convergent, Divergent and Oscillatory Series (1120);
20.9 Properties of Infinite Series (1120); 20.10 Properties of Geometric Series (1121);
20.11 Positive Term Series (1122); 20.12 Necessary Conditions for Convergent Series
(1123); 20.13 Cauchy’s Fundamental Test for Divergence (1123); 20.14 p-Series (1124);
20.15 Comparison Test (1125); 20.16 D’Alembert’s Ratio Test (1129); 20.17 Raabe’s Test
(1132); 20.18 Gauss’s Test (1139); 20.19 Cauchy’s Integral Test (1140); 20.20 Cauchy’s
Root Test (1142); 20.21 Logarithmic Test (1144); 20.22 DeMorgan’s and Bertrand’s Test
(1147); 20.23 Cauchy’s Condensation Test (1148); 20.24 Alternating Series (1148);
20.25 Leibnitz’s Rule for Convergence of an Alternating Series (1148); 20.26 Alternating
Convergent Series (1149); 20.27 Power Series in X (1151); 20.28 Exponential Series
(1152); 20.29 Logarithmic Series (1152); 20.30 Binomial Series (1152); 20.31 Uniform
Convergence (1153); 20.32 Abel’s Test (1153); 20.33 Brief Procedure for Testing a Series
for Convergence (1154); 20.34 List of the Tests for Convergence (1155).
21. Gamma, Beta Functions, Differentiation under the Integral Sign
1158–1188
21.1 Gamma Function (1158); 21.2 Transformation of Gamma Function (1160); 21.3 Beta
Function (1161); 21.4 Evaluation of Beta Function (1162); 21.5 A property of Beta Function
(1162); 21.6 Transformation of Beta Function (1163); 21.7 Relation between Beta and
Gamma Functions (1164); 21.8 Liouville’s Extension of Dirichlet Theorem (1174);
21.9 Elliptic Integrals (1176); 21.10 Definition and property (1176); 21.11 Error Function
(1179); 21.12 Differentiation under the integral sign (1181); 21.13 Leibnitz’s Rule (1181);
21.14 Rule of differentiation under the integral sign when the limits are functions of parameter
(1185).
22. Chebyshev Polynomials
1189 — 1202
22.1 Introduction (1189); 22.2 Chebyshev Polynomials (Tchebcheff Or Tschebyscheff Polynomials)
(1189); 22.3 Orthogonal Properties of Chebyshev Polynomials. (1190); 22.4 Recurrence Relation
of Chebyshev Polynomials (1191); 22.5 Powers of X in Terms of T2 (X) (1192); 22.6 Recurrence
formulae for Un (x) (1194); 22.7 Generating Function for Tn (x) (1199).
23. Fuzzy sets
1203 – 1207
23.1 Introduction (1203); 23.2 Fuzzy set (1203); 23.3 Equality of two fuzzy sets (1204); 23.4
Complement of a fuzzy set (1204); 23.5 Union of two fuzzy sets (1204);
23.6 Intersection of two fuzzy sets (1204); 23.7 Truth value (1205); 23.8 Application (1206).
24. Hankel Transform
1208 – 1229
24.1 Hankel Transform (1208); 24.2. The Formulae used in Finding the Hankel
Transforms (1208); 24.3 Some More Integrals Involving Exponential Functions and
Bessel’s Function (1209 ); 24.4 Inversion formula for Hankel Transform (1215 );
(xv)
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24.5 Parseval’s Theorem for Hankel Transform (1215); 24.6. Hankel Transformation of
the Derivative of a function (1215); 24.7. Finite Hankel Transmission formation (1221);
24.8 Another form of Hankel transform (1222).
25.
Hilbert Tranform
1230 – 1231
25.1 Introduction (1230); 25.2. Elementary Function and their Hilbert Transform (1230); 25.3.
Properties (1231) 25.4. Applications (1231).
26.
Empirical Laws and Curve Fitting (Method of Least Squares)
1232 – 1242
26.1 Empirical Law (1232); 26.2. Curve Fitting (1232); 26.3. Graphical Method (1232);
26.4 Determination of other Empirical Laws Reducible to Linear form (1232); 26.5 Principle
of Least Squares (1234); 26.6 Method of Least Squares (1235); 26.7 Change of Scale (1238).
27.
Linear Programming
1243 – 1303
27.1 Introduction (1243); 27.2 Some definitions (1244); 27.3 Graphical method (1251);
27.4 Corner point Method (1251); 27.5 Iso-profit or Iso-cost method (Maximum Z) (1256);
27.6 Iso-profit or iso-cost method (Minimum Z) (1256); 27.7 Solution of linear programming
problems (1263); 27.8 Simplex method (1277); 27.9 Degeneracy (1287); 27.10 Duality
(1292); 27.11 Dual of L.P.P. (1292); 27.12 North West Corner Method, (1297); 12.13 Vogel’s
approximation method (VAM) (1299).
Useful Formulae
1305–1312
Solved Question Paper 2007
1313–1335
Question Papers, 2006, 2005 and 2004
1336–1353
Index
1355–1358
(xvi)
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1
Partial Differentiation
1.1 INTRODUCTION
Area of a rectangle depends upon its length and breadth, hence we can say that area is the
function of two variables i.e. its length and breadth.
z is called a function of two variables x and y if z has one definite value for every pair of
values of x and y. Symbolically, it is written as
z = f (x, y)
The variable x and y are called independent variables while z is called the dependent variable.
Similarly, we can define z as a function of more than two variables.
Geometrically: Let z = f (x, y)
where x, y belong to an area A of the xy-plane. For each point (x, y) corresponds a value of z. These
values of (x, y, z) form a surface in space.
Hence, the function z = f (x, y) represents a surface.
1.2 LIMIT
The function f (x, y) is said to tend to the limit l as x a and y b if and only if the limit
l is independent of the path followed by the point (x, y) as x a and y b. Then
lim f ( x, y ) = l
x a
y b
The function f (x, y) in region R is said to tend to the limit l as x a and y b if and only
if corresponding to a positive number (a, b), there exists another positive number such that
f (x, y) – l < for 0 < (x – a)2 + (y – b)2 < 2
for every point (x, y) in R.
1.3 WORKING RULE TO FIND THE LIMIT
Step 1. Find the value of f (x, y) along x a and y b.
Step 2. Find the value of f (x, y) along y b and x a.
If the values of f (x, y) in step 1 and step 2 remain the same, the limit exists otherwise
not.
Step 3. If a 0 and b 0, find the limit along y = mx or y = mxn. If the value of the limit
does not contain m then limit exists. If it contains m, the limit does not exist.
Note. (i) Put x = 0 and then y = 0 in f. Find its value f1.
(ii) Put y = 0 and then x = 0 in f. Find the value f2.
If f1 f2, limit does not exist.
If f1 = f2, then
1
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Partial Differentiation
2
(iii) Put y = mx and find the limit f3.
If f1 = f2 f3, then limit does not exist.
If f1 = f2 = f3, then
2
(iv) Put y = mx and find the limit f4.
If f1 = f2 = f3 f4, then limit does not exist.
If f1 = f2 = f3 = f4, then limit exists.
2
Example 1. Evaluate lim
x0
y0
Solution.
(i) lim
x
y
(ii) lim
x
y
x y
4
x y2
x2 y
4
x y
2
x2 y
4
x y
2
0
lim
= 0 = f1 (say)
0 y2
y 0
0
lim
= 0 = f2 (say)
4
x 0
x 0
Here, f1 = f2, therefore
(iii) Put y = mx
lim
x
y
x 2 mx
mx
= 0 = f3
lim 2
4
2 2
x 0 x m 2
x m x
(say)
Here, f1 = f2 = f3, therefore
(vi) Put y = mx2
lim
x
y
x 2 mx
4
2 4
x m x
m
1 m2
= f4
Here, f1 = f2 = f3 f4
Thus, limit does not exist.
3
Ans.
3
Example 2. Evaluate lim ( x y ).
x 0
y0
Solution.
(i) lim ( x 3 y 3 ) lim (0 y 3 ) = 0 = f1
(say)
(ii) lim ( x 3 y 3 ) lim ( x 3 0) = 0 = f2
(say)
x
y
y0
x
y
x 0
Here, f1 = f2, therefore
(iii) Put y = mx
lim ( x 3 y 3 ) lim lim ( x 3 y 3 ) lim ( x 3 m 3 x 3 ) = 0 = f3 (say)
x 0
x 0
y mx
x
y
Here, f1 = f2 = f3, therefore
(iv) Put y = mx2
lim ( x3 y 3 ) lim lim ( x 3 y 3 ) lim ( x3 m3 x 6 )
x
x 0
x 0
y mx2
y
= lim x 3 (1 m 3 x 3 ) = 0 = f4
x0
Here, f1 = f2 = f3 = f4
Thus, limit exists with value 0.
Example 3. Evaluate lim
x 1
y 2
3x 2 y
x2 y 2 5
(say)
Ans.
.
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Partial Differentiation
lim
Solution.
x 1
y 2
3
3x 2 y
3x 2 y
3 x 2 (2)
lim lim 2
lim 2
2
x y 5 x 1 y 2 x y 5 x1 x (2)2 5
2
2
= lim
x 1
Example 4. Evaluate lim
x
y 3
Solution.
2x 3
3
x 4 y3
6x2
2
x 9
6
3
19 5
Ans.
.
2x 3
lim lim 3
y
3
x
x 4y
x 4 y 3
2
3
3
2
00
x
= lim lim x
lim
0 f1
3
y 3 x
y y 3 1 4(0)
1 4
x
2x 3
2x 3
lim lim 3
(ii) lim 3
y 3 x 4 y 3
x y 3 x 4 y 3
x
2
3
3
2
2x 3
00
x
x
lim
0 f2
= lim 3
108 1 0
x x 108
x
1 3
x
Here, f1 = f2.
(i)
lim
x
y 3
2x 3
3
3
Hence, the limit exists with value 0.
(say)
(say)
Ans.
EXERCISE 1.1
Evaluate the following limits:
1.
3.
lim
x 1
y2
lim
x
y3
5.
lim
x 0
y0
6. lim
x 1
y 1
8.
lim
x 0
y 0
10. lim
x 1
y 1
1.4
2 x2 y 2
2 xy
2 xy 3
3
x 4y
xy
x2 y 2
3
3
4
2. lim
Ans. 0
4. lim
Ans.
,
x2 y3
x2 y2
x 0, y 0
3x ( y 2)
2 y ( x 2)
x 0
y 0
x2 y
xy
y x2
,
Ans. 17
; x 0, y 0
Ans. Limit does not exist
Ans. Limit does not exist
; x 0, y 0
xy 2 x
xy 2 y
x 2
y 3
x3 y 2
Ans. 1
7. lim
Ans. 0
9. lim
x 0
y 0
x 0
y0
Ans.
x3 2 y3
x 0, y 0
Ans. 0
, x 0, y 0
Ans. 0
x2 4 y2
xy 2
x2 y 2
1
2
CONTINUITY
A function f (x, y) is said to be continuous at a point (a, b) if
lim
( x, y ) ( a , b )
f ( x, y ) = f (a, b)
A function is said to be continuous in a domain if it is continuous at every point of the domain.
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Partial Differentiation
4
1.5
WORKING RULE FOR CONTINUITY AT A POINT (a, b)
Step 1. f (a, b) should be well defined
Step 2.
lim
f ( x, y ) should exist.
( x, y ) ( a , b )
Step 3.
f ( x, y ) = f (a, b)
lim
( x, y ) ( a , b )
x3 y 3
when x 0,
2
2
Example 5. Test the function f (x, y) = x y
when x 0,
0
for continuity.
Solution. Step 1. The function is well defined at (0, 0).
y0
y0
x3 y 3
lim
lim
lim
f ( x, y ) = ( x, y ) (0, 0) x 2 y 2 x 0 y mx x 2 y 2
Step 2.
( x , y ) (0, 0)
3
3 3
3
x m x
x (1 m )
lim
= xlim
0 x 2 m2 x 2 = x 0 1 m2 = 0
Thus, limit exists at (0, 0).
Step 3. limit of f (x) at origin = value of the function at origin.
lim
lim
x3 y 3
( x , y ) (0, 0)
x2 y 2
x3 y3
= f (0, 0) = 0
Hence, the function f is continuous at the origin.
Ans.
x
, x 0, y 0
2
Example 6. Discuss the continuity of f (x, y) = x y 2
x 0, y 0
2,
at the origin.
x
, x 0, y 0
2
2
Solution. Here, we f (x, y) = x y
x 0, y 0
2,
Step 1. The function f (x, y) at (0, 0) is well defined.
1
x
x
x
lim
lim lim
lim
Step 2. ( x, y )lim
=
=
x0
(0, 0)
x 0 y mx
1 m2
x2 y 2
x 2 y 2 x 0 x 2 m 2 x 2
For different values of m the limit f is not unique.
so the ( x, y )lim
(0, 0)
x
2
x y2
does not exist.
Hence f (x, y) is not continuous at origin.
1.6 TYPES OF DISCONTINUITY
(Gujarat Univ. I sem. Jan. 2009)
Ans.
Y
1. First Kind. f (x) is said to have discontinuity of first kind at
0)
f (x 1–
+
x1
f(
the point x = x1 if Right limit f (x1 + 0) and left limit f (x1 – 0) exist
but are not equal.
O
0)
x1
(First kind)
X
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Partial Differentiation
5
Y
2. Second Kind. f (x) is said to have discontinuity of the second
kind at x = x1 if neither right limit f (x1 + 0) exists nor left limit
f (x1 – 0) exists.
f (x 1–
+0
x1
f(
O
0)
)
x1
(Second kind)
X
3. Third Kind (Mixed discontinuity). f (x) is said to have mixed discontinuity at the point
x = x1 if only one of the two limits right limit f (x1 + 0) and left limit f (x1 – 0) exists and not the
other.
Y
Y
+
x1
f(
0)
0)
f (x 1–
OR
X
x1
Third kind (i)
O
f(
+
x1
0)
f (x 1–
0)
X
x1
Third kind (ii)
O
4. Fourth Kind (Infinite discontinuity). f (x) is said to have infinite discontinuity at the point
x = x1 if either one or both limits right limit and left limit f (x1 – 0) is infinite.
If both limits do not exist and if f (x1 ± h) oscillates between limits one of which is infinite
as ± h 0. It is also a point of infinite discontinuity.
X
x
(Fourth kind)
)
+0
1
f (x
f (x1 + 0)
O
OR
X
O
5. Fifth Kind (Removable discontinuity). If right limit f (x1 + 0)
is equal to left limit f (x1 – 0) is not equal to f (x1), then f (x) is said to
have removable discontinuity.
EXERCISE 1.2
)
–0
OR
x1
f(
1
x1
Y
)
–0
f (x
O
)
f (x 1– 0
Y
f (x1 + 0)
Y
X
x
Y
+
x1
f(
O
0)
)
–0
f (x 1
x1
(Fifth kind)
X
Test for continuity:
xy ( x 2 y 2 )
, when x 0, y 0
1. f (x, y) = x 2 y 2
when x 0, y 0
0,
at origin.
x2 y2
, when x 0, y 0
2. f (x, y) = x 2 y 2
when x 0, y 0
0,
at origin.
x3 y3
, when x 0, y 0
3. f (x, y) = x3 y 3
at origin.
when x 0, y 0
0,
Ans. Continuous at origin.
Ans. Not continuous at origin.
Ans. Not continuous at origin.
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Partial Differentiation
6
4.
5.
6.
7.
8.
1.7
xy
, when x 0, y 0
2
2
f (x, y) = x y
0,
when x 0, y 0
at origin.
Ans. Not continuous at origin.
x3 y 3 , when x 0, y 0
f (x, y) =
when x 0, y 0
0,
at origin.
Ans. Continuous at origin.
x2 2 y
,
f (x, y) = x y 2
when x 1, y 2
1
at the point (1, 2).
Ans. Continuous at (1, 2).
2 x 2 y , ( x, y ) (1, 2)
Show that the function f (x, y) =
( x, y ) (1, 2)
0,
is discontinuous at (1, 2).
1
( x y ) sin
, x y 0
Show that the function f (x, y) =
x y
x y0
0,
is continuous at (0, 0) but its partial derivatives of first order do not exist at (0, 0).
(A.M.I.E.T.E., Dec. 2007)
PARTIAL DERIVATIVES
Let z = f (x, y) be function of two independent variables x and y. If we keep y constant and
x varies then z becomes a function of x only. The derivative of z with respect to x, keeping y as
constant is called partial derivative of ‘z’, w.r.t. ‘x’ and is denoted by symbols.
z f
,
, f (x, y) etc.
x x x
f ( x x, y ) f ( x, y )
z
= lim
x
0
x
x
The process of finding the partial differential coefficient of z w.r.t. ‘x’ is that of ordinary
differentiation, but with the only difference that we treat y as constant.
Similarly, the partial derivative of ‘z’ w.r.t. ‘y’ keeping x as constant is denoted by
Then
z f
, , fy (x, y) etc.
y y
z
=
y
Notation.
z
= p,
x
lim
y 0
f ( x , y y ) f ( x, y )
y
z
= q,
y
2z
x
2
= r,
2 z
= s,
x y
2 z
y 2
=t
u
u
1 x
1 y
Example 7. If u = sin tan , then find the value of x
y .
y
x
x
y
1 x
1 y
Solution.
u = sin tan
y
x
u
1
1
1
1
y
y
.
. 2
2
=
2
2 y
2
2
x
x y2
y x
y x
x
1
1
x
y
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Partial Differentiation
x
u
=
x
u
=
y
7
x
2
y x
2
xy
...(1)
2
x y2
1
1
1
x
x
x
2
2
2
2
2
x
x y2
y y x
x y 1 y
1
x
y
u
x
xy
y.
2
=
2
2
y
x
y2
y x
2
On adding (1) and (2), we have x.
u
u
y.
=0
x
y
...(2)
Ans.
u
u
and
if u = er cos . cos (r sin )
r
u = er cos . cos (r sin )
u
= er cos . [– sin (r sin ).sin ] + [cos .er cos ].cos (r sin )
r
(keeping as constant)
= er cos .[– sin (r sin ).sin + cos (r sin ).cos ]
= er cos .cos (r sin + )
Ans.
u
= er cos .[– sin (r sin ).r cos ] + [–r sin .er cos ].cos (r sin )
(keeping r as constant)
= – r er cos . [sin (r sin ).cos + sin cos (r sin )]
= – r er cos . sin (r sin + )
Ans.
Example 8. Find
Solution.
Example 9. If u = (1 – 2xy + y2)–1/2 prove that, x
u
u
y
= y2 u3.
x
y
Solution.
u = (1 – 2xy + y2)–1/2
Differentiating (1) partially w.r.t. ‘x’, we get
...(1)
u
1
2 3/2
( 2 y )
= (1 2 xy y )
x
2
u
x
= xy (1 – 2xy + y2)–3/2
x
Differentiating (1) partially w.r.t. ‘y’, we get
u
1
2 3/2
( 2 x 2 y )
= (1 2 xy y )
y
2
u
y
= (xy – y2) (1 – 2xy + y2)–3/2
y
Subtracting (3) from (2), we get
u
u
x
y
= xy (1 – 2xy + y2)–3/2 – (xy – y2) (1 – 2xy + y2)–3/2
x
y
= y2 (1 – 2xy + y2)–3/2 = y2u3.
Example 10. If z = eax + by.f (ax – by), prove that b
...(2)
...(3)
Proved.
z
z
a
= 2abz.
x
y
(A.M.I.E.T.E., Summer 2004)
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Partial Differentiation
8
ax + by
Solution.
z = e
.f (ax – by)
Differentiating (1) w.r.t. ‘x’, we get
z
= a eax + by.f (ax – by) + eax + by.a f (ax – by)
x
z
b
= a b eax + by.f (ax – by) + a b eax + by.f (ax – by)
x
Differentiating (1) w.r.t. ‘y’, we get
z
= b eax + by.f (ax – by) + eax + by.(– b) f (ax – by)
y
z
= a b eax + by.f (ax – by) – a b eax + by.f (ax – by)
y
On adding (2) and (3), we get
z
z
b a
= 2ab eax + by f (ax – by)
x
y
a
1.8
b
z
z
a
= 2a b z
x
y
...(1)
...(2)
...(3)
Proved.
PARTIAL DERIVATIVES OF HIGHER ORDERS
z
z
and
being the functions of x and y can further be differentiated
y
x
partially with respect to x and y.
Symbolically
Let z = f (x, y) then
z
2z
=
x x
x 2
z
2 z
=
y x
y x
z
2 z
=
x y
x y
or
or
or
2 f
x 2
2 f
y x
2 f
x y
or
fxx
or
fyx
or
fxy
2 z
2 z
=
yx
xy
Example 11. Prove that y = f (x + at) + g(x – at) satisfies
Note.
2
2 y
a
=
x 2
t 2
where f and g are assumed to be at least twice differentiable and a is any constant.
(U.P., I Sem; Jan 2011, A.M.I.E., Summer 2000)
Solution.
y = f (x + at) + g(x – at)
...(1)
Differentiating (1) w.r.t. ‘x’ partially, we get
y
= f (x + at) + g(x – at)
x
2
y
= f (x + at) + g(x – at)
x 2
Differentiating (1) w.r.t. ‘t’ partially, we get
y
= f (x + at).a + g(x – at) (– a)
t
2 y
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Partial Differentiation
9
2 y
t 2
= a2f (x + at) + g(x – at) a2
2
= a2 [f (x + at) + g(x – at)] = a
2 y
Hence
t 2
2
= a
2 y
x 2
2 y
Proved.
dx 2
2 z
x2 y2
2
1 y
2
1 x
2
.
Example 12. If z = x tan y tan , prove that
y x x y 2
x
y
y
x
Solution.
z = x 2 tan 1 y 2 tan 1
(U.P., I Semester Comp. 2002)
x
y
z
1 y
1 1
y
2 y2
= 2 x tan 1 x 2
2
x
x
y x
x2 y
1 2
1 2
x
y
x2 y
y3
1 y
= 2 x tan 2
x x y 2 x2 y 2
( x2 y 2 )
y
1 y
= 2 x tan y 2
= 2 x tan 1 y
2
x
x y
x
2 z
= 2 x.
y x
x2
x2 y2
1
.
1
2
1
y2 x
x2 y 2
x2 y 2
1 2
x
3u
xyz
Example 13. If u = e , find the value of
.
x y z
Solution.
u = exyz
1
Proved.
(A.M.I.E. Winter 2000)
u
= exyz (x y)
z
2u
= exyz (x) + exyz (x z) (x y) = exyz (x + x2y z)
yz
3u
= exyz (1 + 2x y z) + exyz (y z).(x + x2y z)
xyz
= exyz [1 + 2 x y z + x y z + x2y2z2]
= exyz [1 + 3 x y z + x2y2z2]
Ans.
m
Example 14. If v = ( x 2 y 2 z 2 ) 2 , then find the value of m (m 0) which will make
2v
x 2
2v
y 2
2v
z 2
= 0.
m
Solution. We have,
2
2
2
v = (x y z ) 2
m
m
1
1
v
m 2
( x y 2 z 2 ) 2 (2 x) = mx ( x2 y 2 z 2 ) 2
=
x
2
2v
x 2
m
m
2
1
m
2
2
2
2
2
2
= m 1 x( x y z ) 2 (2 x ) m( x y z ) 2
2
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Partial Differentiation
10
m
2 2
2
2
= m(m 2) x ( x y z ) 2
m
= m( x 2 y 2 z 2 ) 2
m
2v
Similarly,
y
2
2
2
= m( x y z ) 2
2
m
2v
2
2
2
= m( x y z ) 2
2
2
2
2
z
On adding (1), (2) and (3), we get
2v
x
2
2v
y
2
2v
z
2
m
2
2
2
= m( x y z ) 2
2
m
0 = m (x2 y 2 z2 ) 2
2
...(1)
.[(m – 2) y2 + x2 + y2 + z2]
...(2)
.[(m – 2)z2 + x2 + y2 + z2]
...(3)
[(m – 2) (x2 + y2 + z2) + 3(x2 + y2 + z2)]
1
1
m = 0, – 1
1
2
Example 15. If u = (1 2 xy y ) 2 , prove that
2
Solution. We have, u = (1 2 xy y )
u
1
= (1 2 xy
x
2
(1 x 2 )
2v 2v 2v
2 2 2 0
y
z
x
[m – 2 + 3]
m
0 = m (m + 1)
m = –1
1
[(m 2) x x2 y 2 z 2 ]
0 = m(m + 1) ( x 2 y 2 z 2 ) 2
Hence,
m
m( x y 2 z 2 ) 2
(m 0)
Ans.
x
2 u
2 u
y
= 0.
(1 x )
x y
y
1
2
...(1)
3
2 2
y )
y
( 2 y )
3
(1 2 xy y 2 ) 2
(1 x 2 ) y
u
=
x
(1 2 xy
3
2 2
y )
(1 2 xy
3
2 2
y )
2 u
(1 x ) =
x
x
1
3
2 2
(2 xy ) (1 x ) y (1 2 xy y ) (2 y )
2
(1 2 xy y 2 )3
2
1
Cancelling (1 2 xy y 2 ) 2 from numerator and denominator, we have
=
(1 2 xy y 2 ) (2 xy ) 3(1 x 2 ) y 2
5
=
2 xy 4 x 2 y 2 2 xy 3 3 y 2 3 x 2 y 2
5
(1 2 xy y 2 ) 2
=
(1 2 xy y 2 ) 2
x 2 y 2 2 xy 3 2 xy 3 y 2
...(2)
5
2 2
y )
(1 2 xy
Differentiating (1) partially w.r.t. y, we get
3
1
u
2
= (1 2 xy y ) 2 (2 x 2 y )
2
y
xy
(1 2 xy
3
2 2
y )
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Partial Differentiation
y2
u
=
y
11
xy 2 y 3
3
(1 2 xy y 2 ) 2
3
1
3
(1 2 xy y 2 ) 2 (2 xy 3 y 2 ) ( xy 2 y 3 ) (1 2 xy y 2 ) 2 (2 x 2 y )
2
2 u
y
=
2 3
y y
(1 2 xy y )
1
Dividing numerator and denominator by (1 2 xy y 2 ) 2 , we get
2 u
(1 2 xy y 2 )(2 xy 3 y 2 ) ( xy 2 y 3 ) 3 ( x y )
y
=
5
y y
(1 2 xy y 2 ) 2
(2 xy 4 x 2 y 2 2 xy 3 3 y 2 6 xy 3 3 y 4 ) 3x 2 y 2 3xy 3 3xy 3 3 y 4
=
5
(1 2 xy y 2 ) 2
=
x 2 y 2 2 xy 3 2 xy 3 y 2
...(3)
5
y2 )2
(1 2 xy
On adding (2) and (3), we get
2 2
3
2
u 2 u
x 2 y 2 2 xy 3 2 xy 3 y 2 x y 2 xy 2 xy 3 y
(1 x 2 )
y
=
5
=0
5
x
x y y
2 2
2 2
(1
2
xy
y
)
(1 2 xy y )
Proved.
1
Example 16. Prove that if f (x, y) =
y
fxy (x, y) = fyx (x, y).
1
e
( x a )2
4y
then
( x a )2
4y
e
y
Differentiating f (x, y) partially w.r.t. x, we get
Solution.
f (x, y) =
...(1)
( x a)2
1 [2 ( x a)] 4 y
( x a )
.
e
e
fx (x, y) =
4y
y
2 y 3/ 2
Differentiating again partially w.r.t. ‘y’ by product rule, we have
fyx (x, y) =
3( x a)
4 y 5/ 2
( x a)
.e
( x a)2
4y
( x a)2
4y
.e
8y7 / 2
Differentiating (1) partially w.r.t. ‘y’, we have
=
fy (x, y) =
1
2 y 3/ 2
.e
( x a)2
4y
( x a)3
8y7 / 2
e
( x a )2
4y
.[6 y ( x a)2 ]
( x a )2
4 y5 / 2
e
( x a )2
4y
...(2)
( x a )2
4y
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Partial Differentiation
12
Differentiating again partially w.r.t. ‘x’, we have
fxy(x, y) =
=
( x a)
4 y 5/ 2
( x a)
8y7 / 2
( x a)
e
e
( x a)2
4y
( x a )2
4y
( x a )2
4y
( x a)
2 y 5/ 2
e
( x a)2
4y
( x a )3
8 y7 / 2
e
[2 y 4 y ( x a)2 ]
e
[6 y ( x a )2 ]
8y7 / 2
From (2) and (3), we have fxy (x, y) = fyx (x, y)
=
( x a )2
4y
...(3)
Proved.
3u
3u
y
.
Example 17. If u = x , show that
x 2 y xyx
Solution.
u = xy
log u = log xy = y log x
Differentiating partially, we have
y
1 u
,
.
=
x
u x
y
u
= u ,
x
x
and
1 u
.
= log x
u y
u
= u log x
y
2u
1
u u y u
u uy.log x
= . u y . .
=
yx
x
y x x y
x
x
u
u
x. .log x u log
u 1 u
u
x
x
= 2 . y.
x x
xyx
x
x2
u
1 u y log x u uy uy log x
= 2
x x
x
x x 2
x
x2
2
u uy uy log x uy uy log x
2
= 2 2
x
x
x2
x
x2
u
2uy uy 2 log x uy log x
= 2 2
x
x
x2
x2
u
= u log x
y
3
2u
u
u
log x.
=
x
x
xy
=
u
u log x
As
y
x
u
uy
log x.
x
x
...(1)
u uy
x x
u
u
x. log x. u log x (1)
1 u
x
x
= 2 . y.
2
x x
x
x2
x y
u
uy uy y log x u uy log x
= 2 2 2
x
x
x
x
x
x2
u
2uy y log x uy uy log x
= 2 2
x
x
x
x
x2
3u
u
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Partial Differentiation
13
=
u
x2
From (1) and (2), we get
2uy
x2
3u
x 2 y
uy 2 log x
x2
=
uy log x
...(2)
x2
3u
xyx
Proved.
Example 18. If u = log (x3 + y3 + z3 – 3xyz), show that
2
9
u =
( x y z)2
x y z
3
3
Solution.
u = log (x + y + z3 – 3xyz)
Differentiating (1) partially w.r.t. ‘x’, we get
Similarly,
(U.P. I Semester, winter 2003)
...(1)
u
3x 2 3 yz
= 3
x
x y 3 z 3 3xyz
...(2)
u
3 y 2 3zx
=
y
x3 y 3 z 3 3xyz
...(3)
3z 2 3xy
u
= 3
z
x y 3 z 3 3xyz
On adding (2), (3) and (4), we get
u u u
3( x 2 y 2 z 2 xy yz zx )
=
x y z
x 3 y 3 z 3 3 xyz
=
3( x 2 y 2 z 2 xy yz zx)
2
2
2
( x y z ) ( x y z xy yz zx )
...(4)
=
3
x yz
3
u =
x yz
x y z
2
3
u =
x y z x y z
x y z
3
3
3
=
x x y z y x y z z x y z
= – 3(x + y + z)– 2 – 3(x + y + z)– 2 – 3 (x + y + z)– 2
=
9
Proved.
( x y z )2
1.9 WHICH VARIABLE IS TO BE TREATED AS CONSTANT
Let
x = r cos ,
r
To find
, we need a relation between r and x.
x
r = x sec
Differentiating (1) w.r.t. ‘x’ keeping as constant
r
= sec
x
2
Here, we have
r = x2 + y2
Differentiating (3) w.r.t. ‘x’ keeping y as constant.
y = r sin
...(1)
...(2)
...(3)
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Partial Differentiation
14
2r
r
= 2x
x
r x
= cos
x r
or
...(4)
r
r
r
= sec and from (4),
= cos . These two values of
make confusion.
x
x
x
To avoid the confusion we use the following notations.
r
Notation. (i) x means the partial derivative of r with respect to x, keeping as constant.
From (2),
r
From (3), = sec
x
r
(ii) means the partial derivative of r with respect to x keeping y as constant.
x y
r
From (4), = cos
x y
(iii) When no indication is given regarding the variables to be treated as constant
means ,
means .
x
x
y y
y
x
means ,
means .
r
r
r
Example 19. If x = r cos , y = r sin , find
x
r
y
(i)
(ii)
(iii)
r
x y
r
Solution.
constant.
(iv)
y x
x
(i)
means the partial derivative of x with respect to r, keeping as
r
x
= cos
r
x = r cos
y
(ii) means the partial derivative of y with respect to , treating r as constant.
r
y
y = r sin
= r cos
r
r
(iii) means the partial derivative of r with respect to x, treating y as constant. We have
x y
to express r as a function of x and y.
r =
1
r
= 2
x
y
x2 y 2
(From the given equations)
1
2
x y
2
.2 x
x
2
x y2
(iv) Before finding we have to express in terms of x and y.
y x
1 y
= tan
(From the given equations)
x
1
1
x
. 2
Ans.
=
2
y x x y2
y x
1 2
x
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Partial Differentiation
15
EXERCISE 1.3
1. If z3 – 3yz – 3x = 0, show that z
2. If z(z2 + 3x) + 3y = 0, prove that
2
z z 2 z z 2 z
;z
2
x y xy x y
2z
2
2 z
x
y 2
x
y
2
3. If z = log (e + e ), show that rt – s = 0.
2 z ( x 1)
( z 2 x)3
.
1
1
4. If f (x, y) = x3y – xy3, find
f
f
x y x 1
Ans.
13
22
y 2
5. If = t n e
r2
4t ,
find what value of n will make
1
r 2 r
2
r
.
r t
6. Show that the function u = arc tan (y/x) satisfies the Laplace equation
Ans. n =
2u
x 2
2u
y 2
3
2
0.
z
z xz
7. If z = y f (x2 – y2) show that y x x y y .
8. Show that
2z
x 2
2
2 z 2 z
= 0, where z = x . f (x + y) + y . g(x + y).
xy y 2
y
2 z 2u
= 0.
. Show that
x
x 2 y 2
1
9. If u = log (x2 + y2) + tan
10. If u (x, y, z) =
1
2
2
2
, find the value of
x y z
11. If x = er cos cos (r sin ) and y = er
x
1 y y
1 x
,
Prove that
=
r
r r
r
2
2u
x
y
sin (r sin )
2
2u
.
z 2
Ans.
2
( x 2 y 2 z 2 )2
1 x 1 2 x
= 0
r r r 2
r
12. If x = r cos , y = r sin , prove that
Hence deduce that
(i)
r x
1 x
, r. .
x r
x r
2x
cos
2u
2
(ii)
2
x 2
13. If v = (x2 – y2). f (xy), prove that
2v
x
2
2
y 2
= 0
2v
y 2
(c)
2r
x 2
2r
y 2
2
2
r
1 r
r x
y
= (x4 – y4) f (xy)
u v
u v x 2 y 2
14. If ux + vy = 0 and x y = 1, show that
x y y 2 x 2
15. If z = xy + yx, verify that
2 z
2z
x y
y x
16. If u = f (ax2 + 2h x y + by2) and v = (ax2 + 2hx y + by2) show that
17. If u = rm, where r2 = x2 + y2 + z2, find the value of
18. If x =
r
(e e ),
2
y
r
(e e )
2
2u
x 2
2u
2u
y2 z 2
x r
prove that
r x
.
v v
u u .
y x x y
Ans. m(m + 1)r m – 2
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Partial Differentiation
16
u
2u
a2 2 , show that ag =
t
x
u
u
sin 2 y
= log (tan x + tan y), prove that, sin 2 x
= 2.
x
y
2
x2u
2 u
1
x
=
[(x – y) + (x + y)], then show that x x
y 2
x
xz
= ex y z f , prove that
y
u
u
u
u
z
x y
= 2 x y z u,
(ii) y
= 2xyzu
y
z
x
y
19. If u (x, t) = a e–
20. If u
21. If u
22. If u
(i)
gx
sin (nt – gx), satisfies the equation
2u
2u
y
zx
zy
23. If u = f (x, y), x = r cos , y = r sin , then show that
Also deduce that x
2
u u
x y
2
2
1
u
= 2
r
r
u
n
.
2
(A.M.I.E., Summer 2001)
2
1.10 HOMOGENEOUS FUNCTION
A function f (x, y) is said to be homogeneous function in which the power of each term is the
same.
A function f (x, y) is a homogeneous function of order n, if the degree of each of its terms
in x and y is equal to n. Thus
a0 xn + a1xn – 1y + a2xn – 2 y2 + ... + an – 1 xyn – 1 + an yn
...(1)
is a homogeneous function of order n.
The polynomial function (1) which can be written as
2
n 1
n
y
y
y
y
y
n
x n a0 a1 a2 ... an 1
an = x
x
x
x
x
x
...(2)
2
3
y
y
y
3
x
1
3
5
(i) The function
is a homogeneous function of order 3.
x
x
x
y
y
x 1
1
x
x y
x
3/ 2
x
.
(ii)
is a homogeneous function of order – 3/2.
2
2
x2 y 2
y
y
2
1
x 1
x
x
x y
1
(iii) sin
is not a homogeneous function as it cannot be written in the form of
x2 y 2
y
x n f so that its degree may be pronounced. It is a function of homogeneous
x
expression.
1.11 EULER’S THEOREM ON HOMOGENEOUS FUNCTION
(U.P. I Semester, Dec. 2006)
Statement. If z is a homogeneous function of x, y of order n, then
x.
z
z
y.
= nz
x
y
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Partial Differentiation
17
Proof. Since z is a homogeneous function of x, y of order n.
z can be written in the form
y
n
z = x .f
x
Differentiating (1) partially w.r.t. ‘x’, we have
y
y y
n
x . f 2
x
x x
z
y
n 1 y
n2
y. f
= nx . f x
x
x
x
Multiplying both sides by x, we have
z
y
y
n
n 1
x = n x . f x y. f
x
x
x
Differentiating (1) partially w.r.t. ‘y’, we have
z
y 1
n
= x f .
y
x x
Multiplying both sides by y, we get
z
y
n 1
y.
= x y. f
y
x
Adding (2) and (3), we have
z
z
n y
x. y.
= n.x f
x
y
x
z
z
x. y.
= nz
x
y
...(1)
z
n 1
= nx . f
x
...(2)
...(3)
Proved.
Note. If u is a homogeneous function of x, y, z of degree n, then
u
u
u
y
z
= nu
x
y
z
I. Deduction from Euler’s theorem
If z is a homogeneous function of x, y of degree n and z = f (u), then
u
u
f (u )
x
y
n
=
(Nagpur University, Winter 2003)
x
y
f (u )
Proof. Since z is a homogeneous function of x, y of degree n, we have, by Euler’s theorem,
z
z
x y
= nz
...(1)
x
y
Now z = f (u), given
z
u
z
u
= f (u )
and y = f (u ) y
x
x
Substituting in (1), we get
u
u
x f (u )
y f (u )
= nf (u)
x
y
f (u )
u
u
x
y
= n f (u )
x
y
x
Note. If v = f (u) where v is a homogeneous function in x, y, z of degree n, then
x
u
u
u
y
z
x
y
z
=
nf (u)
f (u )
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Partial Differentiation
18
x1/3 y1/3
Example 20. Verify Euler’s theorem for z =
Solution. Here, we have
x1/3 1
=
x1/ 2 1
x1/ 2 y1/ 2
.
(U.P. Ist Semester, Dec. 2009)
1/ 3
y
x
1
6 y
x
z = 1/ 2
1/ 2
1/ 2
x
x y
y
x
Thus z is homogeneous function of degree 1 .
6
z
z
1
By Euler’s theorem x y
= z.
x
y
6
Differentiating (1) w.r.t. ‘x’, we get
x1/ 3 y1/ 3
z
=
x
1
2
1
1
1
1
1
1
x2 y2 x 3 x3 y3 x 2
3
2
1
1
x2 y2
5
x
z
=
x
z
=
y
1
1
1 6 1 3 2 1
x x y x
3
3
2
5
6
2
1
...(1)
...(2)
1
2
1
1
1
1
1 2 3
x y
2
...(3)
2
1
1
x2 y2
1
2 1
1
1
1
1
1
x2 y2 y 3 x3 y3 y 2
3
2
1
1
1
1
x2 y2
2
5
1
1
1
1 6 1 3 2 1 6 1 2 3
x x y x
x y
3
2
2
= 3
1 2
1
x2 y2
1
=
1 2
x y
3
2
3
1
1
1
1 6 1 3 2 1
y x y y
3
2
2
1
1
x2 y2
1
6
2
5
1 2 3 1 6 1 3 2 1 6
x y y x y y
z
3
2
2
y
= 3
2
y
1
1
x2 y2
Adding (3) and (4), we get
5
1 1
5
1 1
1 1
5
1 1
5
1 6 1 3 2 1 6 1 2 3 1 2 3 1 6 1 3 2 1 6
x
x
y
x
x
y
x
y
y
x
y
y
z
z
3
3
2
2
3
3
2
2
x y
=
2
x
y
1
1
x2 y2
1 1
1
1 1
1
5
5
1 1
1 1
1
1
x 2 x3 y 3 y 2 x 3 y 3
x 6 y 6 x3 y 2 x 2 y 3
6
6
=
=
2
2
1
1
1
1
x2 y2
x2 y2
...(4)
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Partial Differentiation
=
19
1
1 1
1
1
x2 y2 x3 y3
6
2
1
1
1 x3 y3
=
1
6 1
2
x y2
1
1
x2 y2
1
z
z
x y
= z
6
x
y
From (2) and (5), Euler’s theorem is verified.
x y
1
, show that
Example 21. If u = cos
x y
u
u 1
x
y
cot u 0.
x
y 2
xy
1
Solution. Here, we have, u = cos
x y
u is not a homogeneous function but if z = cos u, then
y
y
x 1
1 1
x y
x
x
x2
u = cos–1 z =
=
x y
y
y
x 1
1
x
x
1
z is a homogeneous function in x, y of degree .
2
z
z
1
By Euler’s theorem, we have x x y y = z
2
z u
z u
1
x
y
= z
u x
u y
2
u
u
x
( sin u ) y
( sin u ) = 1 cos u
x
y
2
u
u
u
1
x
y
= cot u. x x y
x
y
2
1 x 2 y 3 z
, show that
Example 22. If u = sin 8
8
8
x y z
u
u
u
x
y
z
3 tan u = 0.
x
y
z
1 x 2 y 3 z
Solution. We have, u = sin 8
8
8
x y z
...(5)
Verified.
(U.P. Ist Semester, Dec. 2009)
1
y
x 2 .
x
u 1
cot u = 0
y 2
Proved.
(U.P. I Sem., Winter 2003)
Here, u is not a homogeneous function but if v = sin u =
x 2 y 3z
x8 y 8 z 8
then
v is a homogeneous function in x, y, z of degree – 3.
v
v
v
By Euler’s Theorem
x y
z
= nv
x
y
z
x
v u
v u
v u
y
z
= –3 v
u x
u y
u z
...(1)
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Partial Differentiation
20
Putting the value of
v
in (1), we get
u
u
u
u
= –3 sin u
x cos u
y cos u
z cos u
x
y
z
u
u
u
sin u
y
z
= 3
= –3 tan u
x
y
z
cos u
u
u
u
x
y
z
3 tan u = 0
Proved.
x
y
z
x
u
u
x4 y4
Example 23. If u = loge
, show that x x y y = 3.
x y
(Nagpur University, Summer 2008, Uttarakhand, I Semester 2008)
x4 y 4
u = loge
x y
Here, u is not a homogeneous function but if
Solution. We have,
y 4
x 4 1
x4 y 4
x x 3 y
z = eu =
x y
y
x
x 1
x
Then z is a homogeneous function of degree 3.
By Euler’s Deduction formula I
u
u
f (u )
eu
x
y
n
3
3
=
x
y
f (u)
eu
Example 24. If f (x, y) =
x
Solution.
1
x
2
Proved.
1 log x log y
, prove that
xy
x2 y 2
f
f
y
2 f = 0.
x
y
1
1 log x log y
f (x, y) = 2
xy
x
x2 y2
(A.M.I.E. Summer 2004)
y
0
log
1 y
1 1
1
x
= 2 2
x x
x y x 2 y 2
1
x
x
f (x, y) is a homogeneous function of degree – 2.
By Euler’s Theorem
f
f
f
f
x
y
y
2f = 0
= –2.f x
Proved.
x
y
x
y
Example 25. If z be a homogeneous function of degree n, show that
2 z
2 z
z
2 z
2 z
z
y. 2 (n 1)
(i) x. 2 y.
(n 1)
(ii) x.
xy
y
y
xy
x
x
(iii) x 2 .
2 z
x 2
2 xy.
2 z
2 z
y 2 . 2 n(n 1) z.
xy
y
(Uttarakhand Ist Semester, Dec. 2006)
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Partial Differentiation
21
Solution. By Euler’s Theorem x.
z
z
y
=nz
x
y
...(1)
Differentiating (1), partially w.r.t. ‘x’, we get
z
z
2 z
2 z
x. 2 y
= n
x
x
x
y
x
2 z
z
= (n 1)
2
xy
x
x
Differentiating (1), partially w.r.t. ‘y’, we have
x.
x.
2 z
y
Proved (i)
...(2)
Proved (ii)
...(3)
z
2 z z
2 z
y 2 = n
y
yx y
y
x 2 z y 2 z
z
= (n 1)
2
xy
y
y
Multiplying (2) by x, we have
2 z
z
= ( n 1) x
2
xy
x
x
Multiplying (3) by y, we have
z
2 z
2 z
xy.
y 2 . 2 = (n 1) y
y
yx
y
Adding (4) and (5), we get
x2 .
2 z
...(4)
...(5)
2
z
z
2 z
2 z
y
.
= (n 1) x y
2
2
x
y
xy
x
y
= (n – 1) n z
[From (1)]
= n(n – 1) z
Proved (iii)
Example 26. If f (x, y) and (x, y) are homogeneous functions of x, y of degree p and q
respectively and u = f (x, y) + (x, y), show that
x2 .
2 z
xy.
2 xy.
2
2 2u
1
2u
q 1 u
u
2 u
x
2
xy
y
x
y
f (x, y) = P ( P q)
2
2
xy
y
y P ( P q) x
x
(A.M.I.E.T.E. Winter 2000)
Solution. Since f and are homogeneous functions of degree p and q respectively, we have
f
f
x
y
= P.f
...(1)
x
y
x
y
= q.
...(2)
x
y
On adding (1) and (2), we get
u f ( x, y ) ( x, y )
f
u f
f
x y = P f + q
x
x
y
y
x x x
u f
u
u
x
y
i.e.,
=
P
f
+
q
...(3)
y y y
x
y
Also
x2
2 f
x 2
2 xy
2 f
2 f
y2 2
xy
y
= P (P – 1) f
...(4)
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Partial Differentiation
22
2
2
2
y
And
= q (q – 1)
xy
x 2
y 2
On adding (4) and (5), we obtain
x2
2
2 xy
2 f 2
2 f
2 f 2
2
x 2 2 2 2 xy
y2 2 2
x
xy xy
y
x
y
= P(P – 1) f + q (q – 1)
u f
2
u
x 2
2u
2 f
x 2
2 f
2
x 2
2
2
2u
2 u
2
y
= P(p – 1) f + q (q – 1)
y 2
y 2
y
xy
x 2
y 2
Dividing by P (P – q), we get
2
2 2u
1
2u
1
2 u
2
xy
y
[ P ( P 1) f q(q 1)]
P ( P q) x
2
2 =
x
y
P
(
P
q)
y
x
q 1 u
u
y from both sides, we get
x
Subtracting
P ( P q) x
y
x2
2u
...(5)
2 xy
2
2 2u
1
2u
(q 1)
2 u
2
xy
y
x
2
P ( P q) x 2
xy
P
( P q)
y
u
u
x x y y
1
(q 1) u
u
[ P ( P 1) f q ( q 1) ]
y
=
x
P ( P q)
P ( P q ) x
y
1
[ P ( P 1) f q(q 1) (q 1) [ Pf q]]
=
P ( P q)
1
[ P 2 P Pq P ) f (q 2 q q 2 q)]
=
P ( P q)
1
P( P q )
2
= P ( P q) [( P Pq ) f ] P ( P q) f = f (x, y)
[From (3)]
Proved.
y
n
n x
Example 27. If z = x f y then prove that
x
y
2
2
2
z
z
z
z
z
2
x 2 2 2 xy
y2 2 x y
(Nagpur University, Summer 2003)
xy
x
y = n z.
x
y
y
n
n x
z = x f y
x
y
z = u+v
y
n x
n
where,
u = x f and v = y
x
y
Since u is a homogeneous function of x, y of degree n.
u
u
x
y
= nu
x
y
Solution.
2
2u
2 u
y
and
= n(n – 1)u
xy
x 2
y 2
As v is a homogeneous function of x, y of degree –n.
v
v
x y
= –nv
x
y
x2
2u
2 xy
...(1)
...(2)
...(3)
...(4)
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Partial Differentiation
x2
and
2v
x 2
23
2 xy
2v
2v
y 2 2 = –n (– n – 1)v = n (n + 1)v
xy
y
On adding (2) and (4), we get
u v
u v
x
y
= nu – nv
x x
y y
z
z
x y
= nu – nv
x
y
On adding (3) and (5), we get
...(5)
zuv
z u v
x x x
z u v
y y y
...(6) [From (1)]
2u 2 v
2u
2u 2v
2v
x 2 2 2 2 xy
y 2 2 2 = n(n – 1) u + n (n + 1)v
x
xy xy
y
x
y
2 z 2u 2v
x 2 x 2 x 2
2
2
2
z
z
z
x 2 2 2 xy
y 2 2 = n(n – 1)u + n (n + 1)v
...(7) [From (1)]
xy
x
y
On adding (6) and (7), we have
2
2 z
z
z
2 z
y
x y
2
2
x
y
x
y = n(n – 1)u + n (n + 1) v + nu – nv
x
y
= nu (n – 1 + 1) + nv (n + 1 – 1)
= n2u + n2v = n2 (u + v) = n2z
II. Deduction: Prove that
x2
2 z
2 xy
x2
2u
x 2
2 xy
Proved.
2u
2u
y 2 2 = g(u) [g(u) – 1] (Nagpur University, Winter 2003)
xy
y
f (u )
g(u) = n f (u )
Proof. By Euler’s deduction formula I
u
u
f (u )
x
y
n.
=
x
y
f (u )
where,
u
u
y
= g(u)
x
y
Differentiating (1) partially w.r.t. ‘x’, we have
2 u u
2u
u
x 2 .1 y
g (u )
=
x
xy
x
x
x
2u
u
= [ g (u ) 1]
xy
x
x 2
Similarly, on differentiating (1) partially w.r.t. ‘y’, we have
x
2u
2u
u
[ g (u ) 1]
=
2
y x
y
y
Multiplying (2) by x, (3) by y and adding, we get
y
x2
2u
x 2
2 xy
2u
y.
x.
2u
2u
u
u
y 2 2 = [ g (u ) 1] x
y
xy
x
y
y
= [g (u) – 1]g(u)
= g(u) [g(u) – 1]
f (u )
g (u )
Given n
f (u )
...(1)
...(2)
...(3)
[From (1)]
Proved.
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Partial Differentiation
24
3
3
1 x y
, prove that
Example 28. If u = tan
x y
u
u
(i) x. y.
= sin 2u
(A.M.I.E., Winter 2001)
x
y
2
2u
2u
2 u
y 2 2 = 2 cos 3u sin u. (M.U. 2009; Nagpur University, 2002)
(ii) x . 2 2 xy
x y
x
y
Solution. Here u is not a homogeneous function. We however write
3
y 3
3
y
x
1
1
x3 y 3
x
x
y
x 2 . x 2
z = tan u =
x y
y
y
x
1
x 1
x
x
so that z is a homogeneous function of x, y of order 2.
(i) By Euler’s Theorem
[Here f (u) = tan u]
x
u
u
n f (u )
y.
= f (u)
x
y
=
2 tan u
2
sec u
...(1)
2 sin u cos 2 u
= 2 sin u cos u = sin 2u
cos u
(ii) By deduction II
x2 .
2u
x 2
2 xy
Here
x2 .
2u
x 2
2 xy
2u
2u
y 2 2 = g(u)[g(u) – 1]
xy
y
sin 2u = g(u)
2u
2u
y 2 2 = sin 2u (2 cos 2u – 1) = 2 sin 2u cos 2u – sin 2u
xy
y
= sin 4u – sin 2u = 2 cos 3u sin u
Proved.
xy
1
Example 29. If u = sin
x y
2
2u
sin u cos 2u
2 u
y
.
2
2
xy
x
y
4 cos3 u
x y
1
Solution. We have,
u = sin
x y
y
x 1
x y
x
x1/ 2 ( x)
Let
z = sin u =
x y
y
x 1
x
z = f (u) = sin u
1
z is a homogeneous function of degree .
2
By Euler’s deduction I
u
u
f (u )
u
u
1 sin u
x
y
y
= n
x
=
x
y
f (u )
x
y
2 cos u
u
u
1
x
y
tan u
=
x
y
2
Prove that x 2
2u
2 xy
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Partial Differentiation
25
Let
1
tan u
2
g(u) =
By Euler’s deduction II
x2
2u
2 xy
x 2
2u
2u
1
1
y 2 2 = g(u) [g(u) – 1] = tan u sec 2 u 1
xy
y
2
2
1 sin u 1
sin u cos 2u
1 sin u
2
(1 2 cos 2 u )
= 4 cos u
cos 2 u
4 cos3 u
4 cos3 u
Proved.
EXERCISE 1.4
1. Verify Euler’s theorem in case
(i) f (x, y) = ax2 + 2hxy + by2
2. If v =
x3 y3
3
x y
3. If u = log
3
, show that x.
x3 y3
2
x y
2
(ii) u = ( x
y ) ( xn y n )
v
v
y. 3v .
x
y
, prove that x.
u
u
y. 1 .
x
y
z
z 3
4. If z = ( x 2 y 2 ) / ( x y), prove that x y. z.
x
x 2
y
f
f
5. If f (x, y) = x4y2 sin–1
, then find the value of x y .
x
x
y
(A.M.I.E.T.E., Winter 2001) Ans. 6 f (x, y)
u
y u
1 x y
.
6. If u = sin
, show that
x
x y
x y
3
3
u
u
1 x y
7. If u = sec
, show that x. y.
= 2 cot u, then evaluate
x
y
x y
2u
2u
y2 2
(A.M.I.E.T.E., Winter 2001) Ans. –2 cot u (2 cosec2 u + 1).
x y
x
y
8. If x = eu tan v, y = eu sec v, find the value of
x2
2u
2
2 xy
u
u v
v
x. y. . x. y. . (A.M.I.E., Summer 2001) Ans. 0
y x
y
x
[Hint: Eliminate u and apply formula I. Again eliminate v and apply the formula]
x1/ 4 y1/ 4
9. If u = sin 1
, prove that
1./6
y1/ 6
x
x2
2u
x
2
2 xy
2u
2u
1
y2 2 =
tan u [tan 2 u 11].
x y
144
y
2
10. Find the value of x
2u
x
2
2 xy
2u
2u
y 2 2 if u = sin–1 (x3 + y3)2/5.
x y
y
Ans.
2
1 y
, find
11. If u = tan
x
u
u
(i) x. x y. y , and
1
12. If u = tan
x3 y 3
x
y
2
(ii) x .
2u
x 2
2 xy.
, find the value of x 2
2u
2u
y2. 2
xy
y
2u
x 2
2 xy
Ans. (i)
2u
2u
y2 2
x y
y
5
6
tan u sec2 u 1
6
5
xy 2
x2 y4
(ii)
2 xy 6
( x2 y 4 )2
Ans. –2 sin3 u cos u
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Partial Differentiation
26
y
13. If u = f
x
2
2
2
x 2 y 2 , find the value of x 2 u 2 xy u y 2 u .
x y
x 2
y 2
2
2z
2 z
y
.
Ans. 0
x y
x 2
y 2
2
2
4
15. Verify Euler’s theorem on homogeneous function when f (x, y, z) = 3x yz + 5xy z + 4z
2
14. If z = xy/(x + y), find the value of x
Y
16. If u = x
X
X2
2u
x
2
Y
X
2 XY
2 z
Ans. 0
2 xy
, prove by Euler’s theorem on homogeneous function that
2u
2u
Y 2 2 = 0.
x y
y
17. Given F (u) = V(x, y, z) where V is a homogeneous function of x, y, z of degree n, prove that
x
u
u
u
F (u )
y
z
=n
x
y
z
F (u )
x y z
18. State and prove Euler’s theorem, and verify for u = y z x
19. If u =
x2 y 2 z 2
x2 y 2 z 2
cos
xy yz
x2 y2 z
,
x
2 , show that
(A.M.I.E., Summer 2000)
u
u
u
4 x2 y 2 z 2
y
z
2
x
y
z x y 2 z 2
1.12 TOTAL DIFFERENTIAL
In partial differentiation of a function of two or more variables, only one variable varies. But
in total differentiation, increments are given in all the variables.
1.13 TOTAL DIFFERENTIAL CO-EFFICIENT
Let
z = f (x, y)
...(1)
If x, y be the increments in x and y respectively, let z be the corresponding increment
in z.
Then z + z = f (x + x, y + y)
...(2)
Subtracting (1) from (2), we have
z = f (x + x, y + y) – f (x, y)
...(3)
Adding and subtracting f (x, y + y) on R.H.S. of (3), we have
z = f (x + x, y + y) – f (x, y + y) + f (x, y + y) – f (x, y)
f ( x x, y y ) f ( x, y y )
f ( x, y y ) f ( x, y )
x
y
z =
x
y
On taking limit when x 0 and y 0
f
f
d x
dy
dz =
...(4) [Remember]
x
y
dz is called as the total differential of z.
1.14 CHANGE OF TWO INDEPENDENT VARIABLES x AND y BY ANY OTHER
VARIABLE t.
Differentiation of composite function
If
z = f(x, y)
Where
x = (t)
y = (t)
Here z is composite function of t.
Dividing (4) by dt , we have
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Partial Differentiation
27
z d x z dy
dz
=
x d t y dt
dt
Then
...(5)
[Remember]
dz
is called the total differential co-efficient of z.
dt
1.15 CHANGE IN THE INDEPENDENT VARIABLES x AND y BY OTHER TWO
VARIABLES u AND v.
Let
z = f (x, y)
where
x = (u, v)
y = (u, v)
Then from (5), we obtain
f
z
=
x
u
f
z
=
x
v
and
.
x f y
.
u y u
...(6)
.
x f y
.
v y v
...(7)
Example 30. If u = x3 + y3 where, x = a cos t, y = b sin t, find
du
and verify the result.
dt
Solution. We have, u = x3 + y3
x = a cos t
y = b sin t
dz
u d x u d y
= x dt y dt
dt
=
=
Verification. u =
=
du
=
dt
Results (1) and (2)
(3 x2) (– a sin t) + (3 y2) (b cos t)
–3 a3 cos2 t sin t + 3 b3 sin2 t cos t
x3 + y3
a3 cos3 t + b3 sin3 t
...(1)
– 3a3 cos2t sin t + 3b3 sin2 t cos t
...(2)
are the same.
Verified.
u
u
Example 31. If z = f (x, y) where x = e cos v and y = e sin v, show that
(i)
y
z
z
2u z
.
x
= e
y
u
v
(M.U. 2009; Nagpur Univesity 2002)
2
2
2
2
z
z
z
2u z
e
(ii)
=
(M.U. 2009)
u
v
x
y
Solution. (i) We have,
x = eu cos v,
y = eu sin v
z
z x z y
z u
z
z
z u
e cos v
e sin v x
y
=
=
u
x u y u
x
y
x
y
y
And
z
= xy
u
z
z
=
v
x
z
z
y2
x
y
...(1)
x z y
v y v
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Partial Differentiation
28
=
z u
z
z
z
(eu sin v)
(e cos v) y
x
x
y
x
y
z
z
z
= x y
x2
v
x
y
On adding (1) and (2), we get
z
z
z
2
2 z
y
x
(e2u cos 2 v e 2u sin 2 v )
= (x y )
u
v
y
y
x
2u
2
2
= e (cos v sin v )
z
z
e 2u
y
y
=
z u
z u
(e cos v)
e sin v
x
y
y eu sin v
z
z
z
cos v
sin v
=
u
x
y
On squaring, we get
e u
2
Proved.
x eu cos v
x
x
eu cos v and
eu sin v
u
v
z
z x z y
=
u
x u y u
(ii)
2
y
y
eu sin v and
eu cos v
u
v
2
z
z
z
z z
2
2
e 2u
=
cos v
sin v 2
sin v cos v
u
x
y
x y
z x z y
z
Again
=
x v y v
v
z
z u
=
( eu sin v)
(e cos v)
x
y
z
z
z
e u
sin v
cos v
=
v
x
y
On squaring, we get
2
2
e
...(3)
2
z
z
z
z z
2
2
e 2u
=
sin v
cos v 2
sin v cos v
v
x
y
x y
On adding (3) and (4), we get
2u
...(2)
...(4)
2
2
z 2 z 2
z
z
2
2
2
2
=
(sin v cos v )
(sin v cos v )
u v
x
y
2
z
z
=
x
y
Example 32. If u = u (y – z,
z – x,
2
x – y), prove that
Proved.
u u u
=0
x y z
(Nagpur University, Winter 2002, U.P., I Sem., Winter 2002, A.M.I.E winter 2001)
Solution. Let
r = y – z,
s = z – x,
t=x–y
so that
u = u (r, s, t)
u
u r u s u t
=
x
r x s x t x
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Partial Differentiation
29
=
u
u
u
u u
(0)
(1)
(1)
r
s
t
s t
u r u s u t
u
=
r y s y t y
y
u
u
u
u u
(1)
(0)
(1) =
=
r
s
t
r t
u r u s u t
u
=
r z s z t z
z
u
u
u
u u
(1)
(1)
(0)
=
r
s
t
r s
...(1)
...(2)
...(3)
u u u
Adding (1), (2) and (3), we get x y z = 0
Proved.
y x z x
u
u
2 u
y2
z2
,
Example 33. If u = u
=0
, show that x
x
y
z
x
y
x
z
(U. P. I. Sem., Dec. 2004, Nagpur University, Summer 2000)
y x z x
,
Solution. Here, we have u = u
= u (r, s)
zx
xy
where
r=
yx
,
xy
and
s =
zx
zx
r=
1 1
x y
and
s =
1 1
x z
r
1
= 2
x
x
1
r
= 2
y
y
r
=0
z
and
and
and
s
1
= 2
x
x
s
1
= 2
z
z
s
=0
y
We know that,
u
u r u s
=
x
r x s x
=
u
r
1 u 1 u
1 u 1
2
2 2
s
x
x
x r x2 s
x2
u
u u
=
x
r s
u r u s u 1
u
1 u
u
0 2
=
r y s y r y2 s
y
y r
y2
u
u
=
y
r
u
u r u s u
u 1
1 u
0
2 2
=
z
r z s z r
s z
z s
...(1)
...(2)
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Partial Differentiation
30
u
u
=
...(3)
z
s
On adding (1), (2) and (3), we get
u
u
u
x2
y2
z2
=0
Proved.
x
y
z
Example 34. If (cx – az, cy – bz) = 0 show that ap + bq = c :
z
z
and q
where p =
x
y
Solution. Here, we have
[x and y are independent but
z is dependent on x and y]
(cx – az, cy – bz) = 0
(r, s) = 0
where
r = cx – az,
s = cy – bz
r
z
z
r
a
,
= ca
y
y
x
x
z2
s
z
= b
,
x
x
We know that,
r s
=
r x s x
r
0 =
s
z
cb
y
y
z z
c a
b
r
x s x
0 = c
z
b
a
r x r
s
z
b
c
=
a
x r
s
r
Again
z
a
=
x
ac
a
r
b
r
s
...(1)
r s
=
y
r y s y
z
z
a
c b
r
y s
y
z
z
b
0 = c
=
a
c
s y r
s
y
s
0 =
bc
s
z
b
=
y
a
b
r
s
Adding (1) and (2), we get
ac
bc
r
s
z
z
a
b
=
x
y
a
b
r
s
z
z
a
b
=c
a p b q c
x
y
b
a
s
r
...(2)
Proved.
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Partial Differentiation
31
1.16 CHANGE IN BOTH THE INDEPENDENT AND DEPENDENT VARIABLES,
(POLAR COORDINATES)
Example 35. If w = f (x,y), x = r cos , y = r sin , show that
2
2
2
f
f
w
1 w
2
=
r
x
y
r
Solution. Here,
x = r cos ,
x
= cos
r
2
x
= – r sin
y = r sin
y
= sin
r
y
= r cos
f x f y
w
.
.
=
x r y r
r
w
f
f
. (cos )
. (sin )
=
r
x
y
Now,
...(1)
w
f
f
f x f y
. ( r sin )
. (r cos )
=
.
.
=
x
y
x y
1 w
f
f
=
sin
cos
r
x
y
Squaring (1) and (2) and adding, we obtain
2
w
1 w
2
r
r
2
2
f
f
=
x
y
Example 36. Transform the equation
Solution. We
r2
u
x
2 u
x2
2 u
...(2)
2
Proved.
2 u
= 0 into polar co-ordinates.
x2 y 2
have, x = r cos , y = r sin
y
= x2 + y2,
= tan–1
x
u r u u x u y
u
u sin
cos
=
2
2 =
r x x r r x y
r
r
=
=
=
=
=
u
x x
sin
u sin u
cos
cos
r
r
r
r
u sin u sin
u sin u
cos
cos
cos
r
r
r
r
r
r
2 u sin u sin 2 u
cos cos 2 2
r r
r
r
sin
u
2 u
cos u sin 2 u
cos
sin
r
r
r
r
r 2
2 u sin cos u sin cos 2u
cos 2 2
r
r
r
r2
sin 2 u sin cos 2 u
sin cos u sin 2 2 u
r r
r
r
r2
r 2 2
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Partial Differentiation
32
2
= cos
2u
2
r
u
=
y
2 u
=
y2
=
=
2 sin cos u 2 sin cos 2 u
sin 2 u sin 2 2 u
2
...(1)
2
r
r
r r
r
r
2
u y u
x
u
u cos
u r u
sin
=
r r x2 y2 r
r
r y y
u
cos
u cos u
= sin
sin
y y
r
r
r
r
u cos u
cos
u cos u
sin
sin
sin
+
r
r
r
r
r
r
2 u cos u cos 2 u
sin sin 2 2
r r
r
r
cos
u
2 u
sin u cos 2 u
sin
cos
r
r
r
r
r 2
2 u sin cos u sin cos 2 u
2
= sin 2
r
r
r
r2
2
= sin
2 u
r2
cos 2 u sin cos 2 u
sin cos u cos 2 2 u
r r
r
r
r2
r 2 2
2sin cos u 2 sin cos 2 u
r
r
r2
2
cos u cos 2 2 u
r r
r 2 2
...(2)
Adding (1) and (2), we get
2 u
x
2
2 u
y
(sin 2 cos 2 )
=
2
r2
(sin 2 cos 2 )
1 u
1 2 u
(sin 2 cos 2 ) 2
r r
r 2
1 u 1 2 u
r 2 r r r 2 2
Example 37. If u = f (r) and x = r cos , y = r sin , prove that
=
2 u
x
2
2 u
2 u
2 u
y
2
f " (r )
1
f ' (r ).
r
Ans.
(Nagpur University, Winter 2004)
(A.M.I.E.T.E., Winter 2003, U.P. I Semester, Winter 2005, 2000)
Solution. Here, we have
x = r cos
y = r sin
r2 = x2 + y2 so that
r x
x r
u = f (r)
d f r
u
.
=
dr x
x
Differentiating again w.r.t. x, we get
u
d f x
.
=
x
dr r
r
r.1 x
2
d f r x d f
u
x d f x x d f
.
.
.
.
=
2 =
d r 2 x r d r
d r 2 r r d r
x
r2
2
2
r.1 x .
.
r2
x
r
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Partial Differentiation
d 2 f x2
=
d r2 r2
33
d f r 2 x2 d 2 f x2 d f y 2
.
dr
d r r3
r3
d r2 r2
...(1)
Similarly,
2 u
d f x2
d r r3
y
d r2 r2
On adding (1) and (2), we get
2
d 2 f y2
=
2 u
x2
2 u
y2
=
=
d 2 f x2 y2
d r2
d2 f
dr
2
r2
...(2)
d f x2 y 2
dr
r3
d f 1
1
f (r ) f '(r )
dr r
r
Proved.
Example 38. A function f (x, y) is rewritten in terms of new variables
u = ex cos y,
v = ex sin y
f
f
f
f
f
f
Show that (i)
u
v
and
(ii)
v
u
x
u
v
y
u
v
and hence deduce that
2
2 f 2 f
2 f
2
2 f
(
u
v
)
(iii)
u2
x2 y 2
v 2
u
Solution.
u = excos y, u e x cos y u,
= – ex sin y = – v
y
x
v
v
e x sin y v ,
e x cos y u
x
y
f
f u f v
(i) We know that
=
x
u x v x
v = ex sin y,
(ii)
(iii)
f
f
f
=
u
v
x
u
v
f u f v f
f
f
f
f
= u y v y u .(v ) v u – v . u u . v
y
2 f
x
2
=
x
... (1) Proved.
... (2) Proved.
f
x
f
f
v.
v .
= u .
u .
v u
v
u
f
f
f
f
v
v
= u
u
v
u
u u
v
v u
v
f
f
f
f
= u
u
u
v
v
u
v
v
u u
u v
v u
v v
2 f f 2 f 2 f 2 f f
= u u 2
uv
v u
vv
u u v v u v 2 v
u
2
= u
2 f
u2
u
f
2 f
2 f
2 f
f
u v
uv
v2 2 v
u
vu
uv
v
v
[From (1)]
...(3)
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Partial Differentiation
34
2 f
y
f
f
f
u
u
v
v
y y u
v u
v
=
2
[From (2)]
f
f
f
f
u
u
v
u
v
u
u
v
v u
v
f
f
f
f
= v
v
v
u
u
v
u
u
u
u u v
v
u
v v
2 f 2 f
f
2 f
f 2 f
= v v 2 v u
u
v
uu
u u v v v u u v 2
= v
2
2 f
f
2 f
f
2 f
v
u
v
u
u
uv
v
u v
u
u2
v2
On adding (3) and (4), we obtain
2
= v
2 f
x2
2 f
y2
2
= u
2 f
2 f
u2
u v
v2
2 f
v2
2 f
u2
u2
v2
2
2 V
2 f
Proved.
2 V
(A.M.I.E.T.E., Winter 2007)
x y
(Nagpur University, Summer 2001, Winter 2000, U.P., I Semester, Winter 2001)
Solution. We have,
x + y = 2 e cos
...(1)
x – y = 2 i e sin
...(2)
By adding and subtracting equations (1) and (2), we have
2 x = 2e (cos + i sin ) x = e+i
and
2 y = 2e (cos – i sin ) y = e–i
...(3)
It is clear that V = f (x, y) and x, y are functions of and . Hence V is a composite function
of and .
We want to convert V, , in V, x, y respectively.
From equation (3), we have
x
x
i e i i x
e i x,
=
Show that :
2 V
(u 2 v 2 )
...(4)
2 f
2
2 f
2
2 f
(
u
v
)
u2 v2
u2
v2
Example 39. If x + y = 2 e cos and x – y = 2 ie sin
2
2
= (u v )
2 f
v2
2
4xy
y
e – i y ,
Now,
and
and
y
i
iy
= ie
V
V x V y
V
V
.
.
x
y
=
x y
x
y
2 V
2
=
V
V
V
y
y
x
x
x
y x
y
= x
x
V
V
y
x
y
x
y
y
V
V
y
x
x
y
2 V V
2 V
2 V
2 V V
x
x
y
y
x
y
=
2
x
x y
y2 y
x
x y
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Partial Differentiation
2 V
Again
2
35
2 V
2
= x
x
2
y2
2 V
y
2
V
2 V
V
x
y
x y x
y
2xy
...(4)
V
V
V x V y
V
i x
y
=
x y
y
x
y
= i x
y
x
2V
2
=
= ix
V
V
V
= i x x y y i x x y y
V
V
V
V
.i x
y
.i x
y
iy
x x
y
y x
y
= x
x
V
V
y
x
y
y
y
x
V
V
y
x
y
x
V
2 V
2 V
2 V
2 V V
= x
x
y
y
x
y
x y
x2
y2 y
x
xy
2 V
2
2 V
V
V
2 V
2 V
= x
y
x2
y2
2x y
2
y
xy x
y2
x
Adding (4) and (5), we get
2 V
2
2 V
2
= 4x y
2 V
xy
...(5)
Proved.
EXERCISE 1.5
1. If z = u2 + v2 and u = at2, v = 2 at, find
dz
.
dt
Ans. 4 a2t (t2 + 2)
dz
3
.
dt
1 t2
dw dw
dw
3. If w = f (u, v), where u = x + y and v = x – y, show that
2
dx dy
du
2. If z = sin–1 (x – y), x = 3 t, y = 4 t3; show that
4. If u = xey z, where y =
du
3
a 2 x 2 , z = sin x. Find d x
5. If u = x 2 + y2 + z2 – 2 xyz = 1, show that
[Hint. du =
x2
Ans. eyz 1
3x cot
y
dx
dy
dz
0
2
2
1 x
1 y
1 z2
x
u
u
u
dx
dy
dz 0
x
y
z
= 2 (x – yz) dx + 2 (y – zx) dy + 2 (z – xy) dz = 0
But x2 + y2 + z2 – 2 xyz = 1,
y2 – 2xyz = 1 – x2 – z2
2
2 2
2 2
2
2
y – 2xyz + x z = 1 + x z – x – z
(y – xz)2 = (1 – x2) (1 – z2)]
2
2
6. If z = z (u, v), u = x – 2xy – y and v = y . Show that
z
z
z
( x y)
0 is equivalent to
= 0
x
y
v
7. If u = f (x2 + 2 y z, y2 + 2 z x), prove that
u
du
du
( y 2 z x)
( x 2 y z)
( z 2 xy )
0
x
dy
dz
(x + y)
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Partial Differentiation
36
8. By changing the independent variables x and t to u and v by means of the relationships
u = x – at,
v = x + at
2 y
a2
Show that
x2
2 y
t2
4 a2
2 y
u v
du dx
1 v y
9. If x2 = au + bv, y = au – bv, prove that
.
.
d
x
d
u
y
v 2 y x v u
uv
z
z
z
, show that 2 x
u
v
.
u v
dx
du
dv
y
u u
2
3
2
2
find
,
.
11. If u = x cos , x 3r 2 s, y 4 r 2 s , z 2r 3s
z
r s
10. If z = f (x, y) where x = uv, y =
u
y 4x
y 4x y r
y u
y 6 x s2
y 6 xys
y
6 r cos
sin 2 sin ,
2 cos
sin 2 sin
r
z
z
z
z s
z
z
z
z
z
z
(x, y) where x = eu cos v, y = eu sin v. Prove that
2
2
2
f
f
2u f
e
u
y
v
Ans.
12. If z = f
f
x
2
cos
sin
,y
and z f ( x, y ) , then show that
u
u
13. If x =
2 z
x2
2 z
y2
1
14. If x = z sin
u4
2 z
u2
u3
z
2 z
u4
u
2
y
u u
where x 3 r 2 2 s , y 4 r 2 s 3 , z 2 r 2 3 s 2 , find
,
x
r s
u
6r y z
4z
4 r sin
2
2
2
r
x x y
x y2
15. If z = f (u, v) where u = x cos – y sin , v = x
Ans.
y
,
x
u
2yz
6 s2z
y
6 s sin
2
2
s x x2 y 2
x
x y
sin + y cos , show that
z
z
x
z
y
u
v , being constant.
x
y
u
v
16. Given the transformation x = cosh cos , y = sinh sin
establish the following equation for the function u (function of x, y and also of ):
x
2 u
2 u 2 u
2
2
(sinh
sin
)
2
2 2
y 2
x
17. If z = f (x, y), where x = r cos , y = r sin , prove that
2 u
2
2
2
18. If by substituting u = x2 – y2,
2 f
2
1 2 z 1 z
x 2 y 2 r 2 r 2 2 r r
v = 2 xy,
f (x, y) = (u, v). Show that
z z z
1 z
2
x
y
r
r
and
2 z
2 z
2 z
2 2
4 (u 2 v 2 ) 2 2
u
x
y
v
19. If x = r sin cos , y = r sin sin , z = r cos , v = v(x, y, z), prove that
2
2
2 f
2
2
2
2
2
v
v v v 1 v 1 v
x
y z r r r sin
2
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Partial Differentiation
37
2
2
2
2
20. If v be a potential function such that v = v(r) and r = x + y + z , show that
2 v
x
2
2 v
y
2
2 v
z
2
d2v
d r2
2 dv
r dr
21. Given that w = x + 2y + z2, x = r/s, y = r2 + es, and z = 2r, show that r
w
w
s
12 r 2 2 s e s .
r
s
w
when u = 0, v = 0
v
If w = (x2 + y – 2)4 + (x – y + 2)3 , x = u – 2 v + 1,
and y = 2 u + v – 2
Ans. 99
23. If x = u + v + w , y = v w + w u + u v, z = uvw and F is a function of x, y, z, then show that
F
F
F
F
F
F
u
v
w
x
2y
3z
u
v
w
x
y
z
24. If u = x + a y and v = x + b y, transform the equation
22. Find
2
2 z
x2
5
2 z
2 z
2 z
= 0, find the values of a and b.
3 2 0 into the equation
u v
x y
y
2
2
(A.M.I.E.T.E., Summer 2000) Ans. a 1, b , a , b 1
3
3
1.17 IMPORTANT DEDUCTIONS
Let
z = f (x, y), then
f
f
dx
dy
dz =
x
y
If
z = 0, dz = 0
f
f
dx
dy
0 =
x
y
f
dy
x
= –
dx
f
y
2
d y
We can find
by differentiating (1).
d x2
f
2 f
f
q,
r,
Let
= p,
y
x
x2
y
p
From (1)
= .
x
q
On differentiating again, we obtain
But
dp
p p dy
=
dx
x y dx
dp
f
=
dx
x x y
d y
d x2
f
x
[Remember]
2 f
s,
x y
q
2
f dy
f
.
y dx
x
2
2
y2
t
dp
dq
p
dx
dx
...(2)
q2
dy
.
dx
f
f
f dy f
f x
dp
=
dx
x 2 y x dx x 2 y x f
y
2
2 f
...(1)
2
p qr ps
r s
q
q
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Partial Differentiation
38
dq
q q d y
f f p
=
dx
x y d x x y y y q
2 f
2 f p
t p qst p
s
2
xy y q
q
q
Making substitutions in (2), we obtain
qr ps
qs tp
q
p
2
q 2 r pqs pqs p 2 t
q
q
d y
=
=
q3
q2
d x2
=
d2 y
= –
d x2
q 2 r – 2 pqs + p 2 t
q3
dy
.
dx
3
2
2
3
f (x, y) = x + 3x y + 6xy + y – 1 = 0
f
= 3x2 + 6xy + 6y2
x
Example 40. If x3 + 3 x2y + 6 xy2 + y3 = 1, find
Solution. Let
f
= 3x2 + 12xy + 3y2
y
f
x
3 x 2 6 xy 6 y 2
x 2 2 xy 2 y 2
dy
= f 2
dx
3 x 12 xy 3 y 2
x 2 4 xy y 2
y
Example 41. If y3 – 3 ax2 + x3 = 0, then prove that
d2 y
=
Ans.
2 a 2 x2
d x2
y5
3
Solution. Let
f (x, y) = y – 3ax2 + x3
f
f
6 a x 3 x2 , q
3 y2
p=
x
y
r=
2 y
2 f
x2
=
6 a 6 x,
s
2 f
0,
x y
...(1)
t
2 f
y2
6y
q 2 r 2 p q s p 2t
x2
q3
Putting the values of p, q, r, s and t in (2), we get
2 y
x2
=
=
2 y
2
=
...(2) [Art 1.17]
(3 y 2 )2 ( 6 a 6 x ) 2( 6 a x 3 x 2 ) (3 y 2 ) (0) ( 6 a x 3 x 2 ) 2 (6 y )
(3 y 2 )3
54 y 4 ( a x) 54 ( 2 a x x 2 ) 2 y
27 y 6
2 y 3 ( a x ) 2 (4 a 2 x 2 x 4 4 a x3 )
x
y5
Putting the value of y3 = 3 ax2 – x3 from (1) in (3), we get
2 y
2(3 a x 2 x 3 ) ( a x) 2 (4 a 2 x 2 x 4 4 a x 3 )
=
x2
y5
...(3)
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Partial Differentiation
=
2 y
x2
39
6 a 2 x 2 6 a x 3 2 a x3 2 x 4 8 a 2 x 2 2 x 4 8 a x 3
2
=
2a x
y5
2
Proved.
y5
d2y
dy
.
and
dx
dx 2
3
3
Solution. Let f (x, y) = x + y – 3axy = 0
f
f
3 x 2 3 ay , q
3 y 2 3 ax
p =
x
y
Example 42. If x3 + y3 – 3axy = 0, find
r=
2 f
6 x,
s
2 f
3a,
x y
x2
f
dy
x
3 x 2 3 ay a y x 2
=
2
f
dx
3 y 3 ax y 2 ax
y
d2 y
2
=
t
2 f
y2
6y
q 2 r 2 p q s p 2t
dx
q3
Putting the values of p, q, r, s and t in (1), we get
d2 y
d x2
=
=
d2 y
dx
2
=
...(1) [Art. 1.17]
(3 y 2 3 ax) 2 6 x 2(3x 2 3 ay ) (3 y 2 3 ax) ( 3a) (3 x 2 3 ay )2 (6 y )
(3 y 2 3 ax)3
2 x ( y 2 a x )2 2 a ( x 2 a y ) ( y 2 a x ) 2 y ( x 2 a y )2
( y 2 a x )3
2 a3 x y
( a x y 2 )3
dy
when (cos x) y = (sin y) x
dx
Solution. Given equation can be written as : (cos x)y – (sin y)x = 0
Here
f (x, y) = (cos x)y – (sin y)x = 0
f
= y (cos x)y – 1 (– sin x) – (sin y)x log sin y
x
= – [y sin x (cos x)y – 1 + (sin y)x log sin y]
f
= (cos x)y log cos x – x (sin y)x –1 cos y
y
d f
dy
dx
=
d f
dx
dy
Ans.
Example 43. Find
dy
y sin x (cos x) y 1 (sin y ) x log sin y
=
dx
(cos x ) y log cos x x(sin y ) x 1 cos y
y
In (1), put (cos x) for (sin y)x
[Art. 1.17]
...(1)
y sin x (cos x ) y 1 (cos x) y log sin y
dy
=
x (cos x) y
dx
(cos x ) y log cos x
. cos y
sin y
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Partial Differentiation
40
=
(cos x) y [ y tan x log sin y ]
(cos x ) y [log cos x x cot y ]
y tan x log sin y
log cos x x cot y
f d z
Ans.
f
Example 44. If f (x, y) = 0 and (y, z) = 0, show that y . z . d x x . y .
Solution.
f (x, y) = 0
(y, z) = 0
Differentiating (1) w.r.t. x, we get
...(1)
...(2)
f f dy
.
0 =
x y dx
Differentiating (2) w.r.t. ‘y’, we get
0 =
d z
.
y z d y
f
dy
x
=
f
dx
y
dz
y
=
dy
z
Multiplying (3) and (4), we get
f
x y
dy dz
=
dx dy
f
y z
f d z
f
.
.
=
.
y z dx
x y
...(3)
...(4)
f
dz
x y
=
f
dx
y z
Proved.
du
dx
(U.P. I Sem., Dec. 2005, Com. 2002)
Example 45. If u = x log xy where x3 + y3 + 3 xy = 1. Find
Solution. We have, u = x log xy
1
u
= x . y 1log xy
x
xy
u
= 1 + log xy ...(1)
x
1
x
u
= x .x
xy
y
y
x3 + y3 + 3 xy = 1
On differentiating, we get
dy
dy
3 x2 3 y2
3x
3y = 0
dx
dx
dy
x2 y
=
dx
x y2
We know that
...(2)
...(3)
u d x u d y
du
=
x dx y dx
dx
= (1 log xy ) .1
= 1 log xy
x x2 y
y x y 2
x x2 y
.
y x y2
[From (1), (2), (3)]
Ans.
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Partial Differentiation
41
EXERCISE 1.6
Find
dy
in the following cases :
dx
1. x sin (x – y) – (x + y) = 0
2. xy = yx
Ans. [y + x2 cos (x – y)] / [x + x2 cos (x – y)]
Ans. y (y – x log y)/ x (x – y log x)
3. If ax2 + 2 hxy + by2 = 1, find
d2 y
Ans.
h 2 ab
( hx by )3
d x2
4. If u = x2y + y2z + z2x and if z is defined implicitly as a function of x and y by the equation
x2 + yz + z3 = 0
u
find
, where u is considered as a function of x and y alone.
x
2x
u
2
2
Ans. x 2 xy z ( y 2 zx)
2
y 3z
x
dy
y3 1 0
, if tan–1
y
dx
6. If f (x, y, z) = 0, prove that
5. Find
Ans.
dx dy dz
–1
dy z dz x dx y
y
2 2
x 3x y 3 y 4
dx
f f
Hint .
y x
dy z
1.18 TYPICAL CASES
Example 46. If x = f (u, v),
y = (u, v),
find
u u v v
, , , .
x y x y
Solution.
x = f (u, v)
y = (u, v)
Differentiating (1), (2) w.r.t. x (treating y as constant), we obtain
f u f v
.
.
1 =
u x v x
0 =
u v
.
.
u x v x
v
u
and
, we obtain
x
x
v
f f
.
.
u v v u
u
f f
.
.
u v v u
(2) w.r.t. y, we get
f u f v
.
.
u y v y
...(1)
...(2)
...(3)
...(4)
Solving the equations (3) and (4) for
u
=
x
v
=
x
Similarly, differentiating (1) and
0 =
1 =
u v
.
.
u y v y
Solving the equations (5) and (6) for
Ans.
Ans.
...(5)
...(6)
u
v
and
, we obtain
y
y
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Partial Differentiation
42
f
v
u
=
f
f
y
.
.
u v v u
f
u
v
=
f f
y
.
.
u v v u
Example 47. If x = u2 – v2 and y = uv, find
v
u
u
v
and
,
,
y
x
y
x
Solution. Here, we have x = u2 – v2
y = uv
x
y
= 2 u;
= v
u
u
Ans.
Ans.
(Nagpur University, Winter 2003)
x
y
= – 2 v,
= u
v
v
Differentiating (1) w.r.t. x, we get
u
v
1 = 2u
2v
x
x
Similarly differentiating (2) w.r.t. x, we get
u
v
u
0 = v
x
x
On solving (3) and (4), we get
u
u
v
v
,
=
2
x
2u 2 2v 2
x
2u 2v 2
On differentiating (1) and (2) w.r.t. y, we get
u
v
0 = 2u
2v
y
y
u
v
1 = v y u y
On solving (5) and (6), we get
v
u
u
v
= 2 2,
and
= 2 2
y
y
u v
u v
Example 48. Find p and q, if x = a (sin u + cos v)
...(1)
...(2)
...(3)
...(4)
Ans.
...(5)
...(6)
Ans.
y = a (cos u sin v)
z = 1 + sin (u – v)
z
z
where p and q mean x and y respectively..
Solution. We have,
x =
a (sin u cos v)
y = a (cos u sin v)
z = 1 + sin (u – v)
Differentiating (3) w.r.t. x, we get
u v
z
= cos (u – v)
x
x x
...(1)
...(2)
...(3)
...(4)
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Partial Differentiation
43
Differentiating (1) partially w.r.t. x, we get
u
v
1 = a cos u
sin v
x
x
Differentiating (2) partially w.r.t. x, we get
u
v
cos v
0 = a sin u
x
x
Solving (5) and (6) for
...(6)
v
u
and
, we get
x
x
u
=
x
Putting the values of
...(5)
1
cos v
v
sin u
,
a cos u v x
a cos u v
u
v
and
in (4), we get
x
x
p
1
cos v
sin u
z
= cos u v
a cos u v
x
a cos u v
1
z
sin u cos v
=
x
a
Differentiating (3) w.r.t. y, we get
p
u v
z
= cos u v
y
y y
Differentiating (1) and (2) partially w.r.t. y, we get
u
v
a cos u
sin v
y
y
u
v
= a sin u
cos v
y
y
v
and y , we get
sin v
v
cos u
=
, and
y
a cos u v
a cos u v
Ans.
...(7)
0 =
...(8)
1
...(9)
u
Solving (8) and (9) for y
u
y
Putting the values of u , and v in (7), we have
y
y
sin v
cos u
z
q
= cos (u – v)
y
a cos u v
a cos u v
1
q =
sin v cos u
a
Ans.
EXERCISE 1.7
1.
Fill in the blanks
(i)
(ii)
x y z
is equal to......
· ·
y z x
dz
If z = f (x, y), where x = (t), y = (t), then
=......
dt
If f (x, y, z) = 0, then
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Partial Differentiation
44
dy
= ......
dx
(iii)
If f (x, y) = 0, then
(iv)
If u = x 2 + y 2, x = s + 3t, y = 2s – t, then
(v)
(vi)
du
=
ds
f z
·
· =
If f (x, y) = 0 and (y,z) = 0, then
y z x
f
d2 y
z dx z dy
(iii ) – x
If f (x, y) = 0, then
= .......Ans. (i) – 1 (ii)
f
dx 2
x dt y dt
y
2
2
f
q
r
–
2
pqs
p
t
·
(iv)2x + 4y (v)
(vi) –
x y
q3
1.19 GEOMETRICAL INTERPRETATION OF
z
z
AND
y
x
(Gujarat, I Semester, Jan. 2009)
Let z = f (x, y) be a surface S.
Z
Let y = k be a plane parallel to XZ – plane, passing
through P (x, k, z) cutting the surface z = f (x, y) along the
curve APB.
B
P
This section APB is a plane curve whose equations are
(x, k, z)
z = f (x, y)
A
y = k
X
O
z
The slope of the tangent to this curve is given by
.
x Y
z
Similarly,
is the slope of the tangent to the curve
y
of intersection of the surface z = f (x, y) with a plane parallel to YZ-plane.
1.20 TANGENT PLANE TO A SURFACE
Let f (x, y, z) = 0 be the equation of a surface S. Now
we wish to find out the equation of a tangent plane to S at
the point P (x1, y1, z1).
Let Q (x1 + x1, y1 + y1, z1 + z1) be a neighbouring
point to P. Let the arc PQ be s and the chord PQ be c.
The direction cosines of PQ are
Z
Q
T
P
x y z
,
,
O
c c c
x s
y s
z s
Y
. ,
. ,
.
s c
s c
s c
As s 0, Q P and PQ tends to a tangent line PT. The direction cosines of PT are
dx
dy
dz
,
,
...(1)
ds
ds
ds
Differentiating F (x, y, z) = 0 w.r.t. ‘s’, we get
F dx F dy F dz
=0
...(2)
x ds y ds z ds
From (1) and (2) it is clear that the tangent whose direction cosines are
dx dy dz
, ,
is
ds ds ds
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Partial Differentiation
45
perpendicular to a line having direction ratios
F
F
F
,
,
...(3)
x
y
z
There are a number of tangent lines at P to the curves joining P and Q. All these tangents will
be perpendicular to the line having direction ratios as given by (3).
Hence all these tangent lines will lie in a plane known as tangent plane.
Equation of tangent plane
F
F
F
+ y – y1
+ z – z1
x
y
z
Equation of the normal to the plane.
x – x1 y – y1 z – z1
=
=
F
F
F
x
y
z
x – x1
= 0
Example 49. Find the equation of the tangent plane and normal line to the surface
x2 + 2 y2 + 3 z2 = 12 at (1, 2, – 1).
Solution.
F (x, y, z) = x2 + 2 y2 + 3 z2 – 12
F
= 2 x,
x
F
4 y,
y
F
F
= 2,
x
y
Hence the equation of the tangent plane at (1,
2 (x – 1) + 8 (y – 2) – 6 (z + 1)
2 x + 8 y – 6z = 24 x + 4y – 3z
At the point (1, 2, – 1)
F
6z
z
F
6
z
2, – 1) is
= 0
= 12
8,
x 1 y 2 z 1
x 1 y 2 z 1
Ans.
1
4
3
2
8
6
Example 50. Show that the surface x2 – 2 yz + y3 = 4 is perpendicular to any number of the
family of surfaces x2 + 1 = (2 – 4a) y2 + a z2 at the point of intersection (1, – 1, 2).
Solution.
f (x, y, z) = x2– 2 yz + y3 – 4 = 0
...(1)
2
2
2
F (x, y, z) = x + 1 – (2 – 4 a) y – az = 0
...(2)
f
f
f
= 2x,
2z 3 y2 ,
2y
x
y
z
Direction ratios to the normal of the tangent plane to (1) are
2 x, – 2 z + 3y2, – 2 y
DRs at the point (1, – 1, 2) are 2, – 1, 2.
Now differentiating (2), we get
F
F
F
= 2 x,
2 2 4 a y,
2 a z.
x
y
z
Direction ratios to the normal of the tangent plane to (2) are
2 x, (– 4 + 8 a) y, – 2az.
DRs at the point (1, – 1, 2) are 2, 4 – 8a, – 4a
Now
l1 l2 + m1 m2 + n1 n2 = (2) (2) + (– 1) (4 – 8 a) + 2 (– 4 a)
= 4 – 4 + 8 a – 8 a = 0.
Hence, the given surfaces are perpendicular at (1, – 1, 2).
Ans.
Equation of normal is
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Partial Differentiation
46
EXERCISE 1.8
1. Find the equation of tangent plane and the normal line to the surface
x –1 y – 2 z – 3
6
3
2
2
2
Find the equations of the tangent plane and the normal to the surface z = 4 (1 + x + y2) at
x2 y2 z6
(2, 2, 6).
Ans. 4x + 4y – 3z + 2 = 0, 4 4 3
Find the equations of the tangent plane and the normal to the surface
x2 y3 z 6
x2 y 2
z at (2, 3, – 1)
Ans. 2x – 2y – z + 1 = 0, 2 2 1
2
3
Show that the plane 3x + 12y – 6z – 17 = 0, touches the conicoid 3x2 – 6y2 + 9z2 + 17 = 0.
2
Find also the point of contact.
Ans. 1, 2,
3
Show that the plane ax + by + cz + d = 0 touches the surface px2 + qy2 + 2z = 0,
a2 b2
if
+ 2 c d = 0.
p
q
x y z = 6 at (1, 2, 3).
2.
3.
4.
5.
Ans. 6x + 3y + 2z = 18,
Applications of differential Calculus
(Error, Jacobians, Taylor’s Series, Maxima and Minima)
1.21 ERROR DETERMINATION
y
lim
We know that
=
x 0 x
y
=
x
Definitions:
(i)
dy
dx
dy
dy
approximately y = . x approximately
dx
dx
x is known as absolute error in x.
(ii) x is known as relative error in x.
x
x
(iii)
100 is known as percentage error in x.
x
E2
. Find by using Calculus
R
the approximate percentage change in P when E is increased by 3% and R is decreased by
2%.
(A.M.I.E., Summer 2001)
E2
Solution. Here, we have P =
log P = 2 log E – log R
R
On differentiating, we get
Example 51. The power dissipated in a resistor is given by P =
P 2
R
E–
P
E
R
100
P
100 E 100 R
2
–
P
E
R
P
100 E
100 R
2 (3) – (– 2) 8
2%,
2%
Given, E
P
R
Percentage change in P = 8%
Ans.
Example 52. The diameter and altitude of a can in the shape of a right circular cylinder are
measured as 40 and 64 cm respectively. The possible error in each measurement is ± 5%. Find
approximately the maximum possible error in the computed value for the volume and the
lateral surface. Find the corresponding percentage error.
Solution. Here we have, Diameter of the can (D) = 40 cm
100
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Partial Differentiation
47
100 D
100 h
=
= ± 5%
D
h
V = r2h
(D2 ) h 2
D h
4
4
2 log D log h
4
V
2D h
= 0
V
D
h
2 D
h
V
100
100 2( 5) ( 5) 15
100 =
D
h
V
S=2rl= Dh
log S = log + log D + log h
log V = log
Again
Ans.
D h
S
= 0
D
h
S
D
h
S
100
100 ( 5) ( 5) 10
100 =
D
h
S
Example 53. The period T of a simple pendulum is
Ans.
1
.
8
Find the maximum error in T due to possible errors upto 1% in l and 2% in g.
(U.P. I semester winter 2003)
l
.
Solution. We have, T = 2
g
1
1
log T = log 2 log l – log g
2
2
Differentiating, we get
T
1 l 1 g
–
= 0
T
2 l
2 g
g
1 l
T
100 = 2 l 100 – g 100
T
l
g
100 = 1,
100 2
But
l
g
1
3
T
100 = 2 [1 2] 2
T
Maximum error in T = 1.5%
Ans.
Example 54. A balloon is in the form of right circular cylinder of radius 1.5 m and length
4 m and is surmounted by hemispherical ends. If the radius is increased by 0.01 m and the
length by 0.05 m, find the percentage change in the volume of the balloon.
(U.P. I Sem., Dec., 2005, Comp 2002)
Solution. Radius of the cylinder (r) = 1.5 m
Length of the cylinder (h) = 4 m
Volume of the balloon = Volume of cylinder + Volume of two hemispheres 1.5 m
2
2
4
2
3
3
2
3
Volume (V) = r h r r r h r
4m
3
3
3
4
2
2
V = 2 r r. h r . h 3r . r
3
T = 2
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Partial Differentiation
48
r [2 r. h r. h 4 r r ] 2. r. h r. h 4r. r
V
=
4
4
V
r 2h r3
r h r2
3
3
2 0.01 4 1.5 0.05 4 1.5 0.01
=
4
1.5 4 (1.5) 2
3
0.08 0.075 0.06 0.215
=
63
9
V
100 0.215 21.5
100
2.389%
=
Ans.
V
9
9
Example 55. In estimating the number of bricks in a pile which is measured to be
(5m × 10m × 5m), count of bricks is taken as 100 bricks per m3. Find the error in the cost
when the tape is stretched 2% beyond its standard length. The cost of bricks is ` 2,000 per
thousand bricks.
(U.P., I Semester, Winter 2000)
Solution. Volume V = x y z
log V = log x + log y + log z
Differentiating, we get
x y z
V
= x y z
V
100 x 100 y 100 z
V
100
=
=2+2+2
x
y
z
V
100 V
=6
V
6V 6 (5 10 5)
V=
= 15 cubicmetre.
100
100
Number of bricks in V = 15 × 100 = 1500
1500 2000
3000
1000
Thus error in cost, a loss to the seller of bricks = ` 3000.
Ans.
Example 56. The angles of a triangle are calculated from the sides a, b, c. If small changes
a, b, c are made in the sides, show that approximately
a
a – b. cos C – c. cos B
A=
2
where is the area of the triangle and A, B, C are the angles opposite to a, b, c respectively.
Verify that A + B + C = 0
(U.P., I Sem., Winter 2001, A.M.I.E.T.E., 2001)
Solution. We know that
Error in cost =
b2 c 2 – a 2
cos A =
2bc
2
2
a = b + c2 – 2 b c cos A
...(1)
Differentiating both sides of (1), we get
2a a = 2b b + 2 c c – 2b c cos A – 2 b c cos A + 2b c sin A A
(approx.)
a a = b b + c c – b c cos A – b c cos A + b c sin A A
bc sin A A = a a – (b – c cos A) b – (c – b cos A) c
2 A = a a – (a cos C + c cos A – c cos A) b – (a cos B + b cos A – b cos A) c
1
2 bc sin A
b cos C c cos B a
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Partial Differentiation
49
2 A = a a – a b cos C – a c cos B
= a (a – b cos C – c cos B)
A=
a
[a – b. cos C – c. cos B]
2
...(2)
Similarly,
b
[b – c. cos A – a. cos C ]
2
c
[c – a. cos B – b. cos A]
C=
2
On adding (2), (3) and (4), we get
B=
...(3)
...(4)
1
[(a – b cos C – c cos B ) a (b – a cos C – c cos A) b
2
+ (c – a cos B – b cos A) c]
1
[(a – a) a (b – b) b (c – c) c]
=
2
=0
[ b cos C + c cos B = a] Verified.
Example 57. The height h and semi-vertical angle of a cone are measured, and from there
A, the total area of the cone, including the base, is calculated. If h and are in error by small
quantities h and respectively, find the corresponding error in the area. Show further that,
if = , an error of + 1 per cent in h will be approximately compensated by an error of
6
– 19.8 in
.
(A.M.I.E.T.E., Summer 2003)
A
Solution. Let l be the slant height of the cone and r its radius
l = h sec
[ A + B + C] =
r = h tan
A = r2 + r l
l
h
= h2 tan2 + (h tan ) (h sec )
= h2 [tan2 + tan sec ]
A = 2 h h [tan2 + tan sec ]
+
h2[2
tan
sec2
B
+
A = 2h [tan + sec ] tan . h +
h2
sec2
r
D
C
. . sec + tan sec tan ]
[2 tan sec + sec2 + tan2]. sec .
A = 2h [tan + sec ] tan . h + h2 [tan + sec ]2 sec .
h
(tan sec ) sec .
A = h2 [tan + sec ] 2 tan
h
h
100 1, we get
On putting
A = 0, ,
6 h
1
tan sec
0 = h 2 tan sec 2 tan
6
6
6 100
6
1
tan sec sec
0 = 2 tan
6
100
6
6
6
2 1
1
2 2
2 1
1
0 =
3 100 3
3 3
3 100 3
sec
6
6
2 2
3 3
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Partial Differentiation
50
1
1
2
1
3
1
=
100
100
3
3
100 3
3
180
9 60
degree –
minutes = – 19.8 minutes Ans.
100 3
5 3
Example 58. Find the possible percentage error in computing the parallel resistance r of
1
= –
three resistances r1, r2, r3 from the formula
1.2%.
1 1 1 1
= + + if r1, r2, r3 are each in error by plus
r r1 r2 r3
1
1 1 1
=
r
r1 r2 r3
Solution. Here,
Differentiating, we get
1
1
2
radius remains constant, prove that
r12
dr1 –
1
r22
dr2 –
1
dr3
r32
r
1 100 dr
1 100 dr1 1 100 dr2 1 100 dr3
=
r r
r1 r1 r2 r2 r3 r3
1 1 1
1
1
1
= (1.2) (1.2) (1.2) (1.2)
r1
r2
r3
r1 r2 r3
1
= 1.2
[From (1).]
r
100 dr
1.2%
Ans.
r
Example 59. If the sides and angles of a plane triangle vary in such a way that its circum
–
dr = –
...(1)
da
db
dc
+
+
= 0, where da, db, dc are small
cos A cos B cos C
increments in the sides, a, b, c respectively.
Solution. From the sine rule,
a
b
c
=
sin A
sin B sin C
a
We know that
R = 2 sin A ,
...(1)
Differentiating, we get
a cos A
R
= –
2 sin 2 A
A
1
R
= 2 sin A
a
R
R
dA
da
By total differentiation
dR =
A
a
a cos A
1
. dA
. da ,
0=–
2 sin A
2 sin 2 A
A
R
B
O
a
C
R being constant
cos A
1
dA
da
sin A
sin A
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Partial Differentiation
51
a
da
. dA 2R. dA
=
sin A
cos A
da
=2RdA
cos A
db
Similarly,
=2RdB
cos B
dc
and
=2RdC
cos C
Adding (1), (2) and (3), we have
da
db
dc
= 2R [dA + dB + dC]
cos A cos B cos C
[Using (1)]
...(1)
...(2)
...(3)
...(4)
But in any triangle ABC, A + B + C =
Hence,
dA + dB + dC = 0
Putting value of dA + dB + dC = 0 in (4), we get
da
db
dc
da
db
dc
0
= 2 R (0) 0
cos A cos B cos C
cos A cos B cos C
Proved.
Example 60. Compute an approximate value of (1.04)3.01.
Solution. Let
We have
Here, let
Now
f (x, y) = xy
f
f
x y log x
y xy –1 ,
y
x
x = 1, x = 0.04,
y = 3, x = 0.01
...(1)
f
f
df = x dx y dy
...(2)
= y x y – 1 x y log x
Substituting the values from (1) in (2), we get
d f = (3) (1)3–1 (0.04) + (1)3 log (1) (0.01) = 0.12
(1.04)3.01 = f (1, 3) + d f = 1 + 0.12 = 1.12
Ans.
1
Example 61. Find [(3.82)2 + 2(2.1)3 ] 5
1
2
3
Solution. Let f (x, y) = ( x 2 y ) 5
Taking
x = 4, x = 3.82 – 4 = – 0.18
y = 2, x = 2.1 – 2 = 0.1
4
4
–
–
1 2
2
8 1 1
f
3
3
= [ x 2 y ] 5 (2 x ) (4) [16 2(2) ] 5
5
5
5
x
16 10
4
4
–
–
f
1 2
6
24 1
3
3
2
2
3
= [ x 2 y ] 5 (6 y ) (2) [16 2(2) ] 5
y
5
5
5 16 10
By total differentiation, we get
f
f
df x y = 1 (– 0.18) 3 (0.1) – 0.018 0.03 0.012
x
y
10
10
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Partial Differentiation
52
1
[(3.82)2 2(2.1)3 ]5 = f (4, 2) + df
1
= [(4)2 2(2)3 ]5 0.012 2 0.012 2.012
Ans.
EXERCISE 1.9
1. If the density of a body be inferred from its weights W, in air and water respectively, show that
– W
.
.
the relative error in due to errors W in W, is
W – W
W –
2. The period of oscillation of a pendulum is computed by the formula
T = 2
l
.
g
Show that the percentage error in T =
1
[% error in l – % error in g]
2
1
, find the error in the determination of T..
160
(Given g = 981 cm/sec2)
Ans. – 0.00153
3. The indicated horse power I of an engine is calculated from the formula.
I = PLAN/33000
If l = 6 cm and relative error in g is equal to
2
d . Assuming that errors of r percent may have been made in measuring P. L, N and
4
d. Find the greatest possible error in I.
Ans. 5 r %
The dimensions of a cone are radius 4 cm, height 6 cm. What is the error in its volume if the scale
used in taking the measurement is short by 0.01 cm per cm.
Ans. 0.96 cm3.
The work that must be done to propel a ship of displacement D for a distance s in time t is proportional
to s2 D2/3 t2.
Find approximately the percentage increase of work necessary when the displacement is increased
14
by 1%, the time is diminished by 1% and the distance is increased by 3%.
Ans.
%
3
The power P required to propel a ship of length l moving with a velocity V is given by P = kV3 t2.
Find the percentage increase in power if increase in velocity is 3% and increase in length is 4%.
Ans. 17%
In estimating the cost of a pile of bricks measured as 2m × 15 m × 1.2 m, the tape is stretched 1%
beyond the standard length if the count is 450 bricks to 1 m3 and bricks cost ` 1300 per 1000, find
the approximate error in the cost.
Ans. ` 631.80
In estimating the cost of a pile of bricks measured as 6 × 50 × 4, the tape is stretched 1% beyond
the standard length. If the count is 12 bricks to ft 3, and bricks cost ` 100 per 1000, find the
approximate error in the cost.
(U.P. I Sem., Dec. 2004) Ans. 720 bricks, ` 25.20
The sides of a triangle are measured as 12 cm and 15 cm and the angle included between them as
60°. If the lengths can be measured within 1% accuracy while the angle can be measured within 2%
accuracy. Find the percentage error in determining (i) area of the triangle (ii) length of opposite side
of the triangle.
(A.M.I.E.T.E., Winter 2002)
The voltage V across a resistor is measured with error h, and the resistance R is measured with an
where A =
4.
5.
6.
7.
8.
9.
10.
error k. Show that the error in calculating the power W(V, R) =
V2
generated in the resistor is
R
V
(2 Rh – V k ). If V can be measured to an accuracy of 0.5 p.c. and R to an accuracy of 1 p.c.,
R2
what is the approximate possible percentage error in W ?
Ans. Zero percent
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Partial Differentiation
53
11.
Find the possible percentage error in computing the parallel resistance r of two resistance r1 and r2
1 1 1
from the formula , where r1 and r2 are both is error by + 2% each. Ans. 2%
r r1 r2
12. In the manufacture of closed cylindrical boxes with specified sides a, b, c (a b c), small changes
of A%, B%, C% occurred in a, b, c, respectively from box to box from the specified dimension.
However, the volume and surface area of all boxes were according to specification, show that:
A
B
C
a (b – c ) b(c – a ) c (a – b )
13. Find the percentage error in calculating the area of ellipse x2/a2 + y2/b2 = 1, when error of + 1% is
made in measuring the major and minor axes.
Ans. 2%
(U.P., I Sem, Jan 2011)
1
2 2
14. If f = x y z 10 , find the approximate value of f, when x = 1.99, y = 3.01 and z = 0.98.
Ans. 107.784
15. A diameter and altitude of a can in the form of right circular cylinder are measured as 4 cm and 6 cm
respectively. The possible error in each measurement is 0.1 cm. Find approximately the maximum
possible error in the value computed for the volume and lateral surface.
(A.M.I.E., Summer 2001) Ans. 5.0336 cm3, 3.146 cm2
16. Prove that the relative error of a quotient does not exceed the sum of the relative errors of the
dividend and the divisor.
(A.M.I.E., Winter 2001)
1.22 JACOBIANS
If u and v are functions of the two independent variables x and y, then the determinant
u u
x y
v v
x y
is called the jacobian of u, v with respect to x, y and is written as
u, v
(u , v )
or J
( x, y )
x, y
Similarly, the jacobian of u, v, w with respect to x, y, z is
u
x
(u, v, w)
v
=
( x, y , z )
x
w
x
u
y
v
y
w
y
u
z
v
z
w
z
Example 62. If x = r cos , y = r sin ; evaluate
Solution. We have,
( r , )
( x, y )
, and
( x, y )
( r , )
y = r sin
y
= sin
r
y
= r cos
x = r cos ,
x
= cos ,
r
x
= – r sin ,
x x
( x, y )
r
cos r sin
= y y =
(r , )
sin r cos
r
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Partial Differentiation
54
= r cos2 + r sin2 = r (cos2 + sin2 ) = r
y
Now,
r2 = x2 + y2,
= tan 1
x
y
r
x
y
= ,
= 2
=
x
r
x y2
x
r2
x
r
x
y
= , y = 2
= 2
y
x y2
r
r
r r
x
y
x y
1
r
r
x2 y2
x2 y 2
r2
( r , )
=
=
= 3 3 =
= 3 =
Ans.
3
y x
r
( x, y )
r
r
r
r
x y
r2 r2
1
( x, y ) (r , )
Note :
= r = 1
r
(r , ) ( x, y )
Example 63. If x = a cosh cos , y = a sinh sin , show that
( x, y )
a2
( cosh 2 cos 2 )
=
(, )
2
Solution. Here, we have, x = a cosh cos
y = a sinh sin
x x
a sinh cos a cosh sin
( x, y )
=
= a cosh sin a sinh cos
(, )
y y
2
= a
sinh cos cosh sin
= a2 [sinh2 cos2 + cosh2 sin2 ]
cosh sin sinh cos
= a2 [sinh2 (1 – sin2) + (1 + sinh2 ) sin2 ]
= a2 [sinh2 – sinh2 sin2 + sin2 + sinh2 sin 2 ]
a2
a2
[ cosh 2 1 1 cos 2 ] =
[ cosh 2 cos 2 ] Proved.
= a2 [sinh2 + sin2 ] =
2
2
x1 x2
x2 x3
x3 x1
Example 64. If y1 =
, y2 =
, y3 =
.
x3
x1
x2
Show that the Jacobian of y1, y2, y3 with respect to x1, x2, x3 is 4.
(U.P. I Sem. Jan 2011; 2004, Comp. 2002, A.M.I.E., Summer 2002, 2000, Winter 2001)
x2 x3
x3 x1
x1 x2
Solution. Here, we have y1 = x , y2 = x , y3 = x
1
2
3
x2 x3
y1
x1
y1
x2
y1
x3
y2
( y1 , y2 , y3 )
=
x1
( x1 , x2 , x3 )
y3
x1
y2
x2
y2
=
x3
x3
x2
y3
x2
y3
x3
x2
x3
=
1
x12 x22 x32
x12
x3
x1
x2
x1
x3 x1
x1
x2
x22
x1
x3
x2 x3
x3 x1
x1 x2
x2 x3
x3 x1
x1 x2
=
x1 x2
x32
x12 x22 x32
x12 x22 x32
1
1
1
1
1
1
1
1
x2 x3
x3 x1 x1 x2
= – 1 (1 – 1) – 1 (– 1 – 1) + 1 (1 + 1) = 0 + 2 + 2 = 4
1
Proved.
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Partial Differentiation
55
Example 65. If x = r sin cos ,
y = r sin sin ,
z = r cos ,
( x, y , z )
Show that (r , , ) = r2 sin .
Solution. We have, x = r sin cos ,
x
= sin cos ,
r
(U.P., I Semester, Winter 2000)
x
= r cos cos ,
x
=–
x
r
( x, y , z )
y
=
(r , , )
r
z
r
= r2 sin
= r2 sin
= r2 sin
= r2 sin
r sin sin ,
x
y
z
If u = x2, v = y2, find
2.
If u =
4.
5.
z = r cos
z
= cos
r
y
= r cos sin ,
z
= – r sin
y
= r sin cos ,
z
=0
x
sin cos r cos cos r sin sin
y
= sin sin r cos sin r sin cos
cos
r sin
0
z
sin cos cos cos sin
2
= r sin sin sin cos sin cos
cos
sin
0
[sin cos (0 + sin cos ) – cos cos (0 – cos cos )
– sin (– sin2 sin –cos2 sin )]
[sin2 cos2 + cos2 cos2 + sin2 sin2 + cos2 sin2 ]
[(sin2 + cos2 ) cos2 + (sin2 + cos2 ) sin2 ]
[cos2 + sin2 ] = r2 sin
Ans.
EXERCISE 1.10
1.
3.
y = r sin sin ,
y
= sin sin ,
r
(u , v)
( x, y )
Ans. 4xy
yx
(u , v)
and v = tan–1 y – tan–1 x , find
Ans. 0
1 xy
( x, y )
( u , v , w)
If u = xyz, v = xy + yz + zx, w = x + y + z, compute
Ans. (x – y) (y – z) (z – x)
( x, y , z )
( x, y , z )
.
If x = r cos , y = r sin , z = z find
( r , θ, z)
( u1, u2 ,......un 1 )
xn 1
x1
x2
If u1 = x , u2 = x , ...... un–1 =
and x12 x22 x32 ...... xn2 = 1 find ( x , x ,...... x )
x
n
n
n
1
2
n 1
1
Ans.
6.
7.
8.
xnn 1
( y1 , y2 , y3 )
, y = (1 – x1), y2 = x1 (1 – x2), y3 = x1x2 (1 – x3) Ans. x12 x2
( x1, x2 , x3 ) 1
x
y
(u , v, w)
z
If u =
,v=
,w=
then show that
=0
yz
zx
x y
( x, y , z )
Fill in the blanks
( x, y )
(i) If x = r cos , y = r sin , then the value of Jacobian
is ........
Ans. r
( r ,θ)
Find the value of
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Partial Differentiation
56
(ii) If u = x (1 – y), v = xy, then the value of the Jacobian
(u , v)
= ........
( x, y )
Ans. x
1.23 PROPERTIES OF JACOBIANS
(1) First Property
If u and v are the functions of x and y, then
(u, v ) ( x, y )
=1
( U. P. I Semester Dec. 2005 )
( x , y ) (u , v )
Proof.
Let u = f (x, y)
...(1)
v = (x, y)
...(2)
u u
x x
x y
u v
(u, v ) ( x, y )
=
v
v
y y
( x , y ) (u , v )
x y
u v
On interchanging the rows and columns of second determinant
u
x
u
y
x
u
y
u
u x u y
x u y u
=
v v
x y
v x v y
x y
v v
x u y u
On differentiating (1) and (2) w. r. t. u and v, we get
=
u x u y
x v y v
v x v y
x v y v
...(3)
u
u x u y
1
u
x u y u
u
u x u y
0
v
x v y v
...(4)
v
v x v y
1
v
x v y v
v
v x v y
0
u
x u y u
On making substitutions from (4) in (3), we get
1 0
(u, v ) ( x, y )
=
=1
Proved.
0 1
( x, y ) (u, v )
uv
(u , v )
Example 66. If x = uv, y =
, find
.
uv
( x, y )
x x y y
u u v v
Solution. Here it is easy to find
,
,
,
. But to find
,
,
,
is
u v u v
x y x y
( x, y )
comparatively difficult. So we first find
( u, v )
x x
v
u
u v
1 1
uv
4u v
uv
( x, y )
( 2 2) =
2v
2u
=
=
=
=
2
2
y y
( u v)
( u v )2
( u , v)
( u v) 2 2
(u v)2 ( u v)2
u v
( u , v ) ( x, y )
( u, v )
4u v
( u , v ) ( u v) 2
But
= 1
=
1
Ans.
( x, y ) ( u , v )
( x, y ) ( u v )2
( x, y )
4u v
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Partial Differentiation
57
( x, y, z )
.
( u, v, w)
Solution. Since u, v, w are explicitly given, so first we evaluate (U.P. I Sem., Winter 2002)
( u, v, w)
J = ( x, y, z )
Example 67. If u = xyz, v = x2 + y2 + z2, w = x + y + z, find J =
u u u
x y z
yz zx xy
v v v
J =
= 2x 2y 2 z
x y z
1 1 1
w w w
x y z
= yz ( 2y – 2z ) – zx ( 2x – 2z ) + xy (2x – 2y) = 2 [yz ( y – z ) – zx ( x – z) + xy ( x – y)]
= 2 [x2y – x2z – xy2 + xz2 + y2z – yz2] = 2 [ x2 ( y – z ) – x ( y2 – z2 ) + yz ( y – z)]
= 2 ( y – z ) [x2 – x ( y + z ) + yz] = 2 ( y – z) [ y ( z – x) – x ( z – x )]
= 2 ( y – z ) ( z – x ) ( y – x ) = – 2 ( x – y) ( y – z ) ( z – x )
Hence, by JJ = 1, we have
( x, y , z )
1
J=
=
Ans.
( u , v, w )
2( x y ) ( y z ) ( z x )
EXERCISE 1.11
( x, y )
( u, v )
1.
Given u = x2 – y2, v = 2xy, calculate
2.
If x = uv, y =
3.
If x = r sin cos , y = r sin sin , z = r cos , find
4.
Verify JJ = 1, if x = uv, y =
5.
6.
Ans.
uv
( u, v)
, find
u v
( x, y )
Ans.
( r,θ,φ)
( x, y , z )
Ans.
1
2
4( x y 2 )
( u v)2
4 uv
1
r 2 sin θ
u
v
Verify JJ = 1, if x = ev sec u, y = ev tan u.
Verify JJ = 1, if x = sin cos , y = sin sin
(2) Second Property (Chain Rule)
If u, v are the functions of r, s where r, s are functions of x, y, then
( u, v ) ( r , s )
( u , v)
= ( r , s ) ( x, y )
(U.P. I Sem. Jan 2011)
( x, y )
u u
r r
r s
x y
( u , v) ( r , s )
Proof.
=
v v
s s
( r, s ) ( x, y)
r s
x y
On interchanging the columns and rows in second determinant
u u
r s
u r u s
u r u s
r s
x x
r x s x
r y s y
= v v
r s = v r v s
v r v s
r s
y y
r x s x
r y s y
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Partial Differentiation
58
u
x
=
v
x
Similarly,
u
y
(u , v)
=
v
( x, y )
y
(u, v, w)
(u, v, w) (r , s, t )
=
( x, y , z )
( r , s , t ) ( x, y, z )
Example 68. Find the value of the Jacobian
Proved.
(u, v)
, wheree u = x2 – y2, v = 2x y and
(r , )
x = r cos , y = r sin .
Solution. u = x2 – y2, v = 2 x y
u u
2x 2 y
x y
(u , v)
=
=
= 4 (x2 + y2) = 4 r2
2 y 2x
v v
( x, y )
x y
x x
cos r sin
( x, y )
r
= y y =
= r cos2 + r sin2 = r
( r , )
sin r cos
r
(u, v)
(u , v ) ( x , y )
=
= 4r2 . r = 4r3
Ans.
(r , )
( x, y ) (r , )
EXERCISE 1.12
1. If u = ex cos y, v = ex sin y, where x = lr + sm, y = mr – sl, verify
2
2. If u = x (1 r )
w = z (1
Show that
1
r2 ) 2
1
2,
2
v y (1 r )
( u , v ) ( x, y )
(u , v)
=
( x, y ) ( r , s)
(r, s)
1
2
where r 2 x2 y 2 z 2
5
(u, v, w)
= (1 r 2 ) 2
( x, y , z )
(Q. Bank, U. P. 2001)
3. If u = x + y + z, u2 v = y + z, u3 w = z, show that
(u, v, w)
= u–5
( x, y , z )
Hint. Put r = x + y + z, s = y + z, t = z
u = r u2 v = s
u3w = t
(u, v, w)
(u , v, w) ( r , s, t )
( r , s, t )
1
=
=
( x, y , z )
( r , s , t ) ( x, y , z )
( r , s, t ) ( x, y , z )
(u , v, w)
( x, y , z )
4. If u = x + y + z, uv = y + z, uvw = z. Evaluate
( u , v , w)
5. If u3 + v3 = x + y, u2 + v2 = x3 + y3, show that
(3)
( U. P. I Sem. Winter 2003 ) Ans. u2v
(u , v )
1 ( y2 x2 )
=
( x, y )
2 u v (u v)
Third Property
If functions u, v, w of three independent variables x, y, z are not independent, then
(u , v, w)
=0
( x, y , z )
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Partial Differentiation
59
Proof. As u, v, w are not indepentent, then f (u, v, w) = 0
Differentiating (1) w.r.t x, y, z, we get
f u f v f w
=0
u x v x w x
...(1)
...(2)
f u f v f w
=0
u y v y w y
...(3)
f u f v f w
=0
u z v z w z
...(4)
Eliminating
f f f
,
,
from (2), (3) and (4), we have
u v w
u
x
u
y
u
z
v
x
v
y
v
z
w
x
w
y
w
z
=0
On interchanging rows and columns, we get
u
x
v
x
w
x
u
y
v
y
w
y
u
z
v
=0
z
w
z
( u , v, w )
=0
Proved.
( x, y , z )
Converse (The sufficient condition)
( u , v, w )
If it is given that
= 0 and u, v, w are not independent of one another then they are
( x, y , z )
connected by a relation f (u, v, w) = 0.
Example 69. If u = x y + y z + z x, v = x2 + y2 + z2 and w = x + y + z, determine whether there
is a functional relationship between u, v, w and if so, find it.
Solution. We have, u = x y + y z + z x, v = x2 + y2 + z2, w = x + y + z
u u u
x y z
yz zx x y
( u , v, w )
v v v
2y
2z
=
= 2x
( x, y , z )
x y z
1
1
1
w w w
x y z
x y z x yz x y z R R + R
yz zx x y
l
= 2
x
y
z
1
1
1
=
2
x
y
z
1
1
1
1
2
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Partial Differentiation
60
1 1 1
= 2 ( x y z ) x y z = 0 (R1 = R3)
1 1 1
Hence, the functional relationship exists between u, v and w.
Now, w2 = (x + y + z)2 = x2 + y2 + z2 + 2(x y + y z + z x)
w2 = v + 2u
w2 – v – 2u = 0
which is the required relationship.
Ans.
Example 70. Verify whether the following functions are functionally dependent, and if so, find
the relation between them.
xy
,
v tan 1 x tan 1 y
u=
1 x y
u
1 y2
1 x2
y
1
1
(1 xy )2 (1 xy )2
Solution.
=
=
=0
v
1
1
(1 xy )2 (1 xy )2
y
1 x2
1 y2
Hence u, v are functionally related.
1 x y
tan–1 x + tan–1 y = tan 1 xy
v = tan– 1 u
u = tan v.
Ans.
EXERCISE 1.13
u
x
(u , v)
=
v
( x, y )
x
1.
Verify whether u =
x y
,
x y
v
x y
are
x
functionally dependent, and if so, find the relation between them.
2.
4.
5.
xy
,
x y
v
xy
Ans. 4v = 1– u2
( x y) 2
Are x + y – z, x – y + z, x2 + y2 + z2 – 2yz functionally dependent ? If so, find a relation between
them.
Ans. u2 + v2 = 2w
2
2
2
3
3
3
If u = x + y + z, v = x + y + z , w = x + y + z – 3xyz, prove that u, v, w are not independent
and find the relation between them.
Ans. 2w = u (3v – u2)
Are the following two functions of x, y, z functionally dependent ? If so find the relation between
them.
u
6.
2v
v
Determine functional dependence and find relation between
u
3.
Ans. u =
x y
,
xz
If u =
v
xz
yz
Ans. v =
1
1u
y z
y ( x y z)
x y
,v=
,w=
, show that u, v, w are not independent and find the
x
xz
z
relation between them.
(U.P., Ist Semester, 2009)
1.24 JACOBIAN OF IMPLICIT FUNCTIONS
The variables x, y, u, v are connected by implicit functions
f1 (x, y, u, v) = 0
f2 (x, y, u, v) = 0
where u, v are implicit functions of x, y.
Ans. uv – w = 1
... (1)
... (2)
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Partial Differentiation
61
Differentiating (1) and (2) w.r.t. x and y, we get
f1 f1 u f1 v
=0
x u x v x
f1 f1 u f1 v
=0
y u y v y
f 2 f 2
x
u
f 2 f 2
y
u
Now, we have
u f 2
x v
u f 2
y v
... (3)
... (4)
v
=0
x
v
=0
y
f1
( f1 , f 2 ) (u, v )
u
=
(u , v )
( x, y )
f 2
u
f1
u
=
f 2
u
... (5)
... (6)
u
f1
x
v
f 2
v
v
x
u f1
x v
u f 2
x v
2
= ( 1)
v
x
v
x
u
y
v
y
f1
u
f 2
u
u f1
y v
u f 2
y v
v
y
f1
f
1
x
y
=
v
f
f
2 2
y
x
y
[From (3), (4), (5), (6)]
( f1 , f 2 )
( x, y )
(u, v)
2 ( f1 , f 2 ) / ( x, y )
= ( 1)
( f1 , f 2 ) / (u , v )
( x, y )
In general, the variables x1, x2, ... xn are connected with u1, u2, ... un implicitly as f1(x1, x2, ... xn,
u1, u2, ... un) = 0, f2(x1, x2, ... xn, u1, u2, ... un) = 0 ... ... ... ... ... fn (x1, x2, ... xn, u1, u2, ... un) = 0
Then we have
(u1 , u2 , ... un )
n ( f1 , f 2 , ... f n ) / ( x1 , x2 , ... xn )
= ( 1)
( x1 , x2 , ... xn )
( f1 , f 2 , ... f n ) / (u1 , u2 , ... un )
Example 71. If x2 + y2 + u2 – v2 = 0 and uv + xy = 0, prove that
Solution. Let
But
(u , v ) x 2 y 2
( x, y ) u 2 v 2
f1 = x2 + y2 + u2 – v2, f2 = uv + xy
f1 f1
x y
2x 2 y
( f1 , f 2 )
=
=
= 2 (x2 – y2)
f 2 f 2
y
x
( x, y )
x y
f1 f1
2u 2v
( f1 , f 2 )
u v
=
=
= 2 (u2 + v2)
f 2 f 2
(u , v )
v
u
u v
( f1 , f 2 )
x2 y2
(u , v)
2 (x2 y2 )
( x, y )
= (1)2
=
=
( f1 , f 2 )
( x, y )
u 2 v2
2 (u 2 v 2 )
(u , v )
Proved.
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Partial Differentiation
62
Example 72. If u3 + v + w = x + y2 + z2, u + v3 + w = x2 + y + z2, u + v + w3 = x2 + y2 + z, prove
that
1 4 ( xy yz zx ) 16 xyz
(u, v, w)
=
( x, y , z )
2 3 (u 2 v 2 w2 ) 27 u 2 v 2 w2
Solution. Let
Now,
and
f1 = u3 + v + w –
f2 = u + v3 + w –
f3 = u + v + w3 –
1 2 y
( f1 , f 2 , f 3 )
= 2 x 1
( x, y , z )
2x 2 y
( f1 , f 2 , f 3 )
=
(u , v, w)
3u 2
1
2
1
3v
1
1
x – y2 – z2
x2 – y – z2
x2 – y2 – z
2 z
2 z = – 1 + 4 (yz + zx + xy) – 16 xyz
1
1
1
= 2 – 3 (u2 + v2 + w2) + 27 u2 v2 w2
3w2
(u, v, w)
1 4 ( yz zx xy ) 16 xyz
3 ( f1 , f 2 , f 3 ) / ( x, y , z )
= (1)
=
Proved.
( x, y , z )
( f1 , f 2 , f 3 ) / (u, v, w)
2 3 (u 2 v 2 w2 ) 27 u 2 v 2 w2
( x, y , z )
Example 73. If x + y + z = u, y + z = uv, z = uvw, show that (u, v, w) = u2 v
Solution. Let
f1 = x + y + z – u
f2 = y + z – uv
f3 = z – uvw
f1
x
f 2
( f1 , f 2 , f 3 )
=
x
( x, y , z )
f3
x
f1
y
f 2
y
f3
y
f1
z
1 1 1
f 2
= 0 1 1 =1
z
0 0 1
f 3
z
f1
u
( f1 , f 2 , f 3 )
f 2
=
(u , v, w)
u
f3
u
f1
v
f 2
v
f 3
v
f1
w
1
0
0
f 2
u
0 = – u2 v
= v
w
vw uw uv
f 3
w
( f1 , f 2 , f3 )
u2 v
( x, y , z )
(u, v, w)
3
But
= (–1) ( f , f , f ) =
= u2 v
Proved.
1
(u, v, w)
1
2
3
( x, y , z )
Example 74. If u, v, w are the roots of the equation ( – x)3 + ( – y)3 + ( – z)3 = 0 in find
(u , v, w)
.
( x, y , z )
(U.P. I Sem. Jan, 2011; Winter 2001)
Solution. ( – x)3 + ( – y)3 + ( – z)3 = 0
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Partial Differentiation
63
3 3 – 3(x + y + z) 2 + 3(x2 + y2 + z2) – (x3 + y3 + z3) = 0
Sum of the roots = u + v + w = x + y + z
Product of the roots = uv + vw + wu = x2 + y2 + z2
... (1)
... (2)
1 3
( x y3 z3 )
3
Equations (1), (2) and (3) can be rewritten as
f1 = u + v + w – x – y – z
f2 = uv + vw + wu – x2 – y2 – z2
uvw =
f3 = uvw
f1
x
( f1 , f 2 , f 3 )
f 2
=
x
( x, y , z )
f3
x
1
1
= (1) ( 2) (1) x
y
2
2
x
= 2 ( x y) ( y z)
y
1 3
( x y 3 z3 )
3
f1 f1
y z
1
f 2 f 2
= 2x
y z
x2
f3 f 3
y z
1
0
z = 2 x y
z
2
2
x y
0
0
1
1
1
z
1
y2
y z
z2
1
yz
2
1
2 y 2z
0
2
... (3)
z
2
z
2
C1 C1 C2
C2 C2 C3
2 ( x y) ( y z) ( y z x y)
2
f1
u
f 2
( f1 , f 2 , f 3 )
=
u
(u , v, w)
f3
u
x y y z z 2 ( x y) ( y z) ( z x)
f1 f1
v w
1
1
1
f 2 f 2
v
w
u
w
u
v
=
v w
vw
wu
uv
f3 f 3
v w
=
0
0
0
0
vu
wv
1
C1 C1 C2
u v C2 C2 C3
w ( v u ) u ( w v)
uv
1
= ( v u ) ( w v ) 1 1 u v ( v u ) ( w v ) ( u w)
w u uv (u v) (v w) ( w u )
( f1 , f 2 , f3 )
2 ( x y ) ( y z ) ( z x)
(u, v, w)
2 ( x y) ( y z) ( z x)
( x, y , z )
=–
=–
= – (u v) (v w) ( w u )
( x, y , z )
( f1 , f 2 , f3 )
(u v) (v w) (w u )
(u, v, w)
Ans.
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Partial Differentiation
64
EXERCISE 1.14
1 y 2 x2
(u , v)
=
2 2 uv(u v )
( x, y )
y + z, u2 + v2 + w2 = x3 + y3 + z3, u + v + w = x2 + y2 + z2,
( x y) ( y z) ( z x)
=
(u v ) (v w) ( w u )
y
z
, w
, show that
2
1 r
1 r2
1.
If u3 + v3 = x + y, u2 + v2 = x3 + y3, then prove that
2.
If u3 + v3 + w3 = x +
( u , v , w)
show that
( x, y , z )
x
, v
If u =
1 r2
3.
4.
1
( u , v , w)
=
where r2 = x2 + y2 + z2
( x, y , z )
(1 r 2 )5 2
If u1 = x1 + x2 + x3 + x4, u1u2 = x2 + x3 + x4, u1u2u3 = x3 + x4, u1u2u3u4 = x4
( x1 , x2 , x3 , x4 )
show that
= u13 u22 u3
(u1 , u 2 , u3 , u4 )
5.
If u, v, w are the roots of the equation in and
x
y
z
( x, y , z )
= 1, then find
.
aλ bλ cλ
(u , v, w)
(u v ) (v w) ( w u )
Ans.
(a b) (b c ) (c a )
1.25 PARTIAL DERIVATIVES OF IMPLICIT FUNCTIONS BY JACOBIAN
Given f1 (x, y, u, v) = 0, f2 (x, y, u, v) = 0
f1 u f1 v f1
1 = 0
u x v x x
f 2 u f 2 v f 2
1 = 0
u x v x x
Solving (1) and (2) , we get
u
x
f1 f 2 f1 f 2
v x x v
=
v
x
f1 f 2 f1 f 2
x u u x
f1
u
v
=
f1
x
u
and if,
f 2 f1
x x
f
f
2 1
v v
=
... (1)
... (2)
1
f1 f 2 f1 f 2
u v v u
( f1 , f 2 )
f
2
( x, v )
v
=–
f 2
( f1 , f 2 )
u
(u , v )
( f1 , f 2 )
f1 f 2 f1 f 2
( x, u )
v
= x u u x =
( f1 , f 2 )
f1 f 2 f1 f 2
x
(v , u )
u v v u
f1 (x, y, z, u, v, w) = 0, f2 = (x, y, z, u, v, w) = 0
f3 (x, y, z, u, v, w) = 0
x
( f1 , f 2 , f3 ) / (u, y, z )
=
u
( f1 , f 2 , f3 ) / ( x, y, z )
and so on.
Note. First we write the Jacobian in the denominator and then we write the Jacobian in the
numerator by replacing x by u.
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Partial Differentiation
65
u
Example 75. Use Jacobians to find if :
x v
2
2
2
u + xv – xy = 0 and u + xyv + v2 = 0
Solution. Let
f1 = u2 + xv2 – xy, f2 = u2 + xyv + v2
f1 f1
u v
( f1 , f 2 )
= f
f 2 =
2
(u, v)
u v
f1 f1
( f1 , f 2 )
x v
=
=
f 2 f 2
( x, v)
x v
= x y v2 + 2v3 –
2u
2 xv
xy 2v = 2uxy + 4uv – 4uxv
2u
v2 y
2 xv
yv
xy 2v
xy2 – 2yv – 2xyv2 = – xyv2 + 2v3 – xy2 – 2yv
u
( f1 , f 2 ) / ( x, v)
xyv 2 2v 3 xy 2 2 yv
=
=
x
( f1 , f 2 ) / (u, v)
2uxy 4uv 2 xuv
Proved.
Example 76. If u = x + y2, v = y + z2, w = z + x2, prove that
x
1
=
u
1 8 xyz
Solution. (i) Here
(i)
Now
(ii) Also find
2x
.
u 2
f1 u – x – y2, f2 = v – y – z2
f3 w – z – x2.
1 2y
0
( f1 , f 2 , f 3 )
= 0 1 2 z = 1 ;
(u , y , z )
0
0
1
( f1 , f 2 , f 3 )
=
( x, y , z )
1 2 y
0
0
1 2 z = – 1 (1 + 0) + 2y (0 – 4zx) = – 1 – 8 xyz
0
1
2x
x
( f1 , f 2 , f 3 ) / (u, y , z )
=
u
( f1 , f 2 , f 3 ) / ( x, y , z )
1
x
1
=
=
1 8 xyz
u
1 8 xyz
(ii)
2x
u 2
=
u
=
=
We have,
( f1 , f 2 , f 3 )
=
( x, u , z )
Proved.
1
1
x
(1 8 xyz )
= u 1 8 xyz =
2
u
(1 8 xyz ) u
1
y
z
x
0 8 u y z u z x u x y
(1 8 xyz )
2
8
y
z
x
u y z u z x u x y
(1 8 xyz )
1 1
0
2
... (1)
0
0 2 z = 4 zx ;
2 x 0 1
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Partial Differentiation
66
( f1 , f 2 , f 3 )
=
( x, y , u )
y
u
y
u
z
u
z
u
Substituting in (1), we have
Now,
2x
u
2
1
2y 1
0
1
2x
0
0 = – 2x.
0
( f1 , f 2 , f 3 ) / ( x, u, z )
( f1 , f 2 , f 3 ) / ( x, y , z )
4 zx
4 zx
=
=
1 8 xyz
1 8 xyz
=
( f1 , f 2 , f 3 ) / ( x, y, u )
( f1 , f 2 , f3 ) / ( x, y , z )
2x
2x
=
=
1
8 xyz
1 8 xyz
=
=
=
yz
4 z 2 x2
2 x2 y
(1 8 xyz ) 1 8 xyz 1 8 xyz 1 8 xyz
8
2
8 ( yz 4 z 2 x 2 2 x 2 y )
Ans.
(1 8 xyz )3
Example 77. Given, x = u + v + w, y = u2 + v2 + w2, z = u3 + v3 + w3
vw
u
show that
=
(u v ) (u w )
x
Solution. Let
f1 = u + v + w – x = 0
f2 = u2 + v2 + w2 – y = 0
f3 = u3 + v3 + w3 – z = 0
( f1 , f 2 , f 3 ) / ( x, v, w)
u
=
x
( f1 , f 2 , f 3 ) / (u, v, w)
1 1
( f1 , f 2 , f 3 )
= 0 2v
( x, v, w)
0 3 v2
1
1
( f1 , f 2 , f 3 )
= 2u 2v
(u , v, w)
3 u 2 3 v2
Thus from (1), (2) and (3), we get
... (1)
1
1 1
2 w = 6 vw
= 6 vw (v – w)
v w
2
3w
... (2)
1
2 w = 6 (v – u) (w – u) (w – v)
3w
... (3)
2
vw
6 vw (v w)
u
=
=
(u v ) (u w )
x
6 (v u ) ( w u ) ( w v )
Proved.
EXERCISE 1.15
1.
If u2 + xv2 – uxy = 0, v2 – xy2 + 2uv + u2 = 0 find
2.
If x = u + e–v sin u, y = v + e–v cos u, find
3.
u
.
x
Ans.
(v 2 uy ) (u v ) xvy 2
(u v ) (2 u xy 2 xv)
u v e v sin u
u v
.
Ans.
,
y x 1 e 3v
y x
u u v v
(u , v)
;
; ;
and
If x = u2 – v2, y = 2uv, find
x y x y
( x, y )
u
v
v
u
1
Ans.
;
;
;
;
2 (u 2 v3 ) 2 (u 2 v 3 ) 2 (u 2 v 2 ) 2 (u 2 v 2 ) 4 (u 2 v 2 )
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Partial Differentiation
67
u
x
4.
If u3 + xv2 – uy = 0, u2 + xyv + v2 = 0 , find
5.
If u2 + xv2 = x + y, v2 + yu2 = x – y, find
6.
If u = xyz, v = x2 + y2 + z2, w = x + y + z find
7.
If u = x2 + y2 + z2, v = xyz, find
u v
,
x y
x
u
x
u
Ans.
xyv2 2 v3
2 x y u 2 x y2 6 u 2 v 2 v y 4 x u v
Ans.
1 x v2 1 + y + u 2
,
2 u (1 xy ) 2v (1 xy )
Ans.
1
( x y) ( x z)
Ans.
x
2 (2 x 2 y 2 )
1.26 TAYLOR’S SERIES OF TWO VARIABLES
If f (x, y) and all its partial derivatives upto the nth order are finite and continuous for all points
(x, y), where
a x a + h, b y b + k
2
3
1
1
Then f (a + h, b + k) = f (a , b ) h k f h k f h k f ...
x
y
2! x
y
3! x
y
Proof. Suppose that f (x + h, y + k) is a function of one variable only, say x where y is assumed
as constant. Expanding by Taylor’s Theorem for one variable, we have
f (x + x, y + y) = f ( x, y y ) x
f ( x, y y ) (x )2 2 f ( x, y y )
......
x
2! x 2
Now expanding for y, we get
f ( x, y ) (y )2 2 f ( x, y )
f ( x, y )
..... x f ( x, y ) y
......
= f ( x, y ) y y
2
2!
x
y
y
y
2
2
( x )
f ( x, y ) ..... .....
f ( x, y ) y
2! x 2
y
2
2 f ( x, y )
f ( x, y ) ( y )
.
.....
= f ( x, y ) y y
2!
y2
2
f ( x, y )
f ( x, y )
( x ) 2 2 f ( x , y )
x
y.
... ...
2
xy
2! x
x
2
2
f ( x, y)
f ( x , y ) 1
f ( x, y )
2 f ( x, y )
y.
2 x. y.
(x )
= f ( x , y ) x
2
x
y
2!
x y
x
2 f ( x, y )
( y ) 2 .
...
y2
f
f 1 2 2 f
2 f
2 f
f (a + h, b + k) = f (a, b) h
k
2h k
k2
.....
h
2
y 2! x
x y
y2
x
2
1
k
k
f (a + h, b + k) = f (a, b) h
f h
f .....
y
2! x
y
x
On putting a = 0, b = 0, h = x, k = y, we get
2
f
f 1 2 2 f
2 f
2 f
f (x, y) = f (0, 0) x
y
2
x
y
y
x
y 2!
x y
x 2
y2
x
.....
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Partial Differentiation
68
Example 78. Expand ex. sin y in powers of x and y, x = 0, y = 0 as far as terms of third degree.
Solution.
x = 0, y = 0
f (x, y)
ex sin y,
0
fx (x, y)
ex sin y,
0
fy (x, y)
ex
cos y,
1
fxx (x, y)
ex sin y,
0
fxy (x, y)
ex
cos y,
1
fyy (x, y)
– ex sin y,
0
fxxx (x, y)
ex sin y,
0
fxxy (x, y)
ex
cos y,
1
fxyy (x, y)
– ex sin y,
0
fyyy (x, y)
–
ex
cos y,
–1
By Taylor’s theorem
2
1
y
y
f (x, y) = f (0, 0) x
f (0, 0) x
f (0, 0)
y
2! x
y
x
3
1
x
y
f (0, 0) ...
3! x
y
2
x
2 xy
y2
= f (0, 0) x f x (0, 0) y f y (0, 0)
f xx (0, 0)
f xy (0, 0)
f yy (0, 0)
2!
2!
2!
1 3
3x 2 y
3
1
x f xxx (0, 0)
f xxy (0, 0) x y 2 f xyy (0, 0) y 3 f yyy (0, 0) ...
3!
3!
3!
3!
x2
y2
x3
3x 2 y
3xy 2
y3
(0) x y(1) (0) (0)
(1)
(0) (1) ...
2
2
6
6
6
6
x2 y y3
.......
= yxy
Ans.
2
6
Example 79. Find the expansion for cos x cos y in powers of x, y upto fourth order terms.
Solution.
By Taylor’s Series
1
2 2
2
f (x, y) = f (0, 0) x f x (0, 0) y f y (0, 0) 2 ! x f x (0, 0) 2 x y f xy (0, 0) y f yy (0, 0)
1
x3 f x3 (0, 0) 3 x 2 y f x2y (0, 0) 3 xy 2 f x 2y (0, 0) y 3 f y3 (0, 0)
3!
ex sin y = 0 x (0) y (1)
1
x 4 f x4 (0, 0) 4 x3 y f x3 y (0, 0) 6 x 2 y 2 f 2 2 (0, 0) 4 xy 3 f 3 (0, 0) y 4 f 4 (0, 0) + ...
xy
xy
y
4!
1
4
2 2
4
1
1
cos x cos y = 1 0 0 ( x 2 0 y 2 ) (0 0 0 0) ( x 0 6 x y 0 y )
24
2
6
x2 y2 x4 x2 y2 y2
= 1
Ans.
...
2
2 24
4
24
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Partial Differentiation
69
x = 0, y = 0
f (x, y)
fx
fy
fxx
cos x cos y,
– sin x cos y,
– cos x sin y,
– cos x cos y,
1
0
0
–1
fxy
fyy
fxxx
fxx y
sin x sin y,
– cos x cos y,
sin x cos y,
cos x sin y,
0
–1
0
0
fx yy
fyyy
fxxxx
fxxx y
fxx yy
fx yyy
sin x cos y,
cos x sin y,
cos x cos y,
– sin x sin y,
cos x cos y,
– sin x sin y,
0
0
1
0
1
0
fyyyy
cos x cos y,
1
Example 80. Find the first six terms of the expansion of the function ex log (I + y) in a
Taylor’s series in the neighbourhood of the point (0, 0).
Solution.
x = 0, y = 0
x
Taylor’s series is
f (x, y) e log (1 + y)
0
f
f
f
y
f (x, y) = f (0, 0) x
ex log (1 + y)
0
y
x
x
2
1 2 2 f
2 f
2 f
2
x
y
y
x
2 ! x2
x y
y2
...
ex log (1 + y) = 0 ( x 0 y 1)
1 2
[ x (0) 2 xy 1 y 2 ( 1)] ..
2!
2
ex log (1 + y) = y x y
y
2
Ans.
f
y
2 f
x2
2 f
y
2
2 f
x y
ex
1 y
1
ex log (1 + y)
0
ex
(1 y )2
ex
(1 y )
–1
1
EXERCISE 1.16
1 2
( x y 2 ) ........
2
cos 3y in power series of x and y upto quadratic terms.
(AMIE Summer 2004)
9 2
2
Ans. 1 2 x 2 x y ...
2
1.
Expand ex cos y at (0, 0) upto three terms.
2.
Expand z = e2x
3.
Show that ey log (1 + x) = x xy –
4.
Verify sin (x + y) = x y
Ans. 1 x
x2
approximately..
2
( x y )3
.......
3
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Partial Differentiation
70
Example 81. Expand sin (xy) in powers of (x – 1) and y as far as the terms of second
2
degree.
(Nagpur University, Summer 2003)
Solution. We have, f (x,y) = sin (xy)
x = 1, y =
2
a h x and h x 1
Here
a ( x 1) x a 1
f (x, y)
sin (x y)
1
b k y and k y 2
b y y b
2
2
By Taylor’s theorem for a function of
two variables, we have
f (a + h, b + k) = f (a, b) + hfx (a, b)
fx (x, y)
y cos (xy),
0
fy(x, y)
x cos (xy),
0
fxx (x, y)
– y2 sin (xy),
fxy (x, y)
cos (xy) – xy sin (xy),
fyy (x, y)
– x2 sin (xy),
2
4
2
–1
+ kfy (a, b)
1 2
h f xx (a, b ) 2hkf xy (a , b) k 2 f yy (a, b )
2!
f (x, y)= f 1, ( x 1) f x 1, y f y 1,
2
2
2 2
2
1
( x 1)2 f xx 1, 2 ( x 1) y f xy 1 , y f yy (1, )
2!
2
2
2
2
2
sin (xy) = 1 ( x 1) . 0 y . 0
2
2
2
1
2
(
x
1)
2
(
x
1)
y
y
( 1) ...
2!
2 2
2
4
2
2
1
( x 1)2 ( x 1) y y ...
8
2
2 2
2
x
Example 82. Expand e cos y near the point 1, by Taylor’s Theorem.
4
(U.P., I Semester Dec. 2007)
k
Solution. f (x + h, y + k) = f (x, y) h
f
x
y
f (x, y) ex cos y
sin (xy) = 1
2
3
1
1
h
k
k
f h
f ....
2! x
y
3! x
y
ex cos y = f (x, y)= f 1 ( x 1), y
4
4
where h = x – 1, k = y = f 1 h, k
4
4
Putting these values in Taylor’s Theorem, we get
e
e
e
( x 1)
y
ex cos y =
4 2
2
2
f
x
f
y
2 f
x 2
2 f
y 2
2 f
x y
ex cos y,
– ex sin y,
ex cos y,
– ex cos y,
–1 ex sin y,
Ans.
x = 1, y =
e
4
2
e
2
e
2
e
2
e
2
e
2
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Partial Differentiation
71
2
1
e
e
e
( x 1)2
2( x 1) y
y
....
2!
4 2
4 2
2
2
2
e
( x 1)
( x 1) y y ....
1 ( x 1) y
=
Ans.
4
2
4
4
2
Example 83. If f (x, y) = tan–1 (x y), compute an approximate value of f (0.9, – 1.2).
Solution. We have,
f (x, y) = tan–1 (x y)
Let us expand f (x, y) near the point (1, – 1)
f (0.9, – 1.2) = f (1 – 0.1, – 1 – 0.2)
2
f
f 1
2 f
( 0.2)
= f (1, 1) ( 0.1)
( 0.1)
x
y 2 !
x 2
2 ( 0.1) ( 0.2)
f (x, y) tan–1 (xy)
f
x
f
y
2 f
x
2
2 f
y x
y
1 x2 y2
x
1 x2 y2
,
(1 x 2 y 2 ) 2
(1 x 2 y 2 )2
2 2 2
(1 x y )
Substituting the values of
1
2
1
2
,
1 x 2 y 2 x (2 x y 2 )
x (2 x 2 y )
...(1)
1
2
(2 x) y
2
y
,
2 f
2 f
2 f
( 0.2)2
...
x y
y 2
x = 1, y = – 1
4
1 x2 y2
(1 x 2 y 2 )2
0
1
2
,
f f
,
etc. in (1), we get
x y
f (0.9, – 1.2) =
1
1 1
1
( 0.1) ( 0.2) ( 0.1) 2
4
2
2 2
2
1
2 ( 0.1) ( 0.2) 0 ( 0.2) 2 ...
2
=
22
1
0.05 0.1 (0.005 0.02)
28
2
= 0.786 0.05 0.1 0.0125 0.8235
Ans.
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Partial Differentiation
72
Example 84. Obtain Taylor’s expansion of tan–1
degree terms. Hence compute f (1. 1, 0.9).
Solution.
f (x, y)
f
x
f
y
2 f
x
2
2 f
y
2
2 2
2
2
(x y )
2
f
y x
x = 1, y = 1
4
y
tan
x
1 y
y
2 2
,
2
y x x y2
1 2
x
1 1
x
2
,
2
y x x y2
1 2
x
y (2 x )
2 xy
2
,
2
2 2
(x y )
( x y 2 )2
1
x (2 y )
2
y
about (1, 1) upto and including the second
x
(U.P., I Sem. Winter 2005, 2002)
2 xy
2
(x y )
( x y ) ( x ) (2 x )
2
2 2
2 2
(x y )
1
2
1
2
,
y2 x2
( x2 y2 )2
1
2
,
1
2
0
By Taylor’s Theorem
2
f
f 1
2 f
( y b)
(
x
a
)
f (x, y) = f (a, b) ( x a)
x
y 2!
x2
2
f
2 f
2 ( x a ) ( y b)
( y b) 2
...
x y
y2
Here,
a = 1, b = 1
1 1
1
1
1 y
( x 1) 2
tan
= ( x 1) ( y 1)
4
2 2!
x
2
2
1
2 ( x 1) ( y 1) (0) ( y 1) 2 ...
2
1
1
1
1
y
tan 1
= ( x 1) ( y 1) ( x 1)2 ( y 1)2 ...
...(1)
x
4 2
2
4
4
Putting (x – 1) = 1.1 – 1 = 0.1, (y – 1) = 0.9 – 1 = – 0.1 in (1), we get
f (1.1, 0.9) =
1
1
1
1
(0.1) ( 0.1) (0.1)2 ( 0.1)2
4 2
2
4
4
= 0.786 – 0.05 +0.05 + 0.0025 – 0.0025 = 0.786
Ans.
( x h) ( y k )
in powers of h, k upto and inclusive of the second
xh yk
degree terms.
(A.M.I.E., Summer 2001)
( x h) ( y k )
Solution.
f (x + h, y + k) =
xh yk
xy
f (x, y) =
x y
Example 85. Expand
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Partial Differentiation
73
y2
( x y) y x y
f
=
=
x
( x y )2
( x y) 2
x2
( x y) x x y
f
=
=
( x y) 2
y
( x y )2
2 f
x2
=
2 y2
( x y )3
( x y )2 2 x 2 ( x y ) x 2 ( x y ) 2 x 2 x 2
2x y
2 f
=
4
3
y x
(x y)
(x y)
( x y )3
2 f
y2
=
2x2
( x y )3
2
1
k
k
f (x + h, y + k) = f ( x , y ) h
f ( x, y) h
f ( x, y ) ...
y
2! x
y
x
xy
y2
x2
( x h) ( y k )
h
k
=
x y
xh yk
( x y) 2
( x y )2
h2 ( 2 y 2 ) 1
2x y
1 2 ( 2 x 2 )
2
h
k
k
...
2 ! ( x y )3 2 !
( x y )3 2 !
( x y )3
xy
h y2
kx 2
h2 y2
2h kxy
k 2 x2
... Ans.
2
2
3
3
x y ( x y)
( x y)
( x y)
(x y)
( x y )3
Example 86. Expand x2y + 3y – 2 in powers of x – 1 and y + 2 using Taylor’s Theorem.
(A.M.I.E.T.E., Winter 2003, A.M.I.E., Summer 2004, 2003)
Solution. f (x, y) = x2y + 3 y – 2
Here
a + h = x and h = x – 1, so a = 1
b + k = y and k = y + 2 so b = – 2
x = 1, y = – 2
=
f (x, y)
x2y + 3y – 2,
fx (x, y)
2xy,
fy (x, y)
x2 + 3,
– 10
–4
4
fxx (x, y)
2 y,
–4
fxy (x, y)
2 x,
2
fyy (x, y)
0,
0
fxxx (x, y)
0,
0
fxxy (x, y)
2,
2
fxyy (x, y)
0,
0
fyyy (x, y)
0,
0
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Partial Differentiation
74
Now Taylor’s Theorem is
f
f
1 2 2 f
2 f
k
2
h
k
k2
h
f (a + h, b + k) = f (a, b) h
y ( a, b ) 2 ! x 2
x y
x
1 3 f
3 f
2 f
2
2
h3
3
h
k
3
h
k
k3
3! x3
x2 y
x y2
Putting the values of f (a, b) etc. in Taylor’s Theorem, we get
2 f
y 2 (a, b)
3 f
...
y 3
x2y + 3y – 2 = – 10 + [(x – 1) (– 4) + (y + 2) (4)]
1
[(x – 1)2 (– 4) + 2 (x – 1) (y + 2) (2) + (y + 2)2 (0)]
2!
1
[(x – 1)3 (0) + 3 (x – 1)2 (y + 2) (2) + 3 (x – 1) (y + 2)2 (0) + (y + 2)3 (0)]
3!
x2y + 3y – 2 = – 10 – 4 (x – 1) + 4 (y + 2) – 2 (x – 1)2 + 2 (x – 1) (y + 2) + (x – 1)2 (y + 2)Ans.
EXERCISE 1.17
exy
1.
Expand
at (1, 1) upto three terms.
2.
3.
1
[( x 1) 2 4 ( x 1) ( y 1) ( y 1)2 ]
2!
Expand yx at (1, 1) upto second term
Ans. 1 + (y – 1) + (x – 1) (y – 1) + .......
Expand eax sin by in powers of x and y as far as the terms of third degree.
(U.P. I sem. Jan 2011)
Ans. e [1 ( x 1) ( y 1)
Ans. by abxy
4.
Expand
(x2y
+ sin y +
ex)
1
3a 2 bx 2 y – b3 y 3 ....
3!
in powers of (x – 1) and (x – ).
1
( x 1) 2 (2 e) 2 ( x 1) ( y ).
2
x 1 ( x 1) 2 y 2
...
Ans. 2 1
4
32
4
Ans. e ( x 1) (2 e)
5.
Expand (1 + x + y2)1/2 at (1, 0).
6.
Obtain the linearised form T(x, y) of the function f (x, y) = x2 – xy +
7.
8.
1 2
y + 3 at the point (3, 2), using
2
the Taylor’s series expansion. Find the maximum error in magnitude in the approximation
f (x, y) T (x, y) over the rectangle R: | x – 3 | < 0.1, | y – 2 | < 0.1.
Ans. 8 + 4 (x – 3) – (y – 2)., Error 0.04.
Expand sin (x + h) (y + k) by Taylor’s Theorem.
1
Ans. sin xy h ( x y ) cos xy hk cos xy h 2 ( x y ) 2 sin xy ...
2
Fill in the blank:
f (x, y) = f (2, 3) + ............ Ans. ( x 2)
9.
If f (x) = f (0) kf1 (0)
given as ..........
2
( y 3) f + 1 ( x 2) ( y 3) f ...
x
y
2!
x
y
k2
f 2 (k ), 0 1 then the value of when k = 1 and f(x) = (1 – x)3/2 is
2!
(U.P. Ist Semester, Dec 2008)
1.27 MAXIMUM VALUE
A function f (x, y) is said to have a maximum value at x = a, y = b, if there exists a small
neighbourhood of (a, b) such that,
f (a, b) > f (a + h, b + k)
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Partial Differentiation
75
Minimum Value. A function f (x, y) is said to have a minimum value for x =a, y = b, if there
exists a small neighbourhood of (a, b) such that
f (a, b) < f (a + h, b + k)
The maximum and minimum values of a function are also called extreme or extremum values
of the function.
f(x, y)
f(x, y)
Maximum
Minimum
O
O
Y
X
Y
X
Maximum value of
f(x, y) at (a, b)
Minimum value of
f(x, y) at (a, b)
Saddle point or Minimax. It is a point where a function is neither maximum nor minimum.
Geometrical Interpretation. Such a surface (looks like the leather seat on the back of a
horse) forms a ridge rising in one direction and falling in another direction.
1.28 CONDITIONS FOR EXTREMUM VALUES
If f (a + h, b + k) – f (a, b) remains of the same sign for all values (positive or negative) of h,
k then f (a, b) is said to be extremum value of f (x, y) at (a, b)
(i) If f (a + h, b + k) – f (a, b) < 0, then f (a, b) is maximum.
(ii) If f (a + h, b + k) – f (a, b) > 0, then f (a, b) is minimum.
By Taylor’s Theorem
2
f
f
1 2 f
2 f
2 f
k
h2
2
hk
k
...
y ( a , b ) 2! x 2
x y
y 2
x
f (a + h, b + k) = f (a, b) + h
f
f
1
2 f
2 f
2 f
h2
2hk
k2
...(1)
f (a + h, b + k) – f (a, b) = h x k y
2
2!
x
y
x
y 2
( a , b )
f (a + h, b + k) – f (a, b) = h
f
f
k
x
y ( a , b )
...(2)
For small values of h, k, the second and higher order terms are still smaller and hence may be
neglected.
The sign of L.H.S. of (2) is governed by h f k f which may be positive or negative
x
y
depending on h, k.
Hence, the necessary condition for f (a, b) to be a maximum or minimum is that
f
f
f
f
k
= 0,
=0
h
=0
y
y
x
x
By solving the equations, we get, point x = a, y = b which may be maximum or minimum
value.
Then from (1)
f (a + h, b + k) – f (a, b) =
2
1 2 2 f
2 f
2 f
2
hk
k
h
2! x 2
x y
y 2
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Partial Differentiation
76
=
1 2
[h r 2 h k s k 2 t ]
2!
...(3)
2
where r =
f
x2
2
,s
2
f
f
,t
at ( a, b )
x y
y2
Now the sign of L.H.S. of (3) is sign of [rh2 + 2hks + k2 t]
1 2 2
[ r h 2hkrs k 2 rt ] = sign of 1 [(r 2 h 2 2 hkrs k 2 s 2 ) ( k 2 s 2 k 2rt )]
r
r
1
2
2
2
= sign of [(hr ks ) k ( rt s )]
r
1
2
2
= sign of [(always + ve) k ( rt s )]
[(hr + ks)2 = + ve]
r
1 2
2
= sign of [ k (rt s )] = sign of r if rt – s2 > 0
r
= sign of
Hence, if rt – s2 > 0, then f (x, y) has a maximum or minimum at (a, b), according as r < 0 or
r > 0.
Note: (i) If rt – s2 < 0, then L.H.S. will change with h and k hence there is no maximum or
minimum at (a, b), i.e., it is a saddle point.
(ii) If rt – s2 = 0, then rh2 + 2shk + tk2 =
1
[(rh sk ) 2 k 2 ( rt s 2 )]
r
1
( rh sk ) 2 which is zero for values of h, k, such that
r
h
s
=
k
r
=
This is, therefore, a doubtful case, further investigation is required.
1.29 WORKING RULE TO FIND EXTREMUM VALUES
(i)
f
f
2 f
2 f
2 f
,
,
Differentiate f (x, y) and find out x , y ,
x2 x y y 2
(ii) Put
f
f
= 0 and y = 0 and solve these equations for x and y. Let (a, b) be the values
x
of (x, y).
(iii) Evaluate r =
2 f
,s
2 f
2 f
, t 2 for these values (a, b).
x y
y
x 2
(iv) If rt – > 0 and
(a) r < 0, then f (x, y) has a maximum value.
(b) r > 0, then f (x, y) has a minimum value.
(v) If rt – s2 < 0, then f (x, y) has no extremum value at the point (a, b).
(vi) If rt – s2 = 0, then the case is doubtful and needs further investigation.
s2
Note: The point (a, b) which are the roots of
f
f
0,
= 0, are called stationary points.
x
y
Example 87. Discuss the maximum and minimum of x2 + y2 + 6x + 12.
Solution. We have,
f (x, y) = x2 + y2 + 6x + 12
f
f
2 f
2 f
2 f
2 y, 2 2, 2 2,
0
= 2 x 6,
x
y
x y
x
y
For maxima and minima,
f
f
= 0 and
=0
y
x
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Partial Differentiation
77
At
2x + 6 = 0, and 2y = 0
x = – 3, and y = 0
(– 3, 0)
rt – s2 = 2 × 2 – 0 = 4 > 0
r=2>0
Hence f (x, y) is minimum when x = – 3 and y = 0
Minimum value = f (– 3, 0) = 9 + 0 – 18 + 12 = 3
Ans.
Example 88. Find the absolute maximum and minimum values of
f (x, y) = 2 + 2x + 2y – x2 – y2
on triangular plate in the first quadrant, bounded by the lines x = 0, y = 0 and y = 9 – x.
(Gujarat, I semester, Jan. 2009)
2
2
Solution. We have,
f (x, y) = 2 + 2x + 2y – x – y
f
f
= 2 – 2x,
= 2 – 2y
x
y
2 f
x 2
= – 2,
For maxima and minima,
2 f
2 f
0,
2
xy
y 2
f
= 0 2 – 2x = 0 x = 1
x
f
= 0 2 – 2y = 0 y = 1
y
rt – s2 = (– 2) (– 2) – 0 = 4 > 0
At (1, 1)
Hence f (x, y) is maximum at (1, 1).
Maximum value of f (x, y) = 2 + 2 + 2 – 1 – 1 = 4
Ans.
3
3
Example 89. Examine f (x, y) = x + y – 3 a x y for maximum and minimum values.
(U.P. I Sem., Dec. 2004), (M.U. 2004, 2003)
3
3
Solution. We have,
f (x, y) = x + y – 3axy
p=
r=
f
= 3x2 – 3ay,,
x
2 f
x 2
6 x,
q=
f
= 3y2 – 3ax
y
2 f
2 f
3
a
,
t
6y
s = xy
y 2
For maxima and minima
f
=0
y
3y2 – 3 ax = 0
f
=0
x
3x2 – 3 ay = 0.
x2 = ay y =
x2
a
and
...(1)
y2 = ax
...(2)
Putting the value of y from (1) in (2), we get
x4 = a3x x(x3 – a3) = 0
2
x(x – a)(x + ax + a2) = 0
x = 0, a
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Partial Differentiation
78
Putting x = 0 in (1),
Putting x = a in (1),
we get y = 0,
we get y = a,
Stationary pairs
(0, 0)
(a, a)
r
0
6a
s
– 3a
– 3a
t
0
6a
rt – s2
–9a2 < 0
27 a2 > 0
At (0, 0) there is no extremum value, since rt – s2 < 0.
At (a, a), rt – s2 > 0, r > 0
Therefore (a, a) is a point of minimum value.
The minimum value of f (a, a) = a3 + a3 – 3 a3 = – a3
Example 90. Show that the function
f (x,y) = x3 + y3 – 63 (x + y) + 12 xy
is maximum at (– 7, – 7) and minimum at (3, 3).
Solution. We have,
f (x,y) = x3 + y3 – 63 (x + y) + 12 xy
f
= 3x2 – 63 + 12y,
x
2 f
x 2
2 f
12,
= 6x,
xy
Ans.
...(1)
f
= 3y2 – 63 + 12x
y
2 f
y 2
6y
For extremum, we have
f
= 3x2 – 63 + 12y = 0
x
f
q=
= 3y2 – 63 + 12x = 0
y
p=
x2 + 4y – 21 = 0
...(2)
y2 + 4x – 21 = 0
...(3)
2 f
2 f
2 f
12
6y
6
x
s
=
t
=
xy
y 2
x 2
We have to solve (2) and (3) for x and y.
On subtracting (3) from (2), we have
x2 – y2 – 4 (x – y) = 0
(x – y) (x + y – 4) = 0
x = y and x + y = 4
...(4)
2
If x = y then (2) becomes, x + 4x – 21 = 0, (x + 7)(x – 3) = 0
x = – 7, and x = 3
y = – 7, and y = 3
Two stationary points are (–7, –7) and (3, 3).
On solving (2) and (4), we get
x2 + 4 (4 – x) – 21 = 0, x2 – 4x – 5 = 0
(x – 5) (x + 1) = 0
x = – 1, x = 5
y = 5, y = – 1
Two more stationary points are (–1, 5) and (5, –1).
Hence four possible extremum points of f (x, y) are (– 7, – 7), (3, 3), (–1, 5) and (5, – 1)
may be.
r=
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Partial Differentiation
79
Stationary pairs
r = 6x
– 42
(– 7, – 7)
+ 18
(3, 3)
–6
(– 1, 5) (5, – 1)
30
s = 12
12
12
12
12
t=6y
r t – s2
– 42
+ 1620
18
+ 180
30
– 324
–6
– 324
At (– 7, – 7)
r = – ve, and rt – s2 = + ve
Hence, f (x, y) is maximum at (– 7, – 7),
At (3, 3)
r = + ve, and rt – s2 = + ve
Hence, f (x, y) is minimum at (3, 3).
Proved.
2
2
2
2
Example 91. Find the extreme values of u = x y – 5x – 8xy – 5y .
Solution. We have,
u
u = x2 y2 – 5x2 – 8xy – 5y2
p =
= 2xy2 – 10x – 8y
x
u
2u
q =
= 2x2 y – 8x – 10y
r =
= 2y2 – 10
y
x 2
2u
2u
4 xy 8
s =
t =
= 2x2 – 10
y 2
x y
u
u
= 0,
=0
y
x
For extreme values of u,
2xy2 – 10x – 8y = 0 x =
8y
2
2 y 10
2x2 y – 8x – 10y = 0
2
8y
8y
2 2
y 8 2
10 y 0
2 y 10
2 y 10
128 y 2
2
Now,
64
16 y 2
2
2
4y
y2 5
If y = 1 then x = – 1;
If y = 3 then x = 3;
10 0, y = 0 then x = 0
2
2
16
50
( y 5)
y 5
(2 y 10)
2 y 10
16 y2 – 16 (y2 – 5) – 5 (y2 – 5)2 = 0
16 y2 – 16 y2 + 80 – 5 (y2 – 5)2 = 0
(y2 – 5)2 = 16 y2 – 5 = ± 4 y2 = 9 and 1 and y = ± 3, ± 1
2
x =
If y = – l then x = 1
If y = – 3 then x = – 3
Stationary pairs
(0, 0)
(1, – 1)
(– 1, 1)
(3, 3)
(– 3, – 3)
r = 2 y2 – 10
– 10
–8
–8
8
8
s=4xy–8
–8
– 12
– 12
28
28
t = 2 x – 10
– 10
–8
–8
8
8
rt – s2
+ 36
– 80
– 80
– 720
– 720
2
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Partial Differentiation
80
At (0, 0),
r is – ve.
Origin (0, 0) is the only point at which r t – s2 > 0.
Hence, the function u is maximum at origin.
Ans.
Example 92. A rectangular box, open at the top, is to have a volume of 32 c.c. Find the
dimensions of the box requiring least material for its construction.
(M.U. 2009; U.P. I semester Jan. 2011; Dec. 2005, A.M.I.E Summer 2001)
Solution. Let l, b and h be the length, breadth, and height of the box respectively and S its
surface area and V the volume.
V = 32 c.c.
32
lh
S = 2 (l + b) h + l b
Putting the value of b in (1), we get
32
32
S = 2 l h l
lh
lh
64 32
S = 2lh+
l
h
Differentiating (2) partially w.r.t. l, we get
S
64
= 2h– 2
l
l
Differentiating (2) partially w.r.t. h, we get
l b h = 32 b =
S
32
= 2l– 2
h
h
For maximum and minimum S, we get
S
64
= 0 2h– 2 =0
l
l
S
32
= 0 2l– 2 =0
h
h
From (5) and (6), l = 4, h = 2 and b = 4
2 S
128 128
=
=2
l2
l3
64
2 S
= 2
l h
...(1)
...(2)
...(3)
...(4)
h=
32
l2
...(5)
16
h2
...(6)
l=
2 S
64 64
8
= 3
2
h
h
8
2
2 S 2 S 2 S
.
= (2) (8) – (2)2 = + 12
l 2 h2 l h
2 S
= + 2, so S is minimum for l = 4, b = 4, h = 2
l2
Ans.
EXERCISE 1.18
Find the stationary points of the following functions
1. f (x, y) = y2 + 4 xy + 3 x2 + x3
2 4
Ans. , , Minimum
3 3
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Partial Differentiation
81
1 1
[A.M.I.E., Summer 2004] Ans. , , Maximum
2 3
2. f (x, y) = x3 y2 (1 – x – y)
3. f (x, y) = x3 + 3 xy2 – 15 x2 – 15 y3 + 72x. (M.U. 2007, 2005, 2004) Ans. (6, 0), (4, 0)
4. f (x, y) = x2 + 2xy + 2y2 + 2x + 3y such that x2 – y = 1.
5. xy e– (2x
+ 3y)
7 155
3
.
Ans. , ,
4
16
128
(A.M.I.E., Winter 2000)
2
2
6. Find the extreme value of the function f (x, y) = x + y + xy + x – 4y + 5.
State whether this value is a relative maximum or a relative minimum.
Ans. Minimum value of f (x, y) at (– 2, 3) = – 2.
2
2
7. Find the values of x and y for which x + y + 6 x = 12 has a minimum value and find this minimum
value.
Ans. (– 3, 0), 3.
8. Find a point within a triangle such that the sum of the square of its distances from the three angular
points is a minimum.
9. A tapering log has a square cross-section whose side varies uniformly and is equal to a at the top
3a
and b b at the bottom. Show that the volume of the greatest conical frustum that can be
2
b3l
obtained from the log is
, where l is the length of the log.
27(b a )
10. A tree trunk of length l metres has the shape of a frustum of a circular cone with radii of its ends
a and b metres where a > b. Find the length of a beam of uniform square cross section which can
be cut from the tree trunk so that the beam has the greatest volume.
Ans.
8a 3 l
27( a b)
1.30 LAGRANGE METHOD OF UNDETERMINED MULTIPLIERS
Let f (x, y, z) be a function of three variables x, y, z and the variables be connected by the
relation.
(x, y, z) = 0
...(1)
f (x, y, z) to have stationary values,
f
f
f
= 0, y = 0,
= 0
x
z
f
f
f
dx dy dz = 0
x
y
z
dx
dy
dz = 0
x
y
z
Multiplying (3) by and adding to (2), we get
By total differentiation of (1), we get
...(2)
...(3)
f
f
f
dx dy
dy dz
dz = 0
dx
x y
y z
z
x
f
f
dx
x
x
y
This equation will hold good if
f
x
x
f
y
y
f
z
z
f
dy
dz = 0
y
z
z
= 0
...(4)
= 0
...(5)
= 0
...(6)
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Partial Differentiation
82
On solving (1), (4), (5), (6), we can find the values of x, y, z and for which
f (x, y, z) has stationary value.
Draw Back in Lagrange method is that the nature of stationary point cannot be determined.
Example 93. Find the point upon the plane ax + by + cz = p at which the function
f = x2 + y2 + z2
(Nagpur University, Winter 2000)
has a minimum value and find this minimum f.
Solution. We have,
f = x2 + y2 + z2
...(1)
ax + by + cz = p
= ax + by + cz – p
...(2)
f
= 0
x
x
2x + a = 0
x =
a
2
f
= 0
y
y
2y + b = 0
y =
b
2
z =
c
2
f
= 0
2z+c=0
z
z
Substituting the values of x, y, z in (2), we get
a b c
a
b
c
= p
2 2 2
(a2 + b2 + c2) = – 2p
x =
The minimum value of
f =
=
ap
2
2
a b c
a2 p2
2
2 p
=
, y
(a 2 b2 c 2 )2
p 2 (a 2 b2 c 2 )
2
a b2 c 2
bp
2
2
a b c
b2 p 2
2
, z
(a 2 b 2 c 2 )2
cp
2
a b2 c 2
c2 p 2
(a 2 b 2 c 2 )2
p2
Ans.
(a 2 b2 c 2 ) 2
a 2 b2 c 2
p q r
Example 94. Find the maximum value of u = x y z when the variables x, y, z are subject
to the condition ax + by + cz = p + q + r.
Solution. Here, we have
u = xp yq zr
...(1)
If
log u = p log x + q log y + r log z
...(2)
1 u
p
=
u x
x
u pu
x
x
1 u
q
=
uy
y
u qu
y
y
1 u
u ru
r
=
u z
z
z
z
ax + by + cz = p + q + r
(x, y, z) = ax + by + cz – p – q – r
=
a,
= b,
=c
x
y
z
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Partial Differentiation
83
Lagranges equations are
u
= 0
x
x
u
= 0
y
y
pu
a = 0
x
qu
b = 0
y
u
= 0
z
z
ru
c = 0
z
x =
pu
a
y =
qu
b
z =
ru
c
Putting in (2), we have
pu qu ru
= p+q+r
u
u
( p q r) = p + q + r = 1
pu pu p
x =
a ua a
qu qu q
y =
b ub b
ru ru r
z =
c uc c
Putting in (1), we have
p
q
=–u
r
p q r
u =
Ans.
a b c
Example 95. Show that the rectangular solid of maximum volume that can be inscribed in
a sphere is a cube.
Solution. Let 2x, 2y, 2z be the length, breadth and height of the rectangular solid.
Let R be the radius of the sphere.
Volume of solid V = 8 x . y . z
...(1)
2
2
2
2
x +y +z = R
....(2)
z
(x, y, z) = x2 + y2 + z2 – R2 = 0
Maximum value of
V
+
= 0
x
x
From (3)
From (4)
From (5)
y
8yz + (2x) = 0
z
V
= 0
y
y
8x z + (2 y) = 0
...(4)
V
z
z
8xy + (2 z) = 0
...(5)
= 0
2x
2y
2z
2 x2
x2
x
Hence, rectangular solid
=
=
=
=
=
=
is
y
...(3)
O
– 8y z
– 8x z
– 8x y
2 y2 = 2 z2
y2 = z2
y= z
a cube.
y
R
x
2
2 x = – 8x y z
2 y2 = – 8x y z
2 z2 = – 8 x y z
Proved.
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Partial Differentiation
84
Example 96. A rectangular box, which is open at the top, has a capacity of 256 cubic feet.
Determine the dimensions of the box such that the least material is required for the construction
of the box. Use Lagrange’s method of multipliers to obtain the solution.
Solution. Let x, y, z be the length, breadth and height of the box.
Volume = xyz = 256 xyz – 256 = 0
...(1)
(x, y, z) = x y z – 256
Let S be the material surface of the box.
x
S = xy+2yz+2zx
y
S
=
y
+
2z
and
= yz
x
x
z
S
= x + 2z
and
= xz
y
y
S
z
= 2y + 2x
and
= xy
z
By Lagrange’s method of multiplier, we have
S
x
S
y
= 0
x
= 0
y
y + 2z + yz = 0
...(2)
x + 2z + xz = 0
...(3)
S
= 0
2y + 2x + xy = 0
z
z
Multiplying (2) by x, we get
xy + 2 xz + xyz = 0
xy + 2 xz + 256 = 0
(xyz = 256)
xy + 2 xz = – 256
Multiplying (3) by y, we get
xy + 2 yz + xyz = 0
xy + 2 yz + 256 = 0
xy + 2 yz = – 256
Multiplying (4) by z, we get
2 yz + 2 xz + xyz = 0
2 yz + 2 xz + 256 = 0
2 yz + 2 zx = – 256
From (5) and (6), we have
xy + 2 xz = xy + 2 yz
2 xz = 2 yz x = y
From (6) and (7), we have
xy + 2 yz = 2 yz + 2 xz
xy = 2 xz y = 2z
From (1)
xyz = 256
y
(y) (y) = 256 y3 = 512 y = 8
2
x = 8, y = 8, z = 4
Hence, length = breadth = 8, height = 4.
...(4)
...(5)
...(6)
...(7)
Ans.
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Partial Differentiation
85
Example 97. Use the method of the Lagrange’s multipliers to find the volume of the largest
x2 y 2 z2
rectangular parallelopiped that can be inscribed in the ellipsoid 2 2 2 1 .
a
b
c
(Nagpur Univesity, Summer 2008, Winter 2003)
(A.M.I.E.T.E., Summer 2004, U.P., I Semester, Winter 2002, 2000)
x2 y 2 z2
Solution. Here, we have
= 1
a 2 b2 c 2
x2 y 2 z2
(x, y, z) = 2 2 2 1 0
...(1)
a
b
c
Let 2x, 2y, 2z be the length, breadth and height of the rectangular parallelopiped inscribed in
the ellipsoid.
V = (2x) (2y) (2z) = 8 xyz
V
V
V
= 8 yz;
8xz,
8 xy
x
y
z
2x
= a2 ,
x
2 y
,
y b 2
2 z
z c 2
Lagrange’s equations are
V
x
x
V
y
y
V
z
z
Multiplying (1), (2) and (3)
2x
= 0
a2
2y
= 0
8 xz + 2 = 0
b
2z
= 0
8 xy + 2 = 0
c
by x, y, z respectively and adding, we get
= 0
8 yz +
x2 y 2 z 2
x2 y 2
24 xyz 2 2 2 2 = 0
2 2
b
c
b
a
a
24 xyz + 2 (1) = 0
= – 12 xyz
Putting the value of in (1), we get
2x
3x 2
8 yz + (– 12 xyz) 2 = 0
1– 2 =0
x=
a
a
Similarly from (2) and (3), we have
b
c
, z
y =
3
3
Volume of the largest rectangular parallelopiped = 8 xyz
...(1)
...(2)
...(3)
z2
1
c2
a
3
a b c 8 abc
= 8
Ans.
3 3 3 3 3
Example 98. The shape of a hole pored by a drill is a cone surmounted by cylinder. If the
cylinder be of height h and radius r and the semi-vertical angle of the cone be , where
h
show that for a total height H of the hole, the volume removed is maximum if
r
h = H ( 7 1) / 6.
(R.G.P.V., Bhopal I sem. 2003)
tan
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Partial Differentiation
86
Solution. Let ABCD be the given cylinder of height ‘h’ and radius ‘r’ and DPCO be the cone
of course, of radius r.
Now, since is the semi-vertical angle of the cone.
PC
r
OP OP
h
but, given that
tan =
...(2)
r
h
r
r2
From (1) and (2), we have
OP =
...(3)
r OP
h
Total height of the hole = H
H = h + OP OP = H – h
...(4)
From (3) and (4), we have
r2
=H–h
...(5)
h
2
r
Again, let
=H–h–
...(6)
h
In drilling a hole, the volume of the removed portion
2r
r2
= –
,
= 1 2
r
h
h
h
V = Volume of the cylinder + Volume of the cone.
1 2
1 2 r2
2
2
= r h r (OP ) r h r .
3
3
h
4
r
V = r2h
,
3h
V
4r 3
2rh
r
3h
By Lagrange’s Method
tan =
V
= 0
r
r
2r h
A
B
h
H
D
P r
r2
1 2
h
2r 2
6 r4
2r 2
2h
= 0
3h
h
h
2 r4 + (– 4r2 + 2 h2) = 0
r4 + (– 2 r2 + h2) = 0
C
O
[From (3)]
4 r3
2r
= 0
3h
h
V
r4
= 0
r2
h
h
3 h2
Multiplying (8) by r and (9) by 2h, we get
2r 2
4 r4
2r 2 h
= 0
3h
h
2 r4
r2
2r 2 h
2 h = 0
3h
h
On subtracting, we get
...(1)
= 0
...(7)
...(8)
...(9)
4 r2
6 r4
2h = 0
3h
h
=
r4
h2 2 r 2
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Partial Differentiation
87
Putting the value of in (8), we get
r2
2r
4 r3 r 4
2 r2
r4
H h
2
h
=
0
=
0
2
3h
3 h h (h 2 2 r 2 )
h
h 2r h
2
2
2
2
2
(H h 2 h H )
2
h ( H h)
h ( H h)
= 0 h ( H h)
=0
2
3
3
h 2 H 2h
h [h 2h ( H h)]
2r h
2
H 2 h 2 2hH
( H h)
=0
3
3h 2 H
2
3h2 – 2H h + (H – h) (3 h – 2 H) + H2 + h2 – 2 h H = 0
3
9h2 – 6H h + 6H h – 4H2 – 6h2 + 4Hh + 3H2 + 3h2 – 6h H = 0
6h2 – 2h H – H2 = 0
h
2 H 4 H 2 24 H 2
12
H H 7
[ 7 1]
h =
=H
(–ve is not possible)
Proved.
6
6
Example 99. A tent of a given volume has a square base of side 2a, has its four-side vertical
of length b and is surmounted by a regular pyramid of height h. Find the values of a and b
in terms of h such that the canvas required for its construction is minimum.
h =
Solution. Let V be the volume and S be the surface of the tent.
1
V = 4 a 2b (4 a 2 ) h
3
1
Area of the base × height]
3
1
S = 8 a b 4 a a2 h2
[Surface Area of pyramid =
perimeter × slant height]
2
S
V
= 0
a
a
From (2)
From (3)
[Volume of pyramid =
8b 4 a 2 h 2
4 a2
8a h
8 a b
= 0
3
a h
...(1)
8 a + 4 a2 = 0
...(2)
2
2
S
V
b
b
= 0
S
V
h
h
= 0
a+2 = 0
12ah 4a 2 a 2 h 2
= 0
3h a a 2 h 2
= 0
4 ah
4
a2 = 0
3
a h
a = –2
2
2
...(3)
...(4)
...(5)
Substituting the value of a from (4) in (5), we get
3h 2 a 2 h2
= 0
a =
9 h2 = 4a2 + 4h2 4a2 = 5h2
5
h
2
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Partial Differentiation
88
5
h in (1) and simplifying, we get
2
Substituting a = – 2 and a =
8b 4
5h 2
h2
4
5h 2
2
8h
2 8 b = 0
3
5h
h2
4
10 h
16 h
h
8 b + 6h +
– 16 b –
=0
– 8b + 4h = 0
b=
.
3
3
2
h
5
Thus, when a =
Ans.
h and b = we get the stationary value of S.
2
2
Example 100. Find the maximum and minimum distances of the point (3, 4, 12) from the
sphere x2 + y2 + z2 = 1.
Solution. Let the co-ordinates of the given point be (x, y, z), then its distance (D) from
(3, 4, 12).
D =
( x 3)2 ( y 4)2 ( z 12)2
F (x, y, z) = (x – 3)2 + (y – 4)2 + (z – 12)2
x + y2 + z2 = 1
(x, y, z) = x2 + y2 + z2 – 1
2
F
= 2 (x – 3) + 2 x = 0
x
x
F
= 2 (y – 4) + 2 y = 0
y
y
F
= 2 (z – 12) + 2 z = 0
z
z
Multiplying (1) by x, (2) by y and (3) by z and adding, we get
(x2 + y2 + z2) – 3x – 4y – 12z + (x2 + y2 + z2) = 0
1 – 3x – 4y – 12 z + = 0
...(2)
...(3)
...(4)
3
1
4
From (2)
y =
1
12
From (3)
z =
1
Putting these values of x, y, z in (4),we have
From (1)
...(1)
x =
...(5)
...(6)
...(7)
9
16
144
2
0 (1 + ) = 169
1 1 1
Putting the value of 1 + in (5), (6) and (7) we have the points
1
1 + = ± 13
3 4 12
3 4 12
,
, , and ,
.
13
13
13
13 13 13
2
The minimum distance
3
4
12
3 4 12
13
13
13
=
3
4
12
3 4 12
13
13
13
2
The maximum distance
2
=
2
2
= 12
2
= 14
Ans.
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Partial Differentiation
89
2
2
2
2
2
2
Example 101. If u = ax + by + cz where x + y + z = 1 and lx + my + nz = 0 prove
that stationary values of ‘u’ satisfy the equation
l2
m2
n2
= 0
a u bu cu
Solution. We have,
Let
u
u
x
x
x
= ax2 + by2 + cz2
= x2 + y2 + z2 – 1
= lx + my + nz
u
= 2 a x, y = 2 b y,
= 2 x,
= 2 y,
y
= l,
= m,
y
...(1)
...(2)
...(3)
u
= 2cz
z
= 2z
z
= n
z
By Lagrange’s method
u
1
2
= 0,
x
x
x
2 a x + 2 x l + 2 l = 0
...(4)
u
1
2
= 0,
y
y
y
2 b y + 2 y l + 2 m = 0
...(5)
u
1
2
= 0,
z
z
z
2 c z + 2 z l + 2 n = 0
...(6)
Multiplying (4), (5) and (6) by x, y and z respectively and adding, we get
(2 ax2 + 2 by2 + 2 cz2) + (2 x2 + 2 y2 + 2 z2) 1 + (lx + my + nz) 2 = 0
2u + 21 = 0,
1 = – u
Putting the value of 1 in (4), (5) and (6), we get
2 a x – 2 x u + 2 l = 0,
x=
2l
2 a u
2 b y – 2 y u + 2 m = 0,
y=
2 m
2 b u
2 c z – 2 z u + 2 n = 0,
z=
2 n
2(c u )
Putting the values of x, y, z in (3), we get
2 m2
2 l 2
2 n2
=0
2 a u 2 b u 2 c u
l2
m2
n2
= 0
au bu cu
Proved.
EXERCISE 1.19
1. Show that the greatest value of xm yn where x and y are positive and x + y = a is
where a is constant.
mm . n n . a m n
( m n) m n
,
2. Using Lagrange’s method (of multipliers), find the critical (stationary values) of the function
f (x, y, z) = x2 + y2 + z2, given that z2 = xy + 1.
Ans. (0, 0, – 1), (0, 0, 1).
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Partial Differentiation
90
3. Decompose a positive number ‘a’ into three parts so that their product is maximum.
a a a
, ,
3 3 3
4. The sum, of three numbers is constant. Prove that their product is a maximum when they are equal.
5. Using the method of Lagrange’s multipliers, find the largest product of the numbers x, y and z when
Ans.
x + y + z2 = 16.
Ans.
4096
25 5
6. Using the method of Lagrange’s multipliers, find the largest product of the numbers x, y and z when
x2 + y2 + z2 = 9.
Ans. 3 3
7. Find a point in the plane x + 2y + 3z = 13 nearest to the point (1, 1, 1) using the method of
3 5
Ans. ,2,
2 2
8. Using the Lagrange’s method (of multipliers), find the shortest distance from the point
(1, 2, 2) to the sphere x2 + y2 = 36.
Ans. 3
Lagrange’s multipliers.
9. Find the shortest and the longest distances from the point (1, 2, – 1) to the
x2 + y2 + z2 = 24.
Ans. 6,3 6
10. The sum of the surfaces of a sphere and a cube is given. Show that when the sum of the volumes
is least, the diameter of the sphere is equal to the edge of the cube.
11.
The electric time constant of a cylindrical coil of wire can be expressed approximately by
mxyz
K = ax by cz
where z is the axial length of the coil, y is the difference between the external and internal radii
and x is the mean radius ; a, b, m and c represent positive constants. If the volume of the coil is
fixed, find the values of x and y which make the time constant K as large as possible.
a3
12. If u =
x
x
2
b3
y
2
c3
z2
, where x + y + z = 1, prove that the stationary value of u is given by
a
b
c
, y
, z
abc
abc
a b c
n
n
i 1
i 1
2
13. Find maximum value of the expression ai xi with xi 1,
1
where a1 , a2, a3.......an are positive constants.
Ans. ( a12 a22 ....... an2 ) 2
2
2
2
14. If r is the distance of a point on conic ax + by + cz = 1, lx + my + nz = 0 from origin, then
the stationary values of r are given by the equation.
l2
m2
n2
0
(A.M.I.E.T.E., Winter 2002)
1 ar 2 1 br 2 1 cr 2
2
2
15. If x and y satisfy the relation ax + by = ab, prove that the extreme values of function
u = x2 + xy + y2 are given by the roots of the equation 4 (u – a) (u – b) = ab
(A.M.I.E.T.E., Winter 2000)
16. Use the Lagranges method of undetermined multipliers to find the minimum value of
x2 + y2 + z2 subject to the conditions x + y + z = 1, xyz + 1 = 0.
2
2
17. Test the function f ( x, y ) ( x 2 y 2 ) e ( e y ) for maxima and minima for points not on the circle
x2 + y2 = 1.
18. Find the absolute maximum and minimum values of the function
f (x, y) = cx2 + y2 – x over the region to 2x2 + y2 1
(AMIETE, Dec. 2008)
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Multiple Integral
91
2
Multiple Integral
2.1 DOUBLE INTEGRATION
We know that
b
a
f ( x) dx = lim [ f ( x1 ) δx1 f ( x2 ) δx2 f ( x3 ) δx3 +...
n
x 0
+ f (xn) xn]
Let us consider a function f (x, y) of two variable x and y
defined in the finite region A of xy-plane. Divide the region A
into elementary areas.
A1, A2, A3, ...... An
Then
A f ( x, y) dA
f ( x1 , x1 ) δA1 f ( x2 , y2 ) δA2 ..... f ( xn , yn ) δAn
= nlim
δ A 0
2.2 EVALUATION OF DOUBLE INTEGRAL
Double integral over region A may be evaluated by two
successive integrations.
If A is described as f1 (x) y f2 (x) [y1 y y2]
and
a x b,
Then
b
y2
A f ( x, y) dA = a y1
f ( x, y ) dx dy
(1) First Method
b
y2
A f ( x, y) dA = a y1
f ( x, y ) dy dx
f (x, y) is first integratred with respect to y treating
x as constant between the limits a and b.
In the region we take an elementary area
xy.Then integration w.r.t y (x keeping constant).
converts small rectangle xy into a strip PQ (y x). While
the integration of the result w.r.t. x corresponding to the
sliding to the strip PQ, from AD to BC covering the while
region ABCD.
Second method
A f ( x, y) dxdy
=
d
x2
c x
f ( x, y ) dx dy
1
91
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92
Multiple Integral
Here f (x,y) is first integrated w.r.t x keeping y constant
between the limits x1 and x2 and then the resulting expression
is integrated with respect to y between the limits c and d
Take a small area xy. The integration w.r.t. x between
the limits x1, x2 keeping y fixed indicates that integration is
done, along PQ. Then the integration of result w.r.t y
corresponds to sliding the strips PQ from BC to AD covering
the whole region ABCD.
Note. For constant limits, it does not matter whether
we first integrate w.r.t x and then w.r.t y or vice versa.
1
x
0 0
Example 1. Evaluate
( x 2 y 2 ) dA, where dA indicates small area in xy-plane.
(Gujarat, I Semester, Jan. 2009)
x
1 2
1
x
y3
2
2
I = 0 0 ( x y ) dy dx 0 x y dx
3 0
1
1
1
x3
x 2 ( x 0) ( x 3 0) dx x 3 dx
0
0
3
3
1
4
1 4 3
4 x
1
1
x dx = [1 0] sq. units.
Ans.
0 3
3 4 0 3
3
Solution. Let
1
1 x
1
0
Example 2. Evaluate
x1/3 y 1/ 2 (1 x y)1/ 2 dy dx .
(M.U., II Semester 2002)
Solution. Here, we have
I=
Putting
1
1 x
1
0
x1/3 y 1/ 2 (1 x y)1/ 2 dy dx
...(1)
(1 – x) = c in (1), we get
I=
1
1
Again putting y = ct
I =
=
=
=
=
=
1
1
1
x 3 dx
x1/3 dx
1
1
c
y 1/ 2 (c y)1/ 2 dy
0
1
c
0
c x1/ 3 dx
1
0
1
2
t
1
2
1
(c c t ) 2 c dt
c 1/2 t 1/ 2 c1/ 2 (1 t )1/ 2 c dt
c
0
t 1/ 2 (1 t )1/ 2 dt
1
1
1
1/3
1
1
cx
c x1/3
1 3
dx 2 2
1 3
2 2
dx =
2
2
1
1
c x1/ 3 dx
1 3
c x 3 dx ,
1
2 2
1
...(2)
dy = c dt in (2), we get
1
1
x1/3 dx
=
1
1
1
1
1/3
cx
1
0
1
0
t1/ 2 1 (1 t )3 / 2 1 dt
xl 1 (1 x)m 1 dx (l , m)
1 1 1
.
dx 2 2 2
2
1
1
1/ 3
cx
dx
1
2
1
x1/3 . c dx
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Multiple Integral
93
Putting the value of c, we get
1 1/3
x (1 x) dx
I =
2 1
2
1
x 4/3 x 7/3
( x x ) dx
7
2 4
1
3 1
3
3
3
3
3
9 9
(1) (1) (1) (1) =
=
2 4
7
4
7
2 14 28
R ( x y) dy dx, R
Example 3. Evaluate
1
1/3
4/3
Ans.
is the region bounded by x = 0, x = 2, y = x,
y = x + 2.
Solution. Let I ( x y ) dy dx
(Gujarat, I Semester, Jan. 2009)
Y
R
The limits are x = 0, x = 2, y = x and y = x + 2
I 0 dx
x
2
( x y ) dy 0
x2
y2
xy
2 x
dx
y=
1
x2
2
2
x ( x 2) ( x 2) x dx
2
2
2
1 2
x2
2
x
2
x
(
x
4
x
4)
x
dx
2
2
2
0
2
0
2
[2x + 2x + 2] dx
0
2
2
0
X´
2
(2 x 1) dx 2 [ x
Example 4. Evaluate
x ]02
x+
2
2
x=a
x 2
x= 2
2
–2
–1
y=x
0
1
X
2
= 2 [4 + 2] = 12 Ans.
R xy dx dy
where R is the quadrant of the circle x2 + y2 = a2 where x 0 and y 0.
(A.M.I.E.T.E, Summer 2004, 1999)
Solution. Let the region of integration be the first quadrant of the circle OAB.
y 2 a 2 , y a 2 x2 )
First we integrate w.r.t. y and then w.r.t. x.
R xy dx dy ( x
2
2
The limits for y are 0 and
=
a
0
x dx
a 2 x2
0
a x
y dy =
a
0
2
Y
B
and for x, 0 to a.
y2
x dx
2 0
2
a x
P
y
2
=
2
a
2
–x 2
dy
dx
a
4
1 a 2 x 2 x4
1 a
2
2
= a Ans.
x
(
a
x
)
dx
= 0
=
2 2
4 0
2
8
Example 5. Evaluate
O
Q
y=0
A
X
xy y 2 dy dx,
s
where S is a triangle with vertices (0, 0), (10, 1) and (1, 1).
Solution. Let the verties of a triangle OBA be (0, 0) (10, 1) and (1, 1).
Equation of OA is x = y.
Equation of OB is x = 10 y.
The region of OBA, given by the limits
y < x < 10 y and 0 < y < 1.
s
xy y 2 dy dx =
1
10 y
0 dy y
( xy y 2 )½ dx
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94
Multiple Integral
10 y
2 1
( xy y 2 )3/ 2
y
y
1
0 dy 3
=
12
1
1
(9 y 2 )3/ 2 dy 18 y 2 dy
0
3 y
0
1
y3
18
6
= 18
3
0 3
Example 6. Evaluate
A x
2
Ans.
dx dy, where A is the region in the first quadrant bounded by the
hyperbola xy = 16 and the lines y = x, y = 0 and x = 8.
(A.M.I.E., Summer 2001)
Solution. The line OP, y = x and the curve PS, xy = 16 intersect at (4, 4).
The line SN, x = 8 intersects the hyperbola at S (8, 2). y = 0 is x-axis.
The area A is shown shaded.
Divide the area in to two part by PM
perpendicular to OX.
For the area OMP, y varies from 0 to x, and
then x varies from 0 to 4.
For the area PMNS, y -series from 0 to 16/x
and then x varies from 4 to 8.
A x
=
=
4 2
0 x
4 3
x
0
2
dx dy =
4
x 2
0 0 x
x
8
16 / x
0
4
0
dx dy x2 dx
dx dy
8 16/ x
4 0
dy =
4
x
2
dx dxy
8
x
16/ x
2
2
0 x y 0 dx 4 x y 0 dx
4
8
x4
x2
dx 16 x dx 16 = 64 + 8 (82 – 42) = 64 + 384 = 448. Ans.
4
4 0
2 4
8
Example 7. Evaluate
2
( x y) dx dy over the area bounded by the ellipse
x2
y2
1
a2 b2
(U.P. Ist Semester Compartment 2004)
Solution. For the ellipse
x2
a2
y2
b2
1
Y
y
x2
b
y
a 2 x2
= 1 2
b
a
a
The region of integration can be expressed as
b
b
a x a and
a2 x2 y
a2 x2
a
a
( x y ) 2 dx dy = ( x 2 y 2 2 xy ) dx dy
=
b / a a2 x 2
a
a (b / a)
=
a
a 2 x2
b / a a 2 x2
2
a ( b / a ) a x
=
a
b / a a 2 x2
a 0
2
X
x=a
x = –a
O
( x 2 y 2 2 xy ) dx dy
( x 2 y 2 ) dx dy
a
b / a a 2 x2
a ( b / a )
a 2 x2
2 xy dy dx
2 ( x 2 y 2 ) dy dx 0
[Since (x2 + y2) is an even function of y and 2xy is an odd function of y]
b
2
y 3 a
= a 2 x y
3
0
a
a 2 x2
dx
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Multiple Integral
95
a 2
b
1 b3 2
2
2
2 3/ 2
= 2 a x a a x 3 3 (a x ) dx
a
3
a b 2
b
2
2
2
2 3/ 2
= 4 0 a x a x 3 (a x ) dx
3a
[On putting x = a sin and dx = a cos d]
b
b3
= 4 2 . a 2 sin 2 . a cos 3 a3 cos3
0 a
3a
3
3
ab
2
2
cos 4 d
= 402 a b sin cos
3
3
3
2
2
= (a b ab ) ab (a b )
4
4
Example 8. Evaluate
( x
2
a cos d
3 1 1 ab3 3 1
. . .
= 4 a b . . .
4 2 2
3 4 2 2
Ans.
y 2 ) dx dy throughout the area enclosed by n the curves y = 4x,
x + y = 3, y = 0 and y = 2.
Solution. Let OC represent y = 4x; BD,
x + y = 3; OB, y = 0, and CD, y = 2. The
given integral is to be evaluated over the
area A of the trapezium OCDB.
Area OCDB consists of area OCE, area
ECDF and area FDB.
The co-ordinates of C, D and B are
1
, 2 (1, 2) and (3, 0) respectively..
2
2
2
A ( x y ) dy dx
2
2
2
2
2
2
= OCE ( x y ) dy dx ECDE ( x y ) dy dx FDB ( x y ) dy dx
=
½
4x
1
Now, I1 =
=
I2 =
=
2
3
3 x
( x 2 y 2 ) dy dx ( x 2 y 2 ) dy dx ( x 2 y 2 ) dy
½
0
1
0
I1
I2
I3
4x
½
4x 2
½ 2
½ 76 3
y3
2
0 dx 0 ( x y ) dy 0 x y 3 dx 0 3 x dx
0
½
76 ½ 3
76 x 4
76 1 1 19
x dx
.
3 0
3 4 0
3 4 16 48
2
1
1
1 2
1
y3
8
2
2
2
dx
(
x
y
)
dy
x
y
dx ½ 2 x dx
½ ½
½
3 0
3
1
2 x3 8
2 8 2 1 8 1 23
x . .
3
3
½ 3 3 3 8 3 2 12
0 dx 0
3 x
2
3 2
y3
(3 x)3
I3 = dx ( x y ) dy = x y dx x (3 x )
dx
1
0
1
0
3 0
3
3
3 x 4 (3 x )4
3 2
(3 x)3
3
= 1 3x x
dx x
3
4
3
1
3
3 x
2
2
3
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96
Multiple Integral
81
1 16 22
0 1
= 27
4
4 12 3
19 23 22 463
31
2
2
A ( x y ) dy dx I1 I 2 I3 48 12 3 48 9 48 .
Ans.
EXERCISE 2.1
Evaluate
2 x2
1.
0 0
3.
0 0
a
y
e x dy dx
0 0
0 y2 (1 xy
Ans.
a2
4
4.
xy dy dx
Ans.
2a 4
3
6.
2 a 2 ax x
5.
0
0
2
2
a a x
7.
a
a 2 x2
0
9.
Ans.
0
0
2
a
4
0 0
2
2 ax x 2
1
(1 y 2 )
1 2
3
2e a
log
2
1 ea
1 y
2a
8.
) dx dy
Ans.
41
210
x 2 dy dx
Ans.
5a 4
8
Ans.
4
1 x2 y 2
0
aa
a4
6
dx dy
0
Ans.
xy dx dy
x dx dy
Ans.
a2
log ( 2 1)
2
(A.M.I.E.T.E., June 2009)
Ans.
14
3
where A is given by y = 0, x + 2y = 3, x = y2.
Ans.
dx dy
y
2
(1 e ) a x y
0
1
11
a 2 x2 y 2 dy dx
ay
2.
dx dy
a2 y 2
a
Ans. e2 – 1
Ans.
2
10.
0y
2
x y
2
2
( x 2 3x y 2 ) dx dy
x 0 y 0
12.
(5 2 x y) dx dy,
A
13.
xy dx dy,
where A is given by x2 + y2 – 2x = 0, y2 = 2x, y = x.
A
14.
1
3
33
2
4 x 2 y 2 dx dy , where A is the triangle given by y = 0, y = x and x = 1. Ans.
x
2
xy (1 x y ) dx dy where A is the area bounded by x = 0, y = 0 and x + y = 1. Ans.
A
15.
217
60
7
Ans. 12
dx dy , where R is the two-dimensional region bounded by the curves y = x and y = x2. Ans.
R
16.
A
1
20
2
105
2.3 EVALUATION OF DOUBLE INTEGRALS IN POLAR CO-ORDINATES
We have to evalaute
2
r ( )
1 r12()
f (r , ) dr d over the region bounded by the staight lines
= 1 and = 2 and the curves r = r1 (q) and r = r2
(). We first integrate with respect to r between the
limits r = r1() and r = r2() and taking as constant.
Then the resulting expression is integrated with respect
to between the limits = 1 and = 2.
The area of integration is ABCD. On integrating first
with respect to r, the strip extends from P to Q and the
integration with respect to means the rotation ot this strip PQ from AD to BC.
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Multiple Integral
97
Example 9. Transform the integral to cartesian form and hence evaluate
a
0
0
r 3 sin cos dr d.
(M.U., II Semester 2000)
B
Solution. Here, we have
a
0
0
r 3 sin cos dr d
r=a
...(1)
Here the region i.e., semicircle ABC of integration is bounded by
r = 0, i.e., x-axis.
r = a i.e., circle, = 0 and = i.e., x-axis in the second quadrant.
C
A
O
(r sin ) (r cos ) (r d dr)
Putting x = r cos , y = r sin , dx dy = r d dr in (1), we get
a 2 x2
a
a
=
a
a
1
=
2
0
y2
x dx
2 0
a
a
xy dy dx =
a 2 x2
=
a
x dx
a
2
a
a
x dx
a2 x 2
0
Y
y dy
r = 2 cos
= —
2
O
2
(a x )
2
Since f ( x) is odd function
(a x x ) dx = 0 Ans. a
f ( x) dx 0
a
2
3
Example 10. Evaluate
Solution.
2
0 0
2 x – x2
2
0 0
2 x – x2
rd dr
0 0
2 x – x2
2
X
Y
( x 2 y 2 ) dy dx
( x 2 y 2 ) dy dx
Limits of
y = 2 x – x 2 y2 = 2x – x2 x2 + y2 – 2x = 0
(1) represents a circle whose centre is (1, 0) and radius = 1.
Lower limit of y is 0 i.e., x-axis.
Region of integration is upper half circle.
Let us convert (1) into polar co-ordinate by putting
x = r cos , y = r sin
r2 – 2 r cos = 0 r = 2 cos
Limits of r are 0 to 2 cos
Limits of are 0 to
2
2
x=2
= 0
2
( x y ) dy dx =
2 cos 2
2
r
0 0
(r d dr ) =
4
= 4 02 cos d 4
Example 11. Evaluate
2
0 0
2 x – x2
x dy dx
x2 y2
2 cos 3
r
0
dr
2 d
0
2 cos
r4
4 0
3 1 3
422 4
Ans.
by changing to polar coordinates.
Solution. In the given integral, y varies from 0 to
y=
2 d
0
...(1)
2 x – x 2 and x varies from 0 to 2.
2 x – x2
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98
Multiple Integral
y2 = 2x – x2
x2 + y2 = 2x
In polar co-ordinates, we have r2 = 2r cos
r = 2 cos .
.
2
In the given integral, replacing x by r cos , y by r sin , dy dx by r dr d, we have
For the region of integarion, r varies from 0 to 2 cos and varies from 0 to
I=
=
2 cos r
/2
0 0
/ 2
0
/ 2 2 cos
cos . r dr d
r cos dr d
0
0
r
2 cos
r2
cos
2 0
d
/ 2
0
2 cos3 d 2.
2 4
.
3 3
Ans.
EXERCISE 2.2
Evaluate the folloing:
a (1 – cos )
1.
0 0
2.
0 0
3.
Ans.
a (1 cos ) 2
r cos dr d
r dr d
r 2 a2
A
4.
8 3
a
3
5
3
Ans. a
8
a
Ans. 2a –
2
2 r 2 sin d dr
r
2
where A is a loop of r2 = a2 cos 2
sin d dr where A is r = 2a cos above initial line.
(A.M.I.E. Winter 2001) Ans.
A
5.
Calculate the integral
6.
(x
2
( x y)2
x2 y 2
dx dy over the circle x2 + y2 1.
Ans. – 2
y 2 ) x dx dy over the positive quadrant of the circle x2 + y2 = a2 by changing to polar coordinates.
Ans.
7.
8.
x 2 y 2 dx dy by changing to polar coordinates, R is the region in the xy-plane bounded by the
38
circles x2 + y2 = 4
(AMIETE, Dec. 2009)
Ans.
3
Convert into polar coordinates
0 0
10.
a2
5
R
2 a 2 ax – x 2
9.
2a3
3
3
/ 2 2a cos
dx dy
Ans.
0
r d dr
0
3
4
4
r dr d, over the area bounded between the circles r = 2b cos and r = 2b cos . Ans. 2 (a – b )
5
r sin dr d over the area of the cardiod r = a (1 + cos ) above the initial line. Ans. 8 a3
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Multiple Integral
2
99
11.
Ax
12.
Transform the integral
dr d, where A is the area between the circles r = a cos and r = 2a cos .
1
x
0 0
Ans.
28a 3
9
f ( x, y ) dy dx to the integral in polar co-ordinates.
/4
sec
0 0
Ans.
f ( r , ) r d dr
2.4 CHANGE OF ORDER OF INTEGRATION
On changing the order of integration, the limits of integration change. To find the new limits,
we draw the rough sketch of the region of integration.
Some of the problems connected with double integrals, which seem to be complicated, can be
made easy to handle by a change in the order of integration.
a a
x
dx dy by changing the order of integration.
Example 12. Evaluate 0 y 2
x y2
(AMIETE, June 2010, Nagpur University, Summer 2008)
Solution. Here we have
Y
a a
x
y=a
dx dy
B
I = 0 y 2
x y2
Here
x = a, x = y, y = 0 and y = a
The area of integration is OAB.
x
On changing the order of integration Lower limit of
=
y
y = 0 and
x=a
upper limit is y = x.
Lower limit of x = 0 and upper limit is x = a.
yx
1
a
X
dy
I = xdx 0
O
y=0
A
2
0
x y2
yx
Y
y
1
xdx tan 1
0
x0
x
a x
x
dx tan 1 tan 1 0
0 x
x
a
π a aπ
π
dx [ x]
Ans.
0
4 0 4
4
Example 13. Change the order of integration in
a
I=
1
2– x
0 x2
xy dx dy and hence evaluate the same.
y=a
y
=
B (a, a)
x
x=a
O
y=0
A
X
(A.M.I.E.T.E., June 2010, 2009, U.P. I Sem., Dec., 2004)
Solution. I =
1
2– x
0 x2
xy dx dy
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100
Multiple Integral
The region of integration is shown by shaded portion in the figure bounded by parabola y = x2
and the line y = 2 – x.
The point of intersection of the parabola y = x2 and the line y = 2 – x is B (1, 1).
In the figure below (left) we have taken a strip parallel to y-axis and the order of integration is
1
2– x
0 x dx x2
y dy
In the second figure above we have taken a strip parallel to x-axis in the area OBC and second
strip in the area ABC. The limits of x in the area OBC are 0 and
area ABC are 0 and 2 – y.
=
1
0
1
=
2
y dy
1
0
y
0
2
x dx y dx
1
y 2 dy
1
2
2
1
2– y
0
x2
1
x dx y dy
0
2 0
y (2 – y ) 2 dy
2
1
2
1 1 2 4 3 y4
1 1
= 2 y – y
6 2
3
4
6 2
1
=
1
y3
1
3
0 2
y
2
y
0
1 (4 y – 4 y
2
y and the limits of x in the
2– y
x2
y dy
2 0
y 3 ) dy
32
4 1
8 – 3 4 – 2 3 – 4
1 1 96 – 128 48 – 24 16 – 3 1 5
9 3
6 24 24 8
6 2
12
Example 14. Evaluate the integral
x
0
dy
y
–
xe
x2
y
0 0 x exp –
integration
Solution. Limits are given
y = 0 and y = x
x = 0 and x =
Here, the elementary strip PQ extends from y = 0
to y = x and this vertical strip slides from
x = 0 to x = .
The region of integration is shown by shaded
portion in the figure bounded by y = 0, y = x,
x = 0 and x = .
On changing the order of integration, we first
integrate with respect to x along a horizontal strip
RS which extends from x = y to x = and this
horizontal strip slides from y = 0 to y = to cover
the given region of integration.
New limits :
x = y and x =
y = 0 and y =
We first integrate with respect to x.
Thus,
Ans.
x2
dx dy by changing the order of
y
(U.P. I Semester Dec., 2005)
x2
y 2x – y
dx = dy – –
e
dx
0
y
2 y
2
x
–
y –
y
= 0 dy – e y 0 dy 0 e
2
2
y
y2
2
y e – y dy
0 2
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Multiple Integral
101
y
= 2 (– e
–y
1
) – (e – y )
2
0
1
(Integrating by parts)
1
= (0 – 0) 0 2 2
Ans.
Example 15. Change the order of the integration
x x y
0 0e
y dy dx
(B.P.U.T.; I Semester 2008)
Solution. Here, we have
0
y dy dx
Here the region OAB of integration is bounded by
y = 0 (x-axis), y = x (a straight line), x = 0, i.e., y axis.
A strip is drawn parallel to y-axis, y varies 0 to x and
x varies 0 to .
On changing the order of integration, first we integrate
w.r.t. x and then w.r.t. y.
A strip is drawn parallel to x-axis. On this strip x
varies from y to and y varies from 0 to .
x
0 0
Hence
B
Y
x x y
e
0
e xy y dy dx =
0
y dy
0
=
0
O
2
y dy
[0 e y ]
y
0
X
A
A
x
1
Ans.
2
O
Example 16. Change the order of integration in the double integral
=
x
y=0
Y
e xy dx
=
e xy
y dy
y y
=
y
y
=
y
x=
2
e y dy
2a
0
2 ax
2 ax – x 2
B
X
V dx dy
Solution. Limits are given as
x = 0, x = 2a
y = 2 ax
and y = 2 ax – x 2 y2 = 2 ax
and
(x – a)2 + y2 = a2
The area of integration is the shaded portion OAB. On changing the order of integration first
we have to integrate w.r.t. x, The area of integration has three portions BCE, ODE and ACD.
2a
0
=
dx
2a
0
dy
2 ax
2 ax – x 2
V dy
2a
a
a a 2 y2
y 2 / 2 aV dx 0 dy y 2 / 2 a
a
2a
0
a a 2 – y2
dy
V dx
V dx
Ans.
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102
Multiple Integral
EXERCISE 2.3
Change the order of integration and hence evaluate the following:
a
cos y dy
x
1.
0 0
2.
0 x2
(a – x) (a – y)
2a
3a – x
cos y dx
a
a
2 ay
0
0
( x 2 y 2 ) dy dx Ans. (a) dy
(a – x ) (a – y )
( x2 y 2 ) dx
3a
a
dy
(b) 2 sin a.
3a – y
0
( x 2 y 2 ) dx (b)
314 a 4
.
35
4a
1
x
a
ya
3.
0 x2 (x
4.
0
2
Ans.
0 dy y
f ( x, y ) dx dy
Ans.
0 dx
a 2 – y2
a
– a 0
6.
0 x
1
y 2 ) –1/ 2 dy dx
a2 – y 2
5.
1
a
Ans. ( a ) 0 dy y
dx
f ( x , y ) dx dy
x
dy dx
y
x dy dx
a
a
Ans.
0
Ans.
0
dy
y
b
a
a
bx / a
5
2x
2
a
a
a a2 – y 2
2a
( x 2 y 2 ) – 1 / 2 dx.
a
f ( x, y) dy
a 2 – x2
a 2 – x2
dx
a
2 x
y
– a 2 – x2
y
2
xdx
0
1
2a
dx
a
a
x–a
f ( x, y) dy
f ( x, y ) dy
2 y
dy
y
xdx; log
0
4
e
7.
0 y x2 y2
8.
0 0
9.
Ans. 0 dy 2 – y f ( x, y ) dx 2 dy y – 2 f ( x, y ) dx
0 2 – x f ( x, y) dx dy
y
x
2
2 –y
2
2 –y
Ans. – dx – x ( y – x ) e dy (A.M.I.E., Summer 2000)
0 – y ( y – x ) e dx dy
1
2 y
(A.M.I.E.T.E., June 2009)
y 0 x y yx dy
a 2a – x
0 x2 xy dx dy (U.P. I Semester, Dec., 2007) Ans. a ay xy dx dy 2a – y xy dx dy, 3a 2
10.
11.
12.
13.
0 a –
(M.P. 2003)
b
a2 – y2
a
Ans. ( a ) 0 dy ay / b x dx
x dy dx
5
7
0
xy dx dy
Ans.
0
1 2
a b
3
(b )
x dx
5
0
0
a 2 – ( x – a )2
0
y dy,
8
2 4
a
3
[Hint: Put x = a sin2 dx = 2 a sin cos d ]
14.
1
0
2a
1 – y 1/ 3
x
–1
0
16.
0 0 ( x
18.
19.
0
1
y
2
y 2 ) dx dy
2
1
2– y
0
1
1
x 3 dx
Ans.
–1
Ans.
0 dy
x2
dx 4a ( x y )3 dy
15.
17.
y – 1/ 2 (1 – x – y )1/ 2 dx dy
( x 2 y 2 ) dx dy
a
1– x –
0
2a
4a y
y
1
2
1
(1 – x – y ) 2 dy , –
3
7
( x y )3 dx
Ans.
1
2– x
0 dx x
( x 2 y 2 ) dy,
5
3
a5
20
(U.P., I Semester, Dec. 2007, A.M.I.E., Summer 2001)
1 2
2 2
1
1
1
0 1 x 2 y 2 dx dy 0 y x2 y 2 dx dy R x2 y 2 dy dx
Recognise the region R of integration on the R.H.S. and then evaluate the integral on the right in the
order indicated.
(AMIETE, Dec. 2004)
Ans. Region R is x = 0, x = y, y = 1 and y = 2, log 2.
4
Express as single integral and evaluate :
a
0 0
a
a 2 – x2
x
y2
x 2 y 2 dx dy by changing into polar coordinates. Ans.
a
0 2 0 x dx dy a 0
2
a2 – x 2
x dx dy
Ans.
0
a
2
dy
a2 – y 2
y
x dx,
5a 3
6 2
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Multiple Integral
20.
Express as single integral and evaluate :
1
y
0 0 ( x
21.
103
2
y 2 ) dx dy
2
1
2– y
0
( x 2 y 2 ) dx dy
Ans.
1
2–x
0 dx x
( x 2 y 2 ) dy,
If f(x, y) dx dy, where R is the circle x2 + y2 = a2, is R equivalent to the repeated integral.
5
3
2 1
(AMIE winter 2001) [Ans.
(r, ) r dr d . ]
0 0
2.5 CHANGE OF VARIABLES
Sometimes the problems of double integration can be solved easily by change of independent
variables. Let the double integral as be f ( x, y ) dx dy. It is to be changed by the new variables
R
u, v.
The relation of x, y with u, v are given as x = f(u, v), y = (u, v). Then the double integration
is converted into.
f {(u, v), (u, v)} | J | du dv, where
R
x x
( x, y )
u v
du dv
du dv
dx dy= | J | du dv =
y y
(u , v )
u v
2
Example 17. Evaluate ( x y ) dx dy, where R is the parallelogram in the xy-plane with
R
vertices (1, 0), (3, 1), (2, 2), (0, 1), using the transformation u = x + y and v = x – 2y.
(U.P., I Semester, 2003)
Solution. The region of integration is a parallelogram ABCD, where A (1, 0), B (3, 1), C (2, 2)
and D (0, 1) in xy-plane.
The new region of integration is a rectangle ABCD in uv-plane
xy-plane A (x, y)
A (1, 0)
A (u, v)
uv-plane A (x + y, x – 2y)
A (1 + 0, 1 – 2 × 0)
A (1, 1)
and
u x y
v x – 2 y
B (x, y)
B (3, 1)
B (u, v)
B (x + y, x – 2y)
B (3 + 1, 3 – 2 × 1)
C (x, y)
C (2, 2)
C (u, v)
C (u, v)
C (2 + 2, 2 – 2 × 2)
D (x, y)
D (0, 1)
D (u, v)
B (4, 1)
C (4, – 2)
D (1, – 2)
D (0 + 1, 0 – 2 × 1)
1
(2u v )
3
1
y (u – v )
3
1
1
3
–
1
3
–
3
x
x
( x, y ) u
J =
(u , v ) y
u
and
x
2
v
3
y
1
v
3
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104
Multiple Integral
dx dy = | J | du dv
1
du dv
3
4
u3
1
1
dv – 2 7 dv 7 [v]2 7 3 21 Ans.
3
R
1
Example 18. Using the transformation x + y = u, y = uv, show that
2
( x y ) dx dy =
1
4 2
u .
–2 1
1
du dv =
3
1
– 2 3
1
2
, integration being taken over
105
the area of the tringle bounded by the lines x = 0, y = 0,
x + y = 1.
1/ 2
[ xy (1 – x – y)]
Solution.
dx dy
1/ 2
[ xy (1 – x – y)]
dx dy
x + y = u or x = u – y = u – uv,
x x
( x, y )
u v
du dv
dx dy
du dv =
y y
(u , v )
u v
1– v –u
du dv u du dv.
dx dy =
v
u
x=0
u (1 – v) = 0
u = 0, v = 1
y=0
uv = 0
u = 0, v = 0
x+y=1
u=1
Hence, the limits of u are from 0 to 1 and the limits of v are from
0 to 1.
The area of integration is a square OPQR in uv-plane.
On putting x = u – uv, y = uv, dx dy = u du dv in (1), we get
1/ 2
(u – uv)
(uv)1/ 2 (1 – v )1/ 2 u du dv
3 3 3
2
1/ 2
(1 – v )1/ 2 dv 2 2 2
= 0 u (1 – u ) du
9
5
2
3
1 1 1 1
1
1
2.
.
1
2
2
2
2
2
2
2
2
=
7 5 3
2
1
105
7 5 3 3
2
2
2
2 2 2 2
1
1 1/ 2
v
0
3
Ans.
EXERCISE 2.4
1.
2.
3.
y
sin
dx dy by means of the transformation u = x + y, v = y from (x, y) to
x y
1
(u, v)
Ans.
1
1– x
y
1
dy dx (e – 1)
Using the transformation x + y = u, y = uv, show that
0
0
2
ex y
(A.M.I.E. Winter 2001)
x– y
1
dx dy sin 1 where R is bounded
Using the transformation u = x – y, v = x + y, prove that cos
x
y
2
R
by x = 0, y = 0, x + y = 1
1
1
1
Hint : x 2 (u v ), y 2 (v – u ) so that | J | 2
Evaluate
–( x y )
0 0 e
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Multiple Integral
105
2.6 AREA IN CARTESIAN CO-ORDINATES
Let the curves AB and CD be y1 = f1 (x) and y2 = f2 (x).
Let the ordinates AD and BC be x = a and x = b.
So the area enclosed by the two curves y1 = f1 (x) and y2 = f2(x) and x = a and x = b is ABCD.
Let P(x, y) and Q(x + x, y + y) be two neighbouring points, thent the area of the small
rectangle PQ = x. y.
Area of the vertical strip =
y2
y
x y x y12 dy x
y 0
lim
the width of the strip is constant
y1
throughout.
If we add all the strips from x = a to x = b, we get
b
y
b
y
x y12 dy a dx y12 dy
The area ABCD = xlim
0
a
Area
b
a
y2
y1 dx dy
Example 19. Find the area bounded by the parabola y2 = 4ax
and its latus rectum.
Solution. Required area = 2 (area (ASL)
a
= 2 0
2 ax
0
dy dx
a
= 2 0 2 ax dx
a
x3 / 2
8a 2
= 4 a
3
3/ 2 0
Example 20. Find the area between the parabolas y2 = 4 ax and x2 = 4 ay.
Solution.
y2 = 4ax
x2 = 4ay
On solving the equations (1) and (2) we get the point of intersection (4a, 4a).
Divide the area into horizontal strips of width y, x varies
from P,
...(1)
...(2)
y2
to Q, 4 ay and then y varies from O(y = 0) to
4a
A (y = 4a).
The required area =
4a
0
dy
4ay
y 2 / 4a
dx
4a
4a
4a
y
y 3/ 2
y3
4ay
= 0 dy x y2 / 4 a 0 dy 4ay – 4a 3 –
4 a
12 a
2
0
3
4 a
(4 a ) 32 2 16 2 16 2
3/ 2
= 3 (4a) – 12a 3 a – 3 a 3 a
2
Ans.
Example 21. Find by double integration the area enclosed by the pair of curves
y = 2 – x and y2 = 2(2 – x)
Solution.
y= 2 – x
y2 = 2 (2 – x)
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106
Multiple Integral
On solving the equations (1) and (2), we get the points of
intersection (2, 0) and (0, 2).
A=
The required area =
=
2
2 (2 – x )
0 dx y 2 – x
dx dy
2
2 (2 – x )
0 dx 2 – x
dy
2
dx [ 4 – 2 x – 2 x]
0
2
2
x2
(4 2 x )3/2 – 2 x
=
2 0
3 – 2
2
1
4 8 2
x2
3/ 2
= – (4 – 2 x) – 2 x = – 4
2 3 3
2 0
3
Ans.
EXERCISE 2.5
Use double integration in the following questions:
1.
Find the area bounded by y = x – 2 and y2 = 2x + 4.
x2
+
y2
=
a2
Ans. 18.
2.
Find the area between the circle
and the line x + y = a in the first quadrant.
Ans. ( – 2)a2/4
3.
Find the area of a plate in the form of quadrant of the ellipse
4.
Find the area included between the curves y2 = 4 a(x + a) and y2 = 4 b (b – x).
x2
a
2
y2
b
2
1.
Ans.
Ans.
(A.M.I.E.T.E., Summer 2001)
5.
Find the area bounded by (a) y2 = 4 – x and y2 = x.
(b) x – 2y + 4 = 0, x + y – 5 = 0, y = 0
(A.M.I.E., Winter 2001)
Find the area enclosed by the leminscate r2 = a2 cos 2 .
7.
Find the area common to the circles x2 + y2 = a2 and x2 + y2 = 2ax.
8.
Find the area included between the curves y = x2 – 6x + 3 and y = 2x + 9.
(A.M.I.E., Summer 2001)
2.7
8 ab
3
16 2
3
27
Ans.
2
Ans. a2
Ans.
6.
9.
ab
4
Determine the area of region bounded by the curves xy = 2, 4y = x2, y = 4.
3 2
Ans. –
a
4
3
Ans. 88 22
3
28
Ans.
– 4 log 2
3
(U.P. I Semester 2003)
AREA IN POLAR CO-ORDINATES
Area =
r d dr
Let us consider the area enclosed by the curve r = f ().
Let P (r, ), Q(r + r, + ) be two neighbouring points.
Draw ares PL and QM, radii r and r + r.
PL = r, PM = r
Area of rectangle
PLQM = PL × PM
= (r) (dr) = r r.
The whole area A is composed of such small rectangles.
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Multiple Integral
107
Hence,
A=
lim
r 0
r . r r d dr
0
Example 22. Find by double integration, the area lying inside the cardioid r = a (1 + cos )
and outside the circle r = a.
(Nagpur University, Winter 2000)
Solution.
r = a (1 + cos )
...(1)
r=a
...(2)
Solving (1) and (2), by eliminating r, we get
a(1 + cos ) = a 1 + cos = 1
cos = 0
– or
2
2
limits of r are a and a(1 + cos )
to
2 2
Required area = Area ABCDA
limits of are –
=
/2
a (1 cos )
=
– / 2 a
=
a2
2
2
= a
/ 2
– / 2
/ 2
0
/2
for cardioid
– / 2 r for circle
r d dr
r2
= – / 2
2
/2
r d dr
[(1 cos )2 – 1] d
(cos 2 2 cos ) d
=
a2
2
a (1 cos )
a
/ 2
d
– / 2 (cos
2
2 cos ) d
/ 2
2 / 2
2
= a 0 cos d 2 0 cos d
a2
2
/ 2
2
( 8)
= a 2 (sin )0 a 2 =
Ans.
4
4
4
Example 23. Find by double integration, the area lying inside the circle r = a sin and
outside the cardioid r = a (1 – cos ).
Solution. We have,
r = a sin
...(1)
r = a (1 – cos )
...(2)
Solving (1) and (2) by eliminating r, we have
sin = 1 – cos sin + cos = 1
Squaring above, we get
sin2 + cos2 + 2 sin cos = 1
1 + sin 2 = 1 sin 2 = 0 2 = 0 or = = 0 or
2
The required area is shaded portion in the fig.
Limits of r are a(1 – cos ) and a sin , limits of are 0 and
a sin
2
r
0 a (1 – cos )
a sin
Required area =
=
2
0
r2
1
d
2
2
a (1 – cos )
.
2
dr d
/ 2 2
0
a [sin 2 – (1 – cos )2 ] d
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108
Multiple Integral
a2 / 2
(sin 2 – 1 – cos 2 2 cos ] d
0
2
a2 / 2
(– 2 cos 2 2 cos ) d
=
2 0
/ 2
a2 / 2
– 2 cos 2 d 2 cos d
=
0
0
2
2
a
– 2. 2 (sin )0 / 2
=
2
4
a2
a2
– 2 sin – sin 0 =
– 2 = a 2 1 –
=
Ans.
2 2
2
2 2
4
Example 24. Find by double integration, the area lying inside a cardioid r = 1 + cos and
outside the parabola r (1 + cos ) = 1.
Solutio. We have,
r = 1 + cos ...(1)
r (1 + cos ) = 1
...(2)
Solving (1) and (2), we get
(1 + cos ) (1 + cos ) = 1
(1 + cos )2 = 1
1 + cos = 1
cos = 0
2
1
limits of r are 1 + cos and
limits of are – to .
1 cos
2
2
Required area = Area ADCBA (Shaded portion)
=
/2
=
– / 2
1 cos
r
1
1 cos
d dr =
r2
2
–
2
2
1 cos
1
1 cos
d =
1
2
/ 2
– / 2 (1 cos )
2
–
d
(1 cos )
1
2
1
1
2
d
(1
cos
2
cos
)
–
= – /2
2
2
2
2 cos
2
1 / 2
1
2
4
= 2 (1 cos 2 cos ) – sec d
0
2
4
2
/2
1
2
2
2
= 0 (1 cos 2 cos ) – 1 tan sec d
4
2
2
/ 2
/ 2
=
0
=
0
1
1 cos 2
1
2 cos – 1 tan 2 sec 2 d
2
2
2
4
/2
1 cos 2
1 2
2
2
1 2 2 2 cos – 4 sec 2 tan 2 sec 2 d
sin 2
1
2 sin – 2 tan
=
2
4
4
2
1
1
3
= 0 2 sin – tan – tan
2
4
2
2
4
6
2
2
tan 3
3
2 0
1 1 3 4
3
= 2– –
4
2 6 4 3
4
Ans.
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Multiple Integral
109
EXERCISE 2.6
3a 2
2
2
Ans. a
Ans. a2
Ans. 11
1.
Find the area of cardioid r = a(1 + cos ).
2.
3.
4.
5.
Find
Find
Find
Find
6.
Ans. ( a 2 b 2 )
2
Show that the area of the region included between the cardioides r = a(1 + cos ) and r = a (1 – cos )
the
the
the
the
Ans.
area of the curve r2 = a2 cos 2.
area enclosed by the curve r = 2 a cos
area enclosed by the curve r = 3 + 2 cos .
area enclosed by the curve
r3 = a2 cos2 + b2 sin2.
7.
a2
(3 – 8).
2
Find the area outside the circle r = 2 and inside the cardioid r = 2(1 + cos ). Ans. ( + 8)
8.
Find the area inside the circle r = 2a cos and outside the circle r = a.
is
9.
2.8
3
Ans. 2a 2
4
3
Find the area inside the circle r = 4 sin and outside the lemniscate r2 = 8 cos 2 .
8
Ans. 4 3 – 4
3
VOLUME OF SOLID BY ROTATION OF AN AREA (DOUBLE INTEGRAL)
When the area enclosed by a curve y = f (x) is revolved
about an axis, a solid is generated, we have to find out
the volume of solid generated.
Volume of the solid generated about x-axis
=
b
y2 ( x )
a y ( x ) 2 PQ dx dy
1
Example 25. Find the volume of the torus generated by revolving the circle x2 + y2 = 4 about
the line x = 3.
Solution. x2 + y2 = 4
V=
(2 PQ ) dx dy 2 (3 – x) dx dy
2
4 – x2
= 2 – 2 dx –
= 2
= 2
2
–2
2
–2
4 – x2
(3 – x ) dy
dx 3 y – x y
4 – x2
– 4 – x2
dx [3 4 – x 2 – x 4 – x 2 3 4 – x 2 – x 4 – x 2 ]
x
2
2
= 4 [3 4 – x – x 4 – x ] dx 4 3
2
2
4 – x2 3
4
x 1
sin –1 (4 – x 2 )3 / 2
2
2 3
– 2
2
= 4 6 6 24
Ans.
2
2
Example 26. Calculate by double integration the volume generated by the revolution of the
cardioid r = a (1 – cos ) about its axis.
(AMIETE, June 2010)
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110
Multiple Integral
Solution. r = a (1 – cos )
V = 2
=
y dx dy V 2 (r d dr ) y
2 d r dr ( r sin )
= 2 sin d
0
a (1 – cos ) 2
r dr
0
3 a (1 – cos )
r
2 3
3
= 2 0 sin d
0 a (1 – cos ) sin d
3
3
0
3
2 a (1 – cos )4
2 a 3
8
[16] a3
=
Ans.
3
4
12
3
0
Example 27. A pyramid is bounded by the three co-ordinate planes and the plane
x + 2y + 3z = 6. Compute this volume by double integration.
Solution.
x + 2y + 3z = 6
...(1)
x = 0, y = 0, z = 0 are co-ordinate planes.
The line of intersection of plane (1) and xy plane
(z = 0) is
x + 2y = 6
...(2)
The base of the pyramid may be taken to be the triangle
bounded by x-axis, y-axis and the line (2).
An elementary area on the base is dx dy.
Consider the elementary rod standing on this area and
O
having height z, where
6 – x – 2y
3z = 6 – x – 2y or z =
3
6 – x – 2y
Volume of the rod = dx dy, z, Limits for z are 0 and
.
3
6– x
Limits of y are 0 and
and limits of x are 0 and 6.
2
6– x
6– x
6
6
6 – x – 2y
2
z
dx
dy
dx
dy
Required volume = 0 0
0 0 2
3
6–x
2
1 6
1 6 6(6 – x) x(6 – x ) 6 – x
2
–
–
= 0 dx 6 x – xy – y 2 = 0
dx
0
3
3
2
2
2
1 6 36 – 6 x 6 x – x 2 36 x 2 –12 x
–
–
dx
3 0 2
2
4
1 6
2
2
(72 – 12 x – 12 x 2 x – 36 – x 12 x) dx
=
12 0
6
1 6 2
1 x 3 12 x 2
1
(
x
–
12
x
36)
dx
–
36
x
[72 – 216 216] 6 Ans.
=
=
12 0
12 3
2
12
0
EXERCISE 2.7
=
1. Find the volume of the sphere x2 + y2 + z2 = a2 by revolving area of the circle x2 + y2 = a2. Ans.
4 3
a
3
2.9 CENTRE OF GRAVITY
x =
x dx dy , y y dx dy
dx dy
dx dy
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Multiple Integral
111
Example 28. Find the position of the C.G. of a semi-circular lamina of radius a if its density
varies as the square of the distance from the diameter.
(AMIETE, Dec. 2010)
Solution. Let the bounding diameter be as the x-axis and a line perpendicular to the diameter
and passing through the centre is y-axis. Equation of the circle is x2 + y2 = a2. By symmetry
x 0.
y dx dy
y
dx dy
=
a2 – x 2
2
a
a
(
y
)
dx
dy
– a dx 0
y4
dx
– a 4
2
a –x
a
=
a
– a dx 0
2
( y ) y dx dy
– x2
y 2 dy
2
0
y3
a
– a dx 3
0
2
y 3 dy
a 2 – x2
3
a
–a
a
4
–a
(a 2 – x 2 )2 dx
2 ( a 2 – a 2 sin 2 ) 2 a cos d
–
2
2 ( a 2 – a 2 sin 2 ) 3/ 2 a cos d
–
2
3
=
4
Put x = a sin
(a 2 – x 2 )3/ 2 dx
3
42
3a 5 3
3a 8 16 32a
=
3
1
4
4 15 3 15
42 2
4
2 a5
–
2
4 a4
–
2
cos5 d
cos 4 d
32 a
Hence C.G. is 0,
15
Example 29. Find C.G. of the area in the positive quadrant of the curve
x2/3 + y2/3 = a2/3.
x dx dy , y y dx dy
Solution. For C.G. of area; x
dx dy
dx dy
a
x=
0
a
( a 2 / 3 – x 2 / 3 )3 / 2
0
( a 2 / 3 – x 2 / 3 )3 / 2
0 dx 0
a
=
x dx
0 x dx (a
a
0
2/3
a
dy
– x 2 / 3 )3/ 2
dx (a 2 / 3 – x 2 / 3 )3 / 2
( a 2 / 3 – x 2 / 3 )3 / 2
0 x dx y 0
dy
Ans.
a
( a 2.3 – x 2 / 3 )3 / 2
dx y 0
0
0
3
2/3
–
a cos ( a
2
0 2/3
– a2 / 3
(a
2
[Put x = a cos3]
a 2 / 3 cos 2 )3/ 2 (– 3a cos2 sin d )
cos 2 )3/ 2 (– 3a cos 2 sin d )
5 6
2 2a
4
3
3
3
2
4
5
02 3 a cos sin cos sin d a 02 sin cos d 2 2
=
5 3
2
3
2
4
2
02 3a sin cos sin d
02 sin cos d 2 2
2 4
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112
Multiple Integral
=
3 4a
3 11
2 2
(2) (6) a
256 a
, Similarly, y 256 a
1 9 7 5 3 1
315
315
2 2 2 2 2 2
256 a 256 a
,
Hence, C.G. of the area is
.
315 315
Example 30. Find by double integration, the centre of gravity of the area of the cardioid
r = a (1 + cos ).
Solution. Let ( x , y ) be the C.G. the cardioid
By Symmetry, y 0.
x dx dy
x =
A
=
a (1 cos )
– 0
A
dx dy
A
(r cos ) (r d dr )
a (1 cos )
– 0
r
=
dx dy
A
x dx dy
3 a ( a cos )
0
a (1 cos )
a (1 cos )
d 0
r d dr
– cos d 3
a (1 cos ) 2
– cos d 0
–
cos d .
r dr
r dr
a3
(1 cos )3
3
a2
r2
d
(1 cos )2
d
–
– 2
2
0
3
a3
2
2
2
cos
–
1
1
2
cos
–
1
d
3 –
2
2
=
a2
2
– 1 d
1 2 cos
2 –
2
a3
a2
2
6
2 cos
– 1 8 cos d
4 cos 4 d
=
–
3 –
2
2
2
2
3
8a
8
– cos6 d 2a 2 cos 4 d
=
2 cos
–
3 –
2
2
2
2 8a 3
8
– cos6 d 4 a 2 cos 4 d
=
2 cos
0
0
3
2
2
2
/
2
16a3 / 2
(2 cos8 t – cos6 t ) (2 dt ) 4a 2 cos4 t (2 dt )
=
0
3 0
3
3 1
32 a 2 7 5 3 1 5 3 1
–
8a 2
=
3 8 6 4 2 2 6 4 2 2
42 2
32a3 35 5
8a 3 15
16
5a
2 3
= 3 128 – 32 8a 16 3 128 2
8a 3 24
2.10 CENTRE OF GRAVITY OF AN ARC
Example 31. Find the C.G. of the arc of the curve
x = a ( + sin ), y = a(1 – cos ) in the positive quadrant.
Solution. We know that, x
Ans.
xds , y yds
ds
ds
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Multiple Integral
113
2
2
dx dy
d
d d
Now, ds =
{a 2 (1 cos )2 a 2 sin 2 } d a 1 2 cos cos2 sin 2 d
=
d 2a cos d
2
2
a 2 sin cos d
0
2
2
2
= a 1 2 cos 1 d a 2(1 cos ) d a 4 cos
x =
=
x dx
ds
a
2
0 a ( sin ) 2a cos 2 d
0
2a cos
d
2
a
2
cos 2 2 sin 2 cos 2 d 2
0
2 sin 2
0
2 (2t
0
cos t 2 sin t cos 2 t ) 2 dt
cos3 t 2
1
4
= 2a t sin t cos t –
2a – 1 a –
3
2
3
3
0
a (1 – cos ) 2a cos d a 2 sin 2 cos d
y
ds
0
0
2
2
2
y=
ds
0 2a cos 2 d
0 cos 2 d
a r sin 3
20
4a
2a
4 2a
=
Hence, C.G. of the arc is a – ,
3
2
3
3 3
3 2 sin
2
Ans.
0
EXERCISE 2.8
1.
2.
3.
Find the centre of gravity of the area bounded by the parabola y2 = x and the line x + y = 2.
1
8
Ans. , –
2
5
Find the centroid of the tetrahedron bounded by the coordinate planes and the plane x + y + z = 1, the
1 1 2
density at any point varying as its distance from the face z = 0.
Ans. , ,
5 5 5
Find the centroid of the area enclosed by the parabola y2 = 4 ax, the axis of x and latus rectum.
3a 3a
Ans. ,
20 16
4.
5.
a 2
, 0
Ans.
8
Find the centroid of solid formed by revolving about the x-axis that part of the area of the ellipse
Find the centroid of the loop of curve r2 = a2 cos 2 .
x2
6.
7.
y2
3a
Ans. , 0
1 which lies in the first quadrant.
a
b2
8
Find the average density of the sphere of radius a whose density at a distance r from the centre of the
k
r3
0 1
sphere is 0 1 k 3 .
2
a
The density at a point on a circular lamina varies as the distance from a point O on the circumference.
Show that the C.G. divides the diameter through O in the ratio 3 : 2.
2
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114
Multiple Integral
1.11 TRIPLE INTEGRATION
Let a function f(x, y, z) be a continuous at every point of a finite region S of three dimensional
space. Consider n sub-spaces S1, S2, S3, ... Sn of the space S.
If (xr, yr, zr) be a point in the rth subspace.
n
f (x , y , z ) S , as n , S 0 is known as the triple integral of
The limit of the sum
r
r
r
r
r
r 1
f (x, y, z) over the space S.
Symbolically, it is denoted by
f ( x, y, z) dS
S
x2
y2
z2
x1
y1
z1
( x) dx
It can be calculated as
f ( x, y, z) dx dy dz. First we integrate with respect to z
treating x, y as constant between the limits z1 and z2. The resulting expression (function of x, y) is
integrated with respect to y keeping x as constant between the limits y1 and y2. At the end we
integrate the resulting expression (function of x only) within the limits x1 and x2.
x2 b
x1 a
y2 2 ( x )
y1 1( x )
z2 f2 ( x, y )
( x, y) dy
f ( x, y, z) dz
z1 f1( x, y )
First we integrate from inner most integral w.r.t. z, then we integrate with respect to y and
finally the outer most with respect to x.
But the above order of integration is immaterial provided the limits change accordingly.
Example 32. Evaluate
Solution.
1
R
( x y z ) dx dy dz, where R : 0 x 1, 1 y 2, 2 z 3.
3
( x y z) 2
dy
2
2
1
2
0
1
1
2
1
2
1
1
dx dy [( x y 3) 2 ( x y 2)2 ]
dx (2 x 2 y 5) .1. dy
=
2 0
2 0
1
1
2
0
dx
dy
3
1
( x y z) dz =
dx
2
1
=
2
1
= 8
2
(2x 2 y 5) 2
1
dx
4
1 8
0
1
1
0
(4x 16) . 2 dx
Example 33. Evaluate the integral :
Solution.
log 2
0
=
=
=
=
=
x
x log y
1
0
1
0
dx [(2x 4 5)2 (2x 2 5)2 ]
1
x2
1
9
(x 4) dx 4 x 4
2
0 2
2
log 2
0
x
0
x log y
0
Ans.
e x y z dz dy dx.
xyz
e
dz dy dx.
e dz
e dx e dy(e )
e dx e dy
1)
e dx e dy (e
. e 1)
e dx e dy (e
e dx e ( y e 1) dy = e dx ( ye 1) e e . e dy
e dx ( ye 1) e e = e dx [(xe 1) e e 1 e ]
e dx [ xe e e 1 e ] ( xe e e ) dx
0
0
log 2
0
log 2
0
log 2
0
log 2
0
log 2
0
x
x
x
x
0
x
0
x
y
x log y
0
y
y
x log y
0
log 2
x
x
x
x
0
log 2
y
z x log y
0
0
x
x
y
log y
x
0
x
x
y
x
y
0
0
x
log 2
z
x
2x
y
x
2x
0
log 2
xy x
0
x
x
x
x
2x
x
0
x
log 2
3x
3x
x
0
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Multiple Integral
115
log 2
e3 x
e3 x
e3x
1.
dx
ex
3
3
3
0
= x
log 2
x
e 3x e3x
e 3x
ex
3
9
3
0
log 2 3 log 2 e3 log 2 e3 log 2
1 1
e
e log 2 1
3
9
3
9 3
3
3
log 2 log 23 elog 2
elog 2
1
1
=
e
elog 2 1
3
9
3
9 3
8
8 8
1 1
8
19
= log 2 2 1 log 2
3
9 3
9 3
3
9
=
Example 34. Evaluate
Solution. I =
=
log 2
0
0
=
log 2
x
0
x
0
log 2
0
e
x
0
xy
x y
0
zx y
e
0
e x y z dx dy dz.
(M.U. II Semester, 2005, 2003, 2002)
dx dy
e x y (e x y 1) dx dy =
log 2
0
4x
2x
log 2
2x
0
x
2 x e2 y
x
y
e . 2 e . e dx =
0
log 2
Ans.
x
e 2( x y ) e ( x y ) dx dy
0
e4 x
e2x
e2x
e x dx
2
2
log 2
0
e
e
1 1 1
e
e
e 2 log 2 e 2 log 2
x
elog 2 1
= 8 2 4 e
8
2
4
8
2
4
0
elog 16 elog 4 elog 4
1 1 1
elog 2 1
=
8 2 4
8
2
4
16 4 4
1 1 1
5
= 2 1
Ans.
8
2
4
8
2
4
8
Example 35. Evaluate
R
4 log 2
( x 2 y 2 z 2 ) dx dy dz
where R denotes the region bounded by x = 0, y = 0, z = 0 and x + y + z = a, (a > 0)
Solution.
R
Y
( x 2 y 2 z 2) dx dy dz
x+y+z=a
or
z=a–x–y
Upper limit of z = a– x – y
On x-y plane, x + y + z = a becomes x + y = a
as shown in the figure.
Upper limit of y = a – x
Upper limit of x = a
=
=
=
=
a
0
a
0
a
0
a
x0
dx
dx
dx
ax
y0
ax
0
ax
0
dy
a x y
z0
2
2
2
( x y z ) dz =
x=0
x+
O
a
0
dx
y=0
ax
0
y=
a
x=a
X
ax y
z3
dy x 2 z y 2 z
3 0
(a x y)3
dy x 2 (a x y) y 2 (a x y)
3
2
(a x y)3
2
2
3
x (a x) x y (a x) y y
dy
3
ax
x2 y2
y 3 y 4 (a x y ) 4
dx x 2 (a x ) y
(a x )
2
3
4
12
0
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116
Multiple Integral
=
=
x2
(a x)3 (a x)4 (a x)4
dx x 2 (a x)2
(a x) 2 (a x)
2
3
4
12
0
2
4
4
a x
a 1
(a x)
(a x)
2
2 2
3
4
(a x)
dx
(a x 2 a x x )
dx
2
6
2
6
0
0
a
5 a
1
x 3 ax 4 x5 (a x)
a5 a5 a5 a5 a5
= a2
Ans.
3
4
10
30
6
4 10 30 20
2
0
dx dy dz
Example 36. Compute
if the region of integration is bounded by the
( x y z 1)3
coordinate planes and the plane x + y + z = 1.
(M.U., II Semester 2007, 2006)
Solution. Let the given region be R, then R is expressed as
0 z 1 – x – y, 0 y 1 – x, 0 x 1.
R
=
1
0
dx
=
1
2
=
1
2
=
1
2
dx dy dz
=
( x y z 1)3
1
0
dx
1 x
0
1 x y
dy
1 x y
dz
( x y z 1)3
0
1
dy
2
2(
x
y
z
1)
0
1
1 x
1
1
dx
dy
2
2
0
0
( x y 1)
( x y 1 x y 1)
1 x
1
1 x 1
1
1 1 y
1
dx
dy
dx
4
2 0 4 x y 1 0
0
0
( x y 1) 2
1
1
1
1 1 1 x 1
1
1 x
dx
dx
x 1 1 x x 1
2 0 4
2 x 1
0
4
1 x
0
1
(1 x) 2 x
1 1
log( x 1) log 2
8
2
2 2
0
1
5
= log 2
2
16
=
1
2
Example 37. Evaluate
1
1 5
log 2
8
2 8
Ans.
2
x yz dx dy dz throughout the volume bounded by the planes x = 0,
x y z
= 1.
a b c
y = 0, z = 0,
(M.U. II Semester 2003, 2002, 2001)
Z
Solution. Here, we have
I =
2
x yz dx dy dz
...(1)
Putting x = au,
y = bv,
z = cw
dx = a du, dy = b dv, dz = c dw in (1), we get
I =
c
a2bc u 2 v w a bc du dv dw
Limits are for u = 0, 1 for v = 0, 1 – u and for w = 0, 1 – u – v
u+v+w = 1
I=
1
uo
1 u
v0
=
a3b2c 2
2
=
a3b 2c 2
2
1 u v
w0
1
1 u
0
0
1
1 u
0
0
3 2 2
2
a b c u vw du dv dw =
1
1u
0
0
a
X
b
1 u v
w2
ab c u v
2 0
3 2 2
Y
O
2
du dv
u 2v(1 u v)2 du dv
u 2v (1 u)2 2(1 u)v v 2 du dv
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Multiple Integral
117
=
a3b 2c 2
2
=
a3b2c 2
2
=
a3b 2c 2
2
3 2 2
=
abc
2
1
1 u
0
0
1
0
1
0
1
0
u 2 [(1 u)2 v 2 (1 u) v 2 v 3 ] du dv
1u
v2
v3 v4
u 2 (1 u)2
2(1 u)
2
3
4 0
du
(1 u)4 2(1 u) 4 (1 u)4
u2
du
2
3
4
a3b2c 2
u 2 (1 u)4
du =
24
12
1
0
u3 1 (1 u)5 1 du
a 3b2c 2 2! 4! a 3 b 2 c 2
abc
a3b2c 2 3 5
.
=
Ans.
(3, 5)
.
2520 .
24
24
24
7!
8
2.12 INTEGRATION BY CHANGE OF CARTESIAN COORDINATES INTO
SPHERICAL COORDINATES
Sometime it becomes easy to integrate by changing the cartesian coordinates into spherical
coordinates.
The relations between the cartesian and spherical polar co-ordinates of a point are given by the
relations
x = r sin cos
y = r sin sin
z = r cos
dx dy dz = | J | dr d d
= r 2 sin dr d d
Note. 1. Spherical coordinates are very useful if the expression x2 + y2 + z2 is involved in the
problem.
2. In a sphere x2 + y2 + z2 = a2 the limits of r are 0 and a and limits of are 0, and
that of are 0 and 2.
3 2 2
=
(x
Example 38. Evaluate the integral
2
y 2 z 2 ) dx dy dz taken over the volume
enclosed by the sphere x2 + y2 + z2 = 1.
Solution. Let us convert the given integral into spherical polar co-ordinates. By putting
x = r sin cos ; y = r sin sin ; z = r cos
2
=
2
4
2
0
5
5
0
=
d
0
sin d
1
0
Example 39. Evaluate
x2 + y2 + z2 = a2.
Solution. Here, we have
r 4 dr
2
1
0
0
0
( x 2 y 2 z 2 ) dx dy dz =
2
0
d
0
r 2 (r 2 sin d d dr)
1
r5
1
sin d =
5
5 0
2
0
d cos 0
2
5
2
0
d
Ans.
(x
2
y 2 z 2 ) dx dy dz over the first octant of the spheree
(M.U. II Semester 2007)
I =
(x
2
y 2 z 2 ) dx dy dz
...(1)
2
Putting x = r sin cos , y = r sin sin , z = r cos and dx dy dz = r sin dr d d in (1),
we get
Limits of r are 0, a for are 0,
for are 0, .
2
2
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118
Multiple Integral
2
2
a
I=
0
0
0
r 2 . r 2 sin dr d d =
2
0
d
2
0
sin d
a
0
r 4 dr
x 2 y 2 z 2 r 2 sin 2 cos 2 r 2 sin 2 sin 2 r 2 cos 2
r 2 sin 2 r 2 cos2 r 2
a
5
5
5
a
a
/2
/2 r
. .
= 0 cos 0 = . (1) .
Ans.
2
5
10
5 0
dx dy dz
Example 40. Evaluate
throughout the volume of the sphere x2 + y2 + z2 = a2.
2
x y2 z2
(M.U. II Semester 2002, 2001)
Solution. Here, we have
dx dy dz
I =
...(1)
2
x y2 z2
2
Putting x = r sin cos , y = r sin sin , z = r cos and dx dy dz = r sin dr d d in (1),
we get
The limits of r are 0 and a, for are 0 and
for are 0 and
in first octant.
2
2
2
I= 8
2
I= 8
0
2
0
d
0
2
0
a
0
r 2 sin dr d d
r2
sin d
a
0
/2
dr = 8 0
= 8 .1. a = 4a
2
[Sphere x2 + y2 + z2 lies in 8 quadrants]
cos 0 /2 r a0 8 2 0 (0 1)(a 0)
Ans.
EXERCISE 2.9
Evaluate the following :
1
2
3
1
2
3
4
x
x y
0
0
1.
2.
3.
2
dx dy dz
z dz dy dx
(M.U., II Semester 2002)
Ans. 48
(R.G.P.V. Bhopal I Sem. 2003)
Ans. 70
0
1
1
1
0
1
1
1
1
1
z
xz
( x 2 y 2 z 2 ) dx dy dz
2
Ans. 6
y 2 z 2 ) dz dy dx
4.
0 0 0 (x
5.
1 0 x z ( x y z ) dx dy dz
6.
(x y z)
(AMIETE, June 2006)
Ans. 1
(AMIETE, Summer 2004)
Ans. 0
dx dy dz, where R : 1 x 2; 2 y 3; 1 z 3
Ans. 2
R
7.
2
2
1
xy 2 z dx dy dz (AMIETE, Dec. 2007)
dx
1
1
Ans. 26
8.
0
0
dy
2
2
1
2
(M.U. II Semester, 2003)
1
10.
2
x yz dz Ans. 1
x yz dx dy dz throughout the volume bounded by x = 0, y = 0, z = 0, x + y + z = 1.
0
9.
3
0
1 x
0
1 x2 y2
0
dz dy dx Ans.
1
3
e
11.
1.
1
log y
1
ex
1
Ans.
log z dz dx dy Ans.
1
2520
1 2
(e 8e 13)
2
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Multiple Integral
12.
119
y dx dy dz, where T is the region bounded by the surfaces x = y2, x = y + 2, 4z = x2 + y2 and
T
z = y + 3.
2
x
2x 2 y
0
0
0
13.
14.
(x y z)
ex y z
(AMIETE Dec. 2008)
12
6
1 e
e
1 1 1
dz dy dx Ans.
[e4 1] [e2 1] (M.U. II Sem., 2003)
3 6
3 6 3 2
dx dy dz over the tetrahedron bounded by the planes x = 0, y = 0, z = 0 and
x + y + z = 1.
15.
17.
18.
a
ax
0
0
a x y
0
1
z
xz
1
2
0
y
xz
x y
0
0
xy
Ans.
5
x 2 dx dy dz Ans. a 16.
60
2
(4 x 2) /2
2
(4 x 2) /2
8 x2 y 2
x2 3 y 2
( x y z) dz dx dy
(M.U. II Semester, 2000, 02)
( x y z) dx dy dz
(M.U. II Semester 2004)
20.
a b c dx dy dz over the volume of the ellipsoid a b
x y z dx dy dz throughout the volume of the tetrahedron
21.
x2
y2
z2
x2
y2
2
2
2
2
2
Ans. 0
Ans. 16
z2
c2
24.
25.
dx dy dz
taken throughout the volume of the sphere x2 + y2 + z2 = 1, lying in the first
1 x2 y 2 z 2
2
8
a 2
Ans.
h
2
0
Ans.
2d
a(1 cos )
0
r dr
h
0
/2
a sin
(a 2 r 2 )/ a
0
0
0
r
1 a(1 cos ) dz
z dx dy dz over the volume common to the sphere x
Ans.
r d dr dz
2
2
dx dy dz
(1 x 2 y 2 z 2 ) 2
dx dy dz
V
27.
2
2
2 3/ 2
2a5
15
2
Ans.
8
Ans.
where V is the volume in the first octant.
over the volume bounded by the spheres x 2 + y 2 + z 2 = 16 and
(x y z )
x2 + y2 + z2 = 25.
28.
2
T
5a 3
64
+ y2 + z2 = a 2 and the cylinder
x2 + y2 + z2 = ax.
26.
4
abc
3
1
l m n
Ans. (l m n) .
lmn
octant.
23.
= 1. Ans.
l 1 m 1 n 1
x 0, y 0, z 0, x + y + z 1.
22.
dz dy dx Ans. 8 2
x2 y 2 z 2
x2 y2 z2
dx dy dz throughout the volume of the ellipsoid 2 2 2 = 1.
a
b
c
a 2 b2 c 2
2
Ans.
abc
4
19.
1
1
8
(M.U. II Semester, 2001, 03)
2
2
Ans. 4 log (5/4)
2
z dx dy dz over the volume bounded by the cylinder x + y = a and the paraboloid
x2 + y2 = z and the plane z = 0.
Ans.
a 8
12
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120
Multiple Integral
2.13 VOLUME =
dx dy dz.
The elementary volume v is x . y . z and therefore
the volume of the whole solid is obtained by evaluating the
triple integral.
V = x y z
V dx dy dz.
ρ dx dy dz if is the density..
(ii) In cylindrical co-ordinates, we have V r dr dφ dz
V
Note : (i) Mass = volume × density =
(iii) In spherical polar co-ordinates, we have V r 2 sin θdr dθ dφ
V
Example 41. Find the volume of the tetrahedron bounded by the planes x = 0, y = 0, z = 0
and x + y + z = a.
(M.U. II Semester, 2005, 2000)
Solution. Here, we have a solid which is bounded by x = 0, y = 0, z = 0 and x + y + z = a
Z
planes.
The limits of z are 0 and a – x – y, the limits of y are 0 and 1 – x,
the limits of x are 0 and a.
z = a–x–y
V=
=
=
=
=
=
a
x0
a
x0
x0
a
0
a
0
y0
ax y
dx dy dz =
z0
a
x0
ax
y0
x+y+z=a
z a0 x y dx dy
ax
(a x y) dx dy
O
y0
2
a
0
a
ax
Y
ax
y
ay xy
2 0
dx
z=0
dxdy
2
(a x )
dx
a(a x) x(a x)
2
2
a2
x2
2
a ax ax x 2 ax 2 dx
a2
x2
ax dx
2
2
X
O
y=a–x
y=x
a
3
a2
ax 2 x 3
1 1 1 a
a3
= .x
.
2
6
2 2 6 6
2
0
Y
(a, 0, 0)
Ans.
X
Example 42. Find the volume of the cylindrical column standing on the area common to the
parabolas y2 = x, x2 = y and cut off by the surface z = 12 + y – x2. (U.P., II Sem., Summer 2001)
Y
Solution. We have,
y2 = x
x2 = y
z = 12 + y – x2
V=
=
1
0
dx
x
x2
dy
2
y
12 y x 2
1
dz =
0
0
x
2
(12 y x 2 ) dy
2
X
x
=y
O
x
y2
dx 12 y
x2 y
2
2
0
x
1
x
dx
=x
Y
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X
Multiple Integral
=
1
0
121
x
x4
5/ 2
2
x 4 dx
12 x x 12 x
2
2
1
2
x2 2 7 / 2
x5 x 5
3/2
x 4x3
= 12x
4 7
10 5
3
0
1 2
1 1
1 2 1 1
560 35 40 14 28 569
4
4
=
Ans.
4 7
10 5
4 7 10 5
140
140
Example 43. A triangular prism is formed by planes whose equations are ay = bx, y = 0 and
x = a. Find the volume of the prism between the planes z = 0 and surface z = c + xy.
(M.U. II Semester 2000; U.P., Ist Semester, 2009 (C.O) 2003)
= 8
Z
bx
a
a
Solution.
Required volume =
0
a
=
=
=
0
a
0
0
bx
a
c xy
dz dy dx
(c xy) dy dx
0
x=a
bx
a
O
xy 2
cy
dx
2
0
0
z=0
Y
ay = bx
X
cbx b 2 3
bc
2 x dx
a
2a
a
a
z = c + xy
0
a
x2
b2
2
2 0 2a
a
x4
4 0
a b c b2 a 2 a b
(4c a b)
2
8
8
2.14 VOLUME OF SOLID BOUNDED BY SPHERE OR BY CYLINDER
=
Ans.
We use spherical coordinates (r, , ) and the cylindrical coordinates are (, , z) and the
relations are x = cos , y = sin .
Example 44. Find the volume of a solid bounded by the spherical surface x2 + y2 + z2 = 4a2
and the cylinder x2 + y2 – 2 a y = 0.
x2 + y2 + z2 = 4a2
Solution.
2
...(1)
2
x +y –2ay = 0
...(2)
Considering the section in the positive quadrant of the
xy-plane and taking z to be positive (that is volume above
the xy-plane) and changing to polar co-ordinates,
(1) becomes
r2 + z2 = 4a2
z=
2
4a r
z2 = 4 a2 – r2
Y
(0, a)
X
2
O
(2) becomes r2 – 2 a r sin = 0 r = 2a sin
Volume =
dx dy dz
= 4
/2
d
0
Y
2a sin
r dr
0
4a 2 r 2
dz
0
(Cylindrical coordinates)
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X
122
Multiple Integral
= 4
= 4
=
=
4
3
/2
d
0
/2
0
32 a
3
0
r dr z 0
4a 2 r 2
2a sin
=
( 8a 3 cos3 8a 3 ) d
/ 2
/2
d
0
4
3
8 4a 3
3
/2
0
/ 2
2a sin
r dr . 4a 2 r 2
0
(4a 2 4a 2 sin 2 )3/2 8a3 d
(1 cos3 ) d
0
1
3
1 4 cos 3 4 cos d
0
/ 2
3
=
= 4
1
d (4a 2 r 2 )3/2
3
0
/ 2
0
3
2a sin
32 a
1
3
sin 3 sin
3
12
4
0
32 a 3
3
32 a 3
1 3
2 12 4 = 3
2
2 3 Ans.
Example 45. Find the volume enclosed by the solid
2/3
2/3
2/3
x
y
z
=1
a b c
Solution. The equation of the solid is
2/3
2/3
2/3
x
y
z
a
b
c = 1
1/3
x
Putting
= u
x = a u3
a
1/3
y
= v
y = b v3
b
1/3
z
= w
z = c w3
c
The equation of the solid becomes
u2 + v2 + w2 = 1
V =
d x = 3 au2 du
d y = 3 bv2 dv
d z = 3 c w2 dw
...(1)
dx dy dz
...(2)
On putting the values of dx, dy and dz in (2), we get
V =
27abc u v w du dv dw
2 2
2
...(3)
(1) represents a sphere.
Let us use spherical coordinates.
u = r sin cos ,
v = r sin sin ,
w = r cos ,
du dv dw = r2sin dr d d
On substituting spherical coordinates in (3), we have
V = 27abc . 8
= 216 a b c
1
r0
1
r0
/2
0
r 8 dr
/2
r 2 sin 2 cos 2 . r 2 sin 2 sin 2
0
/2
. r2 cos2 . r2 sin dr d d
sin 2 cos2 d
0
3 3
1
r9 2 2 3
= 216 a b c .
9 0 2 3 2
/ 2
sin 5 cos 2 d
0
3
3
3 3
3
1 2 2 1
2
2
. .
= 24 a b c . .
2
2
9
3
9
2
2
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Multiple Integral
123
2
1 1
3
2 2
2!
2
.
= 6abc .
=
2!
7 5 3 3
2 2 2 2
1
1
4
.
abc
4
7 5 3 35
222
Ans.
Example 46. Find the volume bounded above by the sphere x2 + y2 + z2 = a2 and below by
the cone x2 + y2 = z2.
(U.P. II Semester 2002)
6abc .
Solution. The equation of the sphere is x2 + y2 + z2 = a2
...(1)
x2 + y2 = z2
and that of the cone is
...(2)
In polar coordinates x = r sin cos , y = r sin sin , z = r cos
The equation (1) in polar co-ordinates is
Z
(r sin cos )2 + (r sin sin )2 + (r cos )2 = a2
r2 sin2 cos2 + r2 sin2 sin2 + r2 cos2 = a2
r2 sin2 (cos2 + sin2 ) + r2 cos2 = a2
r2 sin2 + r2 cos2 = a2
O
r2 (sin2 + cos2 ) = a2
r2 = a2 r = a
The equation (2) in polar co-ordinates is
X
(r sin cos )2 + (r sin sin )2 = (r cos )2
r2 sin2 (cos2 + sin2 ) = r2 cos2 r2 sin2 = r2 cos2
x2 + y2 + z2 = a2
Y
x2 + y2 = z2
4
Thus equations (1) and (2) in polar coordinates are respectively,
r = a
and
=
4
The volume in the first octant is one fourth only.
tan2 = 1 tan = 1
=
and from 0 to .
4
2
The required volume lies between x2 + y2 + z2 = a2 and x2 + y2 = z2.
Limits in the first octant : r varies 0 to a, from 0 to
V = 4
2
4
0
= 4
0
2
d
0
4
a
0
r 2 sin dr d d = 4
d
0
3
sin d .
0
2
a
4a
3
3
3
2
0
4
0
a
r3
sin d
30
d cos 04
2 3
1
a 1
3
2
2.15 VOLUME OF SOLID BOUNDED BY CYLINDER OR CONE
=
4a3
02
3
1
2 1
Ans.
We use cylindrical coordinates (r, , z).
Example 47. Find the volume of the solid bounded by the parabolic y2 + z2 = 4x and the
plane x = 5.
Solution. y2 + z2 = 4x, x = 5
5
V=
0
dx
2 x
2 x
dy
4x y2
4x y 2
dz 4
5
0
dx
2 x
0
dy
4 x y2
dz
0
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124
Multiple Integral
= 4
= 4
5
dx
2 x
0
0
5
y
dx
2
0
dy z 0
4 x y2
5
4
dx
0
2 x
dy 4 x y 2
0
2 x
4x
y
4x y 2
sin 1
2
2 x 0
= 4
5
0 2 x 2 dx 4
0
5
x dx
0
5
x2
= 4 50
2 0
Example 48. Calculate the volume of the solid bounded by the following surfaces :
x2 + y2 = 1,
z = 0,
2
Solution.
x+y+z=3
2
x +y = 1
x+y+z = 3
z = 0
Required Volume =
Ans.
...(1)
...(2)
...(3)
3x y
0
dx dy dz = dx dy z
(3 x y) dx dy
On putting x = r cos , y = r sin , dx dy = r d dr, we get
=
=
2
0
(3 r cos r sin ) r d dr =
2
d
0
1
3r 2 r 3
r3
d
cos
sin =
2
3
3
0
2
0
1
(3r r 2 cos r 2 sin ) dr
0
1
3 1
2 3 cos 3 sin d
2
1
1
1
1
1
3
= sin cos = 3 sin 2 cos 2 3
Ans.
3
3
3
2
3
3
0
Example 49. Find the volume bounded by the cylinder x2 + y2 = 4 and the planes y + z = 4
and z = 0.
Z
Solution. x2 + y2 = 4 y = 4 x 2
y + z = 4 z = 4 – y and z = 0
y
x varies from –2 to + 2.
V=
=
=
=
dx dy dz =
2
dx
2
2
dx
2
2
2
4 x2
2
dx
2
4x
4 x
dy
4 y
dy (4 y) =
z=
4
dz
0
O
dy z 0
4 x2
4x
2
4 y
4 x2
2
2
+
2
Y
2
x +y =4
y2
dx 4 y
2
2
2
4 x2
X
4 x2
1
1
dx 4 4 x 2 (4 x 2 ) 4 4 x 2 (4 x 2 )
2
2
2
2
4
x
x
4 x 2 dx 8
4 x 2 sin 1 = 16
Ans.
2
2
2
2
2
Example 50. Find the volume in the first octant bounded by the cylinder x2 + y2 = 2 and the
planes z = x + y, y = x, z = 0 and x = 0.
(M.U. II Semester 2005)
Solution. Here, we have the solid bounded by
= 8
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Multiple Integral
125
2
Z
2
x + y = 2 (cylinder)
(or
r2 = 2)
z = x + y z = r (cos + sin )
y = x
r sin = r cos
tan = 1
q=
4
x = 0 r cos = 0 cos = 0 =
2
z varies from 0 to r (cos + sin )
r varies from 0 to 2
(plane)
(plane)
O
X
varies from
to
4
2
V =
=
=
=
/ 2
/ 4
/ 2
/ 4
/ 2
/ 4
Y
Y
x
=
y=x
y
= /2
2
r0
2
r0
2
r (cos sin )
==
/ /
44
Y
x=0
r dr d dz
z0
r (cos sin )
0
r z
X
O
dr d
x2 + y2 = 2
r= 2
r 2 (cos sin ) dr d
r 0
2
r3
2 2
(cos sin ) d =
3 0
3
/4
/2
/ 2
(cos sin ) d
/ 4
1 2 2
1
Ans.
(1 0)
3
2
2
Example 51. Show that the volume of the wedge intercepted between the cylinder
x2 + y2 = 2ax and planes z = mx, z = nx is (m – n) a3.
(M.U. II Semester, 2000)
Solution. The equation of the cylinder is x2 + y2 = 2 a x
z = mx
we convert the cartesian coordinates into cylindrical coordinates.
x = r cos
y = r sin
=
2 2
2 2
/2
sin cos / 4 =
3
3
x2 + y2 = 2ax r2 = 2ar cos
r = 2a cos
r varies from 0 to 2a cos
z = nx
to
2
2
z varies from z = nx (z = nr cos ) to z = m x (z = m r cos )
varies from
and
V= 2
= 2
= 2
/2
0
/ 2
0
/2
0
= 2 (m n)
2a cos
r0
2a cos
r 0
mr cos
r dr d dz
Y
z nr cos
r = 2a cos
mr cos
r z nr cos dr d
2a cos
r .(m n) r cos dr d
O
X
r0
/2
0
2a cos
r 2 cos dr d
r0
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126
Multiple Integral
= 2 (m n)
/ 2
0
2a cos
r3
3 0
cos d = 2 (m n)
/2
0
8a3
cos3 cos d
3
16 (m n) 3 / 2
16 (m n) 3 3 1
a
cos 4 d =
. a . . . (m n) a3 Ans.
3
4 2 2
3
0
Example 52. A cylindrical hole of radius b is bored through a sphere of radius a. Find the
volume of the remaining solid.
(M.U. II Semester 2004)
Z
Solution. Let the equation of the sphere be
2
2
2
2
x +y +z = a
z = a2 – r 2
Now, we will solve this problem using cylindrical coordinates
x = r cos
y = r sin
z = z
=
a 2 ( x 2 y 2 ) i.e.,
Limits of z are 0 and
Oz=0
a2 r 2
a
Y
Limits of r are a and b.
2
and the limits of are 0 and
/2
=
a
8
(a
V = 8
X
2
0
rb
/2
a
0
a r
2
r dr d dz = 8
z0
2
/2
0
rb
z 0 a
2
r2
r dr d
r 2 )1/2 . r dr d
r b
a
(a 2 r 2 )3/2
3/ 2
8
1
. d =
3
0
2 b
3
3
8 2
4 2
/ 2
(a b 2 ) 2 0
(a b 2 ) 2
=
3
3
Example 53. Find the volume cut off from the paraboloid
= 8
a
/2
/ 2
3
(a 2 b2 ) 2 d
0
Ans.
y2
z = 1 by the plane z = 0.
4
Solution. We have
y2
x2
z = 1
(Paraboloid)
4
z = 0
(x-y plane)
x2
z varies from 0 to 1 – x2 –
(M.U. II Semester 2005)
...(1)
...(2)
2
y
4
Z
y varies from 2 1 x 2 to 2 1 x 2
x varies from –1 to 1.
V=
dx dy dz =
1
=
1
2 1 x
2
2 1 x
1
= 4
2
dx
1
2 1 x2
2 1 x2
dy
y2
2
1 x
4
0
2
y
2
1 x
dx dy
4
0
1
2 1 x2
0
y2
2
1 x
dx dy
4
dz
–1
–2
O
2
1
X
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Y
Multiple Integral
127
Y
= 4
2 1 x2
y3
2
(1 x ) y
12
0
1
0
y = 2 1 – x2
. dx
x2 +
1
8
(1 x 2 ) . 2 1 x 2 (1 x 2 )3/ 2 d x
12
0
X
1
2
2 3/ 2
2 3/ 2
= 4
2(1 x ) 3 (1 x ) d x
0
On putting x = sin , we get
= 4
y2
=1
4
X
O
V = 4
1
4
16
(1 x 2 )3/2 dx
3
3
0
/2
y = –2 1 – x2
( sin 2 )3/2 cos d
Y
0
/2
16
16 3 1
cos 4 d . . .
Ans.
3 0
3 4 2 2
Example 54. Find the volume enclosed between the cylinders x2 + y2 = a x, and z2 = a x.
Solution. Here, we have x2 + y2 = ax
...(1)
z2 = ax
...(2)
=
V=
=
=
=
2
2
dx dy dz
a
dx
0
a
dx
0
a
ax x 2
ax x 2
dy
ax
ax
ax x 2
ax x
dz = 2
ax
dy z 0 = 2
2
a
dx
0
dx
0
ax dx 2 ax x 2 4 a
0
a
ax x 2
2
a x
ax x 2
ax x
2
2
dy
ax
dz
0
dy ax = 2
a
ax dx y
ax x 2
0
Z
a
x a x dx
0
2
Putting x = a sin so that dx = 2a sin cos d, we get
V=
4 a
= 8a 3
/2
=a
2
z
x
a sin 2 a a sin 2 . 2a sin cos d
0
/2
O
sin 3 cos 2 d
0
2
X
(a, 0)
2
2
x + y = ax
3
3
3
3
2
2 16a
= 8a
4a
15
7
5 3 3
2
.
2
2 2 2
EXERCISE 2.10
3
ax x 2
Y
Ans.
x y z
abc
= 1
Ans.
a b c
6
2. Find the volume bounded by the cylinders y2 = x and x2 = y between the planes z = 0 and
1. Find the volume bounded by the coordinate planes and the plane.
x + y + z = 2.
Ans.
11
30
3. Find the volume bounded by the co-ordinate planes and the plane.
1
6l m n
4
4. Find the volume of the sphere x2 + y2 + z2 = a2 by triple integration. (AMIETE, June 2009) Ans. a 3
3
lx+ my+n z= 1
(A.M.I.E.T.E. Winter 2001)
Ans.
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128
Multiple Integral
6.
7.
8.
9.
10.
11.
12.
x2
y2
z2
4 a b c
= 1
Ans.
a
b
c2
3
Find the volume bounded by the cylinder x2 + y2 = a2 and the planes y + z = 2a and z = 0.
(M.U. II Semester 2000, 02, 06)
Ans. 2a3
2
2
2
Find the volume bounded by the cylinder x + y = a and the planes z = 0 and y + z = b.
Ans. a2b
Find the volume of the region bounded by z = x2 + y2, z = 0, x = – a, x = a and y = – a, y = a.
8 4
Ans. a
3
Find the volume enclosed by the cylinder x2 + y2 = 9 and the planes x + z = 5 and z = 0.
Ans. 45 – 36
Compute the volume of the solid bounded by x2 + y2 = z, z = 2x.(A.M.I.E., Summer 2000)
Ans. 2
Find the volume cut from the paraboloid 4 z = x2 + y2 by plane z = 4.
(U.P. I Semester, Dec. 2005)
Ans. 32
By using triple integration find the volume cut off from the sphere x2 + y2 + z2 = 16 by the plane
5. Find the volume of the ellipsoid
2
2
z = 0 and the cylinder x2 + y2 = 4 x.
Ans.
13. The sphere x2 + y2 + z2 = a2 is pierced by the cylinder x2 + y2 = a2 (x2 – y2).
64
(3 4)
9
8 5 4 2 3
Prove that the volume of the sphere that lies inside the cylinder is 3 4 3 3 a .
14. Find the volume of the solid bounded by the surfaces z = 0, 3 z = x2 + y2 and x2 + y2 = 9.
(A.M.I.E.T.E., Summer 2005)
Ans.
27
2
x
y
15. Obtain the volume bounded by the surface z = c 1 a 1 b and a quadrant of the elliptic cylinder
x2
y2
= 1, z > 0 and where a, b > 0.
(A.M.I.E.T.E., Dec. 2005)
a 2 b2
2
2
16. Find the volume of the paraboloid x + y = 4z cut off by the plane z = 4.
Ans. 32
17. Find the volume bounded by the cone z2 = x2 + y2 and the paraboloid z = x2 + y2.
Ans.
6
128a 3
15
19. Find the volume of the solid bounded by the plane z = 0, the paraboloid z = x2 + y2 + 2 and the cylinder
x2 + y2 = 4.
Ans. 16
18. Find the volume enclosed by the cylinders x2 + y2 = 2ax and z2 = 2 a x.
20. The triple integral
dx dy dz
(a) Volume of region
(c) Area of region T
Ans.
gives
(b) Surface area of region T
(d) Density of region T.
(A.M.I.E.T.E., 2002) Ans. (a)
2.16 SURFACE AREA
Let z = f(x,y) be the surface S. Let its projection on the
x-y plane be the region A. Consider an element 8x. y in the
region A. Erect a cylinder on the element x. y having its
generator parallel to OZ and meeting the surface S in an
element of area s.
x y = s cos ,
Where is the angle between the xy-plane and the
tangent plane to S at P, i.e., it is the angle between the Zaxis and the normal to S at P.
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Multiple Integral
129
The direction cosines of the normal to the surface F (x, y, z) = 0 are proportional to
F F F
,
,
x y z
The direction of the normal to S [F = f (x, y) – z] are proportional to
those of the Z-axis are 0, 0 , 1.
z
x
2
2
,
1
,
2
2
2
2
z
z
z
z
1 1
x
x
y
y
1
Hence
cos =
(cos = l1l2 + m1 m2 + n1 n2)
z 2 z 2
1
x
y
z 2 z 2
z 2 z 2
x y
S = cos = 1 x y ; S = A 1 dx dy
x y
x y
2
2
Example 55. Find the surface area of the cylinder x + z = 4 inside the cylinder x2 + y2 = 4.
Solution. x2 + y2 = 4
z
z
x z
2x2z
0 or
,
0
x
x
z y
Direction cosines =
,
z
y
z
z
,
, 1 and
x
y
z
z
1
x
y
2
2
x2 z 2
x2
z
4
z
1
1
=
=
=
2
2
y
z
z
4
x2
x
2
2
2
4 x 2 z
z
Hence, the required surface area = 8 0 0
1 dx dy
x
y
2
2
2
1
2
2
4 x
1
4x
2
[ y ]0
dx = 16
[ 4 x 2 ] dx
= 8
dx dy = 16 0
2
0
0 0
2
2
4x
4x
4 x
2
2
= 16 0 dx = 16 ( x)0 = 32
Ans.
2
2
2
Example 56. Find the surface area of the sphere x + y + z = 9 lying inside the cylinder
x2 + y2 = 3y.
Solution.
x2 + y2 + z2 = 9
z
z
x
2x 2 z
= 0,
x
x
z
z
z
y
2x 2 z
= 0,
y
y
z
2
2
2
z 2 z 2
x r cos
x
y
x y2 z2
9
9
1 = 2 2 1 =
=
=
z
z
z2
9 x 2 y 2 9 r 2 y r sin
x
y
x2 + y2 = 3y or r2 = 3 r sin or r = 3 sin .
Hence, the required surface area
/2
/ 2 3sin
z 2 z 2
3 sin r dr
3
r d dr = 12 d 0
= 1 dx dy = 4
x
9 r 2
y
9 r 2
0
0
0
/2
= 12
0
d [ 9 r 2 ]30 sin = 12
/2
[
9 9 sin 2 3] d
0
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130
Multiple Integral
/2
/ 2
( cos 1) d = 36 ( sin )0 = 36 1 = 18 ( – 2)
2
0
Example 57. Find the surface area of the section of the cylinder x2 + y2 = a2 made
plane x + y + z = a.
Solution.
x2 + y2 = a2
x+ y+ z=a
The projection of the surface area on xy-plane is a circle
x2 + y2 = a2
z
z
1
1
= 0 or
x
x
z
z
1
= 0 or
1
y
y
= 36
a 2 x2
= 4
( 1)2 ( 1)2 1 =
2
2
a
z
z
1 dx dy = 4
x
y
0
0
by the
... (1)
... (2)
2
2
z
z
1 =
x
y
Hence the required surface area
a
Ans.
0
a
= 4 3 [ y ]0
a2 x 2
a
dx = 4 3
0
3
a 2 x2
3 dx dy
0
a 2 x 2 dx
0
a
a2
x
a2
a
x
2
a2 x2
sin 1 = 4 3 0
= 4 3
= 3 a Ans.
= 4 3
4
2
2
2
2
a
0
Example 58. Find the area of that part of the surface of the paraboloid of the paraboloid
y2 + z2 = 2 ax, which lies between the cylinder, y2 = ax and the plane x = a.
Solution.
y2 + z2 = 2 ax
... (1)
y2 = ax
... (2)
x= a
... (3)
Differentiating (1), we get
z
z a
2z
= 2 a,
x
x z
z
z
y
2y 2 z
= 0,
y
y
z
2
y 2 z 2 2 ax
2
2
z 2 ax y
2
2
a2 y2
a2 y2
z
z
1
1 = 2 2 1 =
z2
z
z
x
y
a2 y2
a2 2 a x
a2 y2 2 a x y2
1
=
=
=
2 a x y2
2 a x y2
2 a x y2
a
S =
z
z
1 dx dy =
x
y
0 ax
a
=
a
2
2
ax
ax
0 ax
ax
0 ax
a 2 2 ax
2 ax y 2
a
a2x
2 ax y
a
2
dx dy =
a
0
ax
a 2 x dx
ax
y 2 ax
y ax
dx dy
1
2 ax y 2
dy
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Multiple Integral
131
a
=
a
0
a
=
a
0
=
=
1.
2.
3.
5.
2
2ax
y
ax
ax
1
1
a 2 x dx sin 1
sin 1
=
2
2
a
a 2 x dx =
0
a
a
0
a 2 x dx
4 4
a 2
[(a 2 x )3 / 2 ]0a
2 2 3
a
a2
[(3a)3/ 2 a3/ 2 ] =
[3 3 1]
6
6
EXERCISE 2.11
Ans.
Find the surface area of sphere x2 + y2 + z2 = 16.
Ans. 64
Find the surface area of the portion of the cylinder x 2 + y 2 = 4 y lying inside the sphere
x2 + y2 + z2 = 16.
Ans. 64.
Show that the area of surfaces cz = xy intercepted by the cylinder x2 + y2 = b2
is
4.
a
a 2 x dx sin 1
c2 x2 y2
dx dy , where A is the area of the circle x2 + y2 = b2, z = 0
c
A
1
Ans. 2 π (c 2 b 2 ) 2 c 2
3 c
2
2
2
2
2
2
Find the area of the portion of the sphere x + y + z = a lying inside the cylinder x + y = ax.
Ans. 2 ( – 2) a2
Find the area of the surface of the cone z2 = 3 (x2 + y2) cut out by the paraboloid z = x2 + y2 using surface
integral.
Ans. 6
2.17 CALCULATION OF MASS
We have,
Volume = V dx dy dz
[Density = Mass per unit volume]
Mass =
Density = = f (x, y, z)
Mass = Volume × Density
Mass f ( x, y, z ) dx dy dz
V dx dy dz
V
Example 59. Find the mass of a plate which is formed by the co-ordinate planes and the plane
x y z
1, the density is given by = k x y z.
a b c
(U.P., I Semester, Dec., 2003)
Solution. The plate is bounded by the planes x = 0, y = 0, z = 0 and
Mass =
dx dy dz =
z
y z
b 1 a 1
c
b c dx
0 0
0
c
x y z
1.
a b c
dy dz (k xyz )
y z
a 1
x 2 b c
y dy
dx = k z dz
y dy
= k 0 z dz
0
2 0
2
2
z
z
2
2
b 1
c
b 1
ka c
z y
a
y z
c y
z
dz
1
dy
1
= k 0 z dz 0 c y dy
=
0
2 0
c b
2
b c
2
z
b 1
k a2 c
z
y3 2 y 2
z
c y 1
z
dz
=
1 dy
2
0
0
2
c
b
c
b
c
b 1
0
z
c
y z
a 1
b cx
0
c
z
b 1
c
0
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132
Multiple Integral
k a2
=
2
k a2
=
2
c
0
c
0
2
y2
z dz
2
z
y4
2 y3
1
c
3b
4 b2
b2
z dz
2
z
b4
1
c
4 b2
4
z
b 1
c
z
1
c
0
4
4
z
1
c
z
2 b3
1
c
3 b
4
4
b2 b 2 2b 2
k a 2 b2 c
z
z
1
dz
z
1
dz
=
[Put z = c sin2 ]
0 2 4 3 c
2 12 0
c
k a 2 b2 c 2 2
2
2
4
=
0 c sin (1 sin ) (2 c sin cos d )
12
k 2 a2 b 2 c 2 / 2 2
k 2 a2 b 2 c 2 / 2 3
8
9
sin
(cos
)
sin
cos
d
=
=
0
0 sin cos d
12
12
3 1 9 1
k a 2 b2 c2 2 5
k a 2 b 2 c2
k 2 a2 b 2 c 2 2
k a 2 b2 c 2 (1) ( 5)
2
=
=
=
=
Ans.
720
12
12
12
392
2 7
265 5
2
2
2.18 CENTRE OF GRAVITY
=
k a2
2
c
x
x dx dy dz , y y dx dy dz , z z dx dy dz
dx dy dz
dx dy dz
dx dy dz
Example 60. Find the co-ordinates of the centre of gravity of the positive octant of the sphere
x2 + y2 + z2 = a2, density being given = k xyz.
V x dx dy dz
V dx dy dz
Solution. x =
=
z dx dy dz
dx dy dz
2
=
V x yz dx dy dz
V xyz dx dy dz
Converting into polar co-ordinates, x = r sin cos , y = r sin sin , z = r cos ,
dx dy dz = r2 sin dr d d
/ 2 / 2 a
x =
=
=
=
Similarly,
2
2
0 0 0 (r sin cos ) ( r sin sin ) (r cos ) (r sin dr d d )
/2 /2 a
2
0 0 0 (r sin cos ) (r sin sin ) (r cos ) (r sin dr d d )
/ 2 / 2 a 6
4
2
0 0 0 r sin cos sin cos dr d d
/ 2 / 2 a 5
3
0 0 0 r sin cos sin cos dr d d
/ 2
/2
a 6
2
4
0 sin cos d 0 sin cos d 0 r dr
/ 2
/ 2
a 5
3
0 sin cos d 0 sin cos d 0 r dr
/ 2
sin 5
5 0
/2
sin 4
4 0
cos3
3 0
cos2
2 0
16 a
;
y = z =
35
/ 2
r7
7 0
a
/ 2
r6
6 0
a
7
1 1 a
3 5 7
16 a
=
=
6
35
11 a
24 6
16 a 16 a 16 a
,
,
Hence, C.G. is
35 35 35
Ans.
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Multiple Integral
133
2.19 MOMENT OF INERTIA OF A SOLID
Let the mass of an element of a solid of volume V be x y z.
Perpendicular distance of this element from the x-axis =
M.I. of this element about the x-axis = x y z
y2 z2
y2 z2
M.I. of the solid about x-axis =
V ( y
2
z 2 ) dx dy dz
M.I. of the solid about y-axis =
V ( x
2
z 2 ) dx dy dz
M.I. of the solid about z-axis =
V ( x
2
y 2 ) dx dy dz
The Perpendicular Axes Theorem
If Iox and Ioy be the moments of inertia of a lamina about x-axis and y-axis respectively and Ioz
be the moment of inertia of the lamina about an axis perpendicular to the lamina and passing
through the point of intersection of the axes OX and OY.
IOZ = IOX + IOY
The Parallel Axes Theorem
M.I. of a lamina about an axis in the plane of the lamina equals the sum of the moment of
inertia about a parallel centroidal axis in the plane of lamina together with the product of the mass
of the lamin a and square of the distance between the two axes.
IAB = IXX + My–2
Example 61. Find M.I. of a sphere about diameter.
Solution. Let a circular disc of x thickness be
perpendicular to the given diameter XX at a distance x
from it.
The radius of the disc =
a 2 x2
Mass of the disc = (a2 – x2)
Moment of inertia of the disc about a diameter perpendicular on it
1
1
1
2
2
2
2
2
2
2 2
= MR = [ (a x )] (a x ) = (a x )
2
2
2
a 1
1
a 4
2 2
4
2
2 2
M.I. of the sphere = a (a x ) dx = 2 0 [a 2 a x x ] dx
2
2
a
5 2 a5 a5
4
2 a 2 x3 x5
= a
= a x
3
5
3
5 0
2 4 3 2
2
8
a a = M a2
a5 =
=
Ans.
5 3
5
15
Example 62. The mass of a solid right circular cylinder of radius a and height h is M. Find
the moment of inertia of the cylinder about (i) its axis (ii) a line through its centre of gravity
perpendicular to its axis (iii) any diameter through its base.
Solution. To find M.I. about OX. Consider a disc at a distance x from O at the base.
a4 dx
( a 2 dx ) a 2
=
2
2
M.I. of the cylinder about OX
M.I. of the about OX, =
(i)
h
0
4
2
2
a4
a 4 dx
x 0h = a h = ( a 2 h) a = M a
=
2
2
2
2
2
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134
Multiple Integral
(ii) M.I. of the disc about a line through C.G. and
perpendicular to OX.
IOX + IOY = IOZ
IOX + IOX = IOZ
1
IOZ
2
M.I. of the disc about a line through
1 M a2
M a2
C.G. = 2 2 =
4
IOX =
a 2 dx 2
M.I. of the disc about the diameter =
a
4
2
a 2 dx
h
2
(
a
dx
)
x
M.I. of the disc about line GD =
4
2
2
2
ha
h
h
dx ( a 2 dx) x
Hence, M.I. of cylinder about GD =
0
0
4
2
3
3
3 h
2
2
2
2
a h
a h
a2 h
a
a
h
h
x
x
0
=
=
4
3 2
4
2
4
3 2
0
M a2 M h2
a 2 h a 2 h3
=
4
12
4
12
(iii) M.I. of cylinder about line OB (through) base
=
2
M a 2 M h2
M a 2 M h2 M h2
h
IOB = IG M =
=
Ans.
4
3
4
12
4
2
Example 63. Find the moment of inertia and radius of gyration about z-axis of the region in
x y z
the first octant bounded by 1 .
a b c
Solution. Let r be the density. M.I. of tetrahedron about z-axis
= ( dx dy dz ) ( x 2 y 2 )
x
b 1 2
a (x
0
a
= dx
0
y 2 ) dy
x y
c 1
a b dz
0
x
x y
c 1
a b
= a dx b 1 a ( x 2 y 2 ) dy ( z )
0
0
0
x
b 1 2
a (x
0
x y
y 2 ) dy c c 1
a b
x
a
b 1 2
x x2 y
x y3
y 2 1 dy
= c 0 dx 0 a x 1
a
b
a b
a
= 0 dx
x
b 1
a
2
x
x 2 y 2 y3
x y4
c
dx
x
1
y
1
=
0 a
2b
3
a 4 b
0
2
2
2
a
b3
x
x x 2
x
b 1
= c 0 dx x 1 b 1
3
a
a 2b
a
2
2
4
a 2
x
x2
x
b2
x
b2
1
1
= bc 0 x 1
a
2
a
3
a
4
a
3
4
x
x b4
x
1
1
1
a
a 4b
a
4
x
1
dx
a
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Multiple Integral
135
2
ax
= bc 0
2
2
x
b2
1
a
12
4
x
1
dx
a
a1 2
2 x3 x 4 b 2
bc
x
2
=
0 2
a
a 12
a 1
= bc 0
x3 x 4
x5
2
2 3 2 a 5 a
4 x 6 x 2 4 x3 x4
1
2 3 4 dx
a
a
a
a
b2
12
a
2 x2 6 x2 4 x3 x4
2 3 4 dx
x
a
a
a
a 0
3
a 1 a
a3 a3 b 2
a
= bc 0
a 2a 2a a
2
5 12
5
2 3
a3 ab2
abc 2
(a b2 )
= bc 60 60 =
60
abc 2
(a b2 )
1 2
M .I .
60
(a b2 )
Radius of gyration =
=
=
Ans.
10
abc
Mass
6
2.20 CENTRE OF PRESSURE
The centre of pressure of a plane area immersed in a fluid is the point at which the resultant
force acts on the area.
Consider a plane area A immersed vertically in a homogeneous liquid. Let x-axis be the line of
intersection of the plane with the free surface. Any line in this plane and perpendicular to x-axis is
the y-axis.
Let P be the pressure at the point (x, y). Then the pressure on elementary area x y is P x y.
Let ( x , y) be the centre of pressure. Taking moment about y-axis.
x P dx dy =
A
A Px dx dy
x =
A Px dx dy
A P dx dy
y =
A Py dx dy
A P dx dy
Similarly,
Example 64. A uniform semi-circular lamina is immersed in a fluid with its plane vertical and
its bounding diameter on the free surface. If the density at any point of the fluid varies as the
depth of the point below the free surface, find the position of the centre of pressure of the lamina.
Solution. Let the semi-circular lamina be
x2 + y2 = a2
By symmetry its centre of pressure lies on OY. Let ky be the density of the fluid.
Py dx dy = A (y) y dx dy
y = A
( = ky)
P dx dy (y) dx dy
A
A
A
A (ky . y ) dx dy
=
a
y 3 dx dy
(ky . y ) y dx dy
=
A
2
A y dx dy
=
a dx 0
a
a
dx
0
a 2 x2
a 2 x2
y 3 dy
y 2 dy
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136
Multiple Integral
y4
a dx 4
0
a
=
y3
dx
a 3
0
a
a 2 x2
a
a 2 x2
/ 2
3
=
4
/ 2 (a cos d ) (a
/ 2
/ 2
2.
3.
2
a dx (a
a
a dx (a
2
2
x 2 )2
x 2 )3 / 2
a 2 sin 2 )2
(Put x = a sin )
(a cos d ) (a 2 a 2 sin 2 )3/ 2
42
/2
5
32 a
3 a 2 0 cos d
3a 53
=
=
=
/2
/
2
3
1
4
4
15
4
/ 2 cos d 4 2 0 cos d
42 2
EXERCISE 2.12
/ 2
3a
=
4
1.
3
=
4
/ 2 cos
5
d
Find the mass of the solid bounded by the ellipsoid
x2
a2
y2
b2
z2
c2
Ans.
1 and the co-ordinate planes, where
the density at any point P (x, y, z) is k xyz.
Ans. P
If the density at a point varies as the square of the distance of the point from XOY plane, find the mass
of the volume common to the sphere x2 + y2 + z2 = a2 and cylinder x2 + y2 = ax.
4 k 5 8
a
Ans.
15
2 15
2
2
Find the mass of the plate in the form of one loop of leminscate r = a sin 2 , where = k r2.
k π a4
16
Find the mass of the plate which is inside the circle r = 2a cos and outside the circle r = a, if the
density varies as the distance from the pole.
Find the mass of a lamina in the form of the cardioid r = a (1 + cos ) whose density at any point varies
21 π k a 4
as the square of its distance from the initial line.
Ans.
32
x y z
a b c
Find the centroid of the region in the first octant bounded by + + = 1 . Ans. , ,
a b c
4 4 4
4
Find the centroid of the region bounded by z = 4 – x2 – y2 and xy-plane.
Ans. 0, 0,
3
Find the position of C.G.. of the volume intercepted between the parallelepiped x2+y2 = a(a – z) and the
a
plane z = 0.
Ans. 0, 0,
3
A solid is cut off the cylinder x2 + y2 = a2 by the plane z = 0 and that part of the olane z = mx for which
z is positive. The density of the solid cut off at any point varies as the height of the point above plane
Ans.
4.
5.
6.
7.
8.
9.
64 ma
45 π
If an area is bounded by two concentric semi-circles with their common bounding diameter in a free
Ans. z =
z = 0. Find C.G. of the solid.
10.
surface, prove that the depth of the centre of pressure is
11.
An ellipse
x2
a2
y2
b2
3 π ( a b) ( a 2 b 2 )
16 a 2 ab b 2
1 is immersed vertically in a fluid with its major axis horizontal. If its centre be
at depth h, find the depth of its centre of pressure.
Ans. h
b2
4h
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Multiple Integral
12.
13.
137
A horizontal boiler has a flat bottom and its ends are plane and semi-circular. If it is just full of water,
show that the depth of centre of pressure of either end is 0.7 × total depth approximately.
A quadrant of a circle of radius a is just immersed vertically in a homogeneous liquid with one edge in
3a 3π a
,
Ans.
8 16
Find the product of inertia of an equilateral triangle about two perpendicular axes in its plane at a vertex,
one of the axes being along a side.
Find the M.I. of a right circular cylinder of radius a and height h about axis if density varies as distance
the surface. Determine the co-ordinates of the centre of pressure.
14.
15.
2
k π a5 h
5
Compute the moment of inertia of a right circular cone whose altitude is h and base radius r, about (i)
from the axis.
16.
Ans.
π h r4
π h r2
(ii)
(2 h 2 3 r 2 )
10
60
Find the moment of inertia for the area of the cardioid r = a (1 – cos ) relative to the pole.
35 π a 4
Ans.
16
π
Find the M.I. about the line =
of the area enclosed by r = a (1 + cos ).
2
Find the moment of inertia of the uniform solid in the form of octant of the ellipsoid
the axis of symmetry (ii) the diameter of the base.
17.
18.
19.
x2
z2
M 2
(b c 2 )
1 about OX
Ans.
5
a
b
c2
Prove that the moment of inertia of the area included between the curves y2 = 4 ax and x2 = 4 ay about
2
20.
y2
Ans. (i)
2
144
M a 2, , where M is the mass of area included between the curves.
35
A solid body of density p is the shape of solid formed by revolution of the cardioid r = a (1 + cos )
about the initial line. Show that its moment of inertia about a straight line through the pole perpendicular
the x-axis is
21.
352
5
l a .
105
to the initial line is
22.
(U. P. II Semester, Summer 2001)
Find the product of inertia of a disc in the form of a quadrant of a circle of radius ‘a’ about bounding
(U. P. II Semester, Summer 2002) Ans.
radii.
23.
Show that the principal axes at the origin of the triangle enclosed by x = 0, y = 0,
1
ab
π
1
to the x-axis, where a = tan 2
2
2
2
a b
Choose the correct answer:
at angles and α
24. The triple integral
a4
4
x y
1 are inclined
a b
(U.P. II Semester Summer 2001)
dx dy dz gives
T
(i) Volume of region T
(ii) Area of region T
(ii) Surface area of region T
(iv) Density of region T.
(A.M.I.E.T.E. 2002)
Ans. (i)
25. The volume of the solid under the surface az = x2 + y2 and whose base R is the circle x2 + y2 = a2
is given as
a 3
(i)
(ii)
Ans. (ii)
2a
2
4 3
(iii) a
(iv) None of the above.
[U.P., I. Sem. Dec. 2008]
3
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138
Differential Equations
3
Differential Equations
3.1 DEFINITION
An equation which involves differential co-efficient is called a differential equation.
For example,
dy 2
3. 1
dx
d2y
dy 1 x 2
1.
dx 1 y 2
dy
2 8y 0
2.
2
dx
dx
3
2
k
d2y
dx 2
2 z
z
u
u
y
nu, 5.
x y y
x
y
There are two types of differential equations :
4. x
(1) Ordinary Differential Equation
A differential equation involving derivatives with respect to a single independent variable is
called an ordinary differential equation.
(2) Partial Differential Equation
A differential equation involving partial derivatives with respect to more than one independent
variable is called a partial differential equation.
3.2 ORDER AND DEGREE OF A DIFFERENTIAL EQUATION
The order of a differential equation is the order of the highest differential co-efficient present in the
equation. Consider
1. L
d 2q
dt 2
R
dq q
E sin wt.
dt c
3
dy 2 d 2 y
3. 1 2
dx dx
2. cos x
d2y
2
dy
sin x 8 y tan x
2
dx
dx
2
The order of the above equations is 2.
The degree of a differential equation is the degree of the highest derivative after removing
the radical sign and fraction.
The degree of the equation (1) and (2) is 1. The degree of the equation (3) is 2.
3.3 FORMATION OF DIFFERENTIAL EQUATIONS
The differential equations can be formed by differentiating the ordinary equation and
eliminating the arbitrary constants.
Example 1. Form the differential equation by eliminating arbitrary constants, in the following
cases and also write down the order of the differential equations obtained.
(a) y = A x + A2
(b) y = A cos x + B sin x
(c) y2 = Ax2 + Bx + C.
(R.G.P.V. Bhopal, June 2008)
138
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Differential Equations
139
2
Solution. (a) y = Ax + A
dy
A
On differentiation
dx
... (1)
2
dy dy
dx dx
On eliminating one constant A we get the differential equation of order 1.
(b) y = A cos x + B sin x
dy
A sin x B cos x
On differentiation
dx
Again differentiating
Putting the value of A in (1), we get y x
d2y
dx 2
d2 y
A cos x – B sin x
d2y
dx 2
Ans.
( A cos x B sin x )
d2y
y0
Ans.
dx 2
dx 2
This is differential equation of order 2 obtained by eliminating two constants A and B.
(c) y2 = Ax2 + Bx + C
On differentiation 2 y
y
dy
2 Ax B
dx
Again differentiating 2 y
d2y
2
dy
2 2A
2
dx
dx
3
2
2
d3y
dy d 2 y
On differentiating again y d y dy d y 2 dy d y 0 y 3 3
0 Ans.
dx dx 2
dx dx 2
dx
dx 3 dx dx 2
This is the differential equation of order 3, obtained by eliminating three constants A, B, C.
EXERCISE 3.1
1. Write the order and the degree of the following differential equations.
(i)
d2y
dx 2
3
2
a x 0;
2
dy 2 d 2 y
(ii) 1
;
dx 2
dx
3
4
d2y
dy
4
(iii) x 2 y y 0.
dx
dx
2
Ans. (i) 2,1 (ii) 2,2 (iii) 2,3
2. Give an example of each of the following type of differential equations.
(i) A linear-differential equation of second order and first degree
Ans. Q, 1 (i)
(ii) A non-linear differential equation of second order and second degree
(iii) Second order and third degree.
Ans. Q, 1 (ii)
Ans. Q 1 (iii)
2
3. Obtain the differential equation of which y = 4a(x + a) is a solution.
2
dy
dy
Ans. y 2 2 xy y 2 0
dx
dx
4. Obtain the differential equation associated with the primitive Ax² + By² = 1.
2
d2y
dy
dy
0
Ans. xy 2 x y
dx
dx
dx
5. Find the differential equation corresponding to
d2 y
dy
4 3y 0
y = a e3x + bex.
Ans.
2
dx
dx
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140
Differential Equations
6. By the elimination of constants A and B, find the differential equation of which
d2 y
dy
2y 0
dx
dx
7. Find the differential equation whose solution is y = a cos (x = 3). (A.M.I.E., Summer 2000)
dy
tan ( x 3)
Ans.
dx
1
8. Show that set of function x, forms a basis of the differential equation x2y + xy – y = 0.
x
3x 1
Obtain a particular solution when y (1) = 1, y (1) = 2.
Ans. y
2 2x
y = ex (A cos x + B sin x) is a solution.
Ans.
2
2
3.4 SOLUTION OF A DIFFERENTIAL EQUATION
In the example 1(b), y = A cos x + B sin x, on eliminating A and B we get the differential
equation
d2y
y0
dx 2
d2y
y0.
y = A cos x + B sin x is called the solution of the differential equation
dx 2
d2y
y 0 is two and the solution
The order of the differential equation
dx 2
y = A cos x + B sin x contains two arbitrary constants. The number of arbitrary constants in
the solution is equal to the order of the differential equation.
An equation containing dependent variable (y) and independent variable (x) and free from
derivative, which satisfies the differential equation, is called the solution (primative) of the
differential equation.
3.5 DIFFERENTIAL EQUATIONS OF THE FIRST ORDER AND FIRST DEGREE
We will discuss the standard methods of solving the differential equations of the following
types:
(i) Equations solvable by separation of the variables.
(iii) Linear equations of the first order.
(ii) Homogeneous equations.
(iv) Exact differential equations.
3.6 VARIABLES SEPARABLE
If a differential equation can be written in the form
f ( y ) dy ( x ) dx
We say that variables are separable, y on left hand side and x on right hand side.
We get the solution by integrating both sides.
Working Rule:
Step 1. Separate the variables as
Step 2. Integrate both sides as
f ( y ) dy ( x ) dx
f ( y) dy ( x) dx
Step 3. Add an arbitrary constant C on R.H.S.
Example 2. Solve :
Solution. We have,
dy
x(2 log x 1)
dx sin y y cos y
dy x (2log x 1)
dx sin y y cos y
(UP, II 2008, U.P.B. Pharm (C.O.) 2005)
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Differential Equations
141
Separating the variables, we get
(sin y + y cos y) dy = {x (2 log x + 1)} dx
x(2log x 1) dx C
cos y y sin y (1) sin y dy 2 log x x dx x dx C
Integrating both the sides, we get (sin y y cos y )dy
x2
1 x2 x2
cos y y sin y cos y 2 log x
dx
C
2
x 2 2
x2
x2
y sin y 2log x x dx C
2
2
2
2
2
x
x
x
y sin y 2 log x C
2
2
2
y sin y x 2 log x C
Ans.
Example 3. Solve the differential equation.
dy
x4
x3 y sec( x y ).
(A.M.I.E.T.E., Winter 2003)
dx
3 dy
4 dy
x 3 y sec( x y )
Solution. x
x x y sec xy
dx
dx
dv
dy
3 dv
x y
sec v
Put v = xy,
x
dx
dx
dx
dv
dx
dx
3
cos v dv 3 c
sec v
x
x
1
1
c
c
Ans.
sin v =
sin xy =
2x2
2x2
Example 4. Solve :
cos (x + y)dy = dx
Solution.
cos (x + y) dy = dx
On putting
So that
Separating the variables, we get
x+y=z
dy dz
1
dx dx
dz
1 sec z
dx
dy
sec ( x y )
dx
dy dz
1
dx dx
dz
1 sec z
dx
dz
dx
1 sec z
On integrating,
1
cos z
1
dz dx
dz x C
cos z 1
cos z 1
1
1
dz x C
2cos 2 z 1 1
2
z
1
z
2
z tan x C
1 sec dz x C
2
2
2
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142
Differential Equations
x y
xC
2
x y
y tan
C
2
x y tan
Ans.
Example 5. Solve the equation.
(2x2 + 3y2 – 7) x dx – (3x2 + 2y2 – 8) y dy = 0
(U.P. II Semester, Summer 2005)
Solution. We have
(2x2 + 3y2 – 7) x dx – (3x2 + 2y2 – 8) y dy = 0
x dx 3x 2 2 y 2 8
Re-arranging (1), we get y dy 2
2x 3y 2 7
Applying componendo and dividendo rule, we get
x dx y dy
x dx y dy 5 x 2 5 y 2 –15
x dx y dy
5 2
2
2
2
2
2
x dx – y dy
x – y –1
x y 3
x y 1
Multiplying by 2 both the sides, we get
2 x dx 2 y dy
2 x dx 2 y dy
2
5 2
2
2
x y 3
x y 1
Integrating both sides, we get
log (x² + y² – 3) = 5 log (x² – y² – 1) + log C
x² + y² – 3 = C (x² – y² – 1)5
Ans.
where C is arbitrary constant of integration.
EXERCISE 3.2
Solve the following differential equations :
dx
tan y dy
1.
x
1 y2
dy
2.
dx
1 x2
Ans. x cos y = C
3. y (1 x 2 )1/ 2 dy x 1 y 2 dx 0
4. sec² x tan y dx + sec² y tan x dy = 0
5. (1 + x²) dy – x y dx = 0
6. (ey + 1) cos x dx + ey sin x dy = 0
7. 3 ex tan y dx + (1 – ex) sec² y dy = 0
8. (ey + 2) sin x dx – ey cos x dy = 0
9.
Ans. sin–1 y = sin–1 x + C
1 y2 1 x2 C
Ans. tan x tan y = C
Ans. y² = C (1 + x²)
Ans. (ey + 1) sin x = C
Ans. (1 – ex)³ = C tan y
Ans. (ey + 2) cos x = C
Ans.
dy
e x y x 2 e y
dx
y
x
Ans. e e
x3
C
3
dy
10. d x 1 tan ( y x ) [Put y – x = z]
Ans. sin (y – x) = ex+c
2 d x
1 4 x y
1
2x C
11. (4 x y )
Ans. tan
dy
2
dy
2
1 4 x y 1
2x C
12. d x (4 x y 1) [Hint. Put 4x + y +1 = z] Ans. tan
2
3.7 HOMOGENEOUS DIFFERENTIAL EQUATIONS
A differential equation of the form
dy f ( x, y )
dx ( x, y )
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Differential Equations
143
is called a homogeneous equation if each term of f (x, y) and (x, y) is of the same degree i.e.,
dy 3 x y y 2
dx 3 x 2 x y
dv
dy
v x
In such case we put y = vx. and
dx
dx
The reduced equation involves v and x only. This new differential equation can be solved by
variables separable method.
Working Rule
dv
dy
vx
Step 1. Put y = vx so that
Step 2. Separate the variables.
dx
dx
Step 4. Put v
Step 3. Integrate both the sides.
y
and simplify..
x
Example 6. Solve the following differential equation
(2xy + x2) y = 3y2 + 2xy
(A.M.I.E.T.E. Dec. 2006)
2
dy
dy 3 y 2 xy
3 y 2 2 xy
Solution. We have, (2xy + x2)
dx
dx 2 xy x 2
dy
dv
v x
Put y = vx so that
dx
dx
dv 3v 2 x 2 2vx 2 3v 2 2v
On substituting, the given equation becomes v x
dx
2v 1
2vx 2 x 2
dv 3v 2 2v 2v 2 v
dx
2v 1
dx
2v 1
v 2 v dv x
x
v2 + v = cx
x
log (v2 + v) log x + log c
y2 + xy = cx3
Example 7. Solve the equation :
dy y
x sin
dx x
dy y
x sin
dx x
Solution.
Put
dv v 2 v
dx
2v 1
2
dv
dx 2v 1
x
v v
y2
x
2
y
cx
x
y
x
y
x
... (1)
y = vx in (1) so that
dv
v x sin v
dx
dv
x
x sin v
dx
Separating the variable, we get
dv
dx
sin v
v
log tan x C
2
dy
dv
v x
dx
dx
v x
dv
sin v
dx
cosec v dv dx C
log tan
y
xC
2x
Ans.
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144
Differential Equations
EXERCISE 3.3
Solve the following differential equations:
2. (x² – y²) dx+2xy dy = 0 (AMIETE, June 2009)
x
Ans. y log x C
Ans. x² + y² = ax
dy
y ( y x).
dx
4. x (x – y) dy + y² dx = 0
Ans. y log xy a
x
Ans. y = x log C y
1. (y² – xy) dx + x²dy = 0
3. x ( y x)
5.
(U.P. B. Pharm (C.O.) 2005)
dy x 2 y
0 Ans. y – x = C (x + y)³
dx 2 x y
dy 3 x y y 2
7. dx
3 x2
6.
dy
y y
tan
dx
x x
Ans. sin
Ans. 3x + y log x + Cy = 0 8.
dy x 2 2 y 2
dx
2x y
Ans. 4y² – x² =
Ans.
9. (x² + y²) dy = xy dx
x2
2 y2
y
Cx
x
C
x2
log y C
x3
log y C
3 y3
11. (y² + 2xy) dx + (2 x² + 3xy) dy = 0 (AMIETE, Summer 2004) Ans. xy² (x + y) = C
12. (2xy² – x³) dy + (y³ – 2yx²) dx = 0
Ans. y² (y² – x²) = Cx–2
10. x²y dx – (x³ + y³) dy = 0
Ans.
13. (x³ – 3 xy²) dx + (y³ – 3 x²y) dy = 0, y (0) = 1
14. 2 xy² dy – (x³ + 2y³) dx = 0
Ans. x4 – 6x² y² + y4 = 1
Ans. 2y³ = 3x³ log x + 3x³ + C
15. x sin
y
y
dy y sin x dx
x
x
Ans. cos
y
y
y
y dy
0
16. x cos y sin y y sin x cos x
x
x
x
x
dx
y x2 y 2
17. dy
dx
x
dy
y (log y log x 1) (AMIETE, Summer 2004)
18. x
dx
2
x
x
2
19. xy log dx y x log dy 0 given that y (1) = 0
y
y
Ans.
x
y
log x C
x
Ans. x y cos
y
a
x
Ans. y x 2 y 2 C x 2
Ans. log
x2
2 y2
log
x
y
Cx
x
x x2
3
2 log y 1 2
y 4y
4e
x
x
x
y
y
(1 e y ) dx e y 1 dy 0 (AMIETE, June 2009)
Ans. e e C
y
y
3.8 EQUATIONS REDUCIBLE TO HOMOGENEOUS FORM
20.
a b
A B
The equations of the form
dy
ax + by + c
=
dx Ax + By + C
Case I.
can be reduced to the homogeneous form by the substitution if
a b
A B
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Differential Equations
145
x = X + h, y = Y + k
(h,k being constants)
d y dY
dx dX
The given differential equation reduces to
dY
a ( X h) b (Y k ) c
a X bY a h b k c
d X A ( X h) B (Y k ) C A X BY A h B k C
Choose h, k so that
ah+bk+c=0
Ah+Kk+C=0
dY
a X bY
Then the given equation becomes homogeneous d X A X BY
a b
Case II. If then the value of h, k will not be finite.
A B
a b 1
(say)
A B m
A = a m, B = b m
The given equation becomes
dy
a x b y c
d x m (a x b y ) c
Now put ax + by = z and apply the method of variables separable.
dy x 2 y 3
dx 2 x y 3
Solution. Put
x = X + h,
y = Y + k.
The given equation reduces to
d Y ( X h) 2(Y k ) 3
d X 2( X h) (Y k ) 3
Example 8. Solve :
1 2
2 1
X 2 Y (h 2 k 3)
2 X Y (2 h k 3)
Now choose h and k so that h + 2k – 3 = 0, 2h + k – 3 = 0
Solving these equations we get h = k = 1
dY
X 2Y
d X 2 X Y
.... (1)
... (2)
dY
dv
Put Y = v X, so that d X v X d X
The equation (2) is transformed as
d v X 2 v X 1 2 v
v X
d X 2X v X
2v
X
d v 1 2v
1 v2
v
d X 2v
2v
1 1
3 1
dX
dv
dv
2 (1 v)
2 1 v
X
On integrating, we have
1
3
log(1 v ) log(1 v ) log X log C
2
2
dX
2v
dv
2
X
1 v
(Partial fractions)
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146
Differential Equations
Put
log
1 v
3
log C 2 X 2
(1 v )
Y
1
X C2 X 2
3
Y
1
X
X = x – 1 and Y = y – 1
1 v
(1 v)
3
C2X 2
X Y
( X Y )3
C 2 or
X + Y = C 2 (X – Y)³
x + y – 2 = a (x – y)³
Ans.
Example 9. Solve : (x + 2y) (dx – dy) = dx + dy
Solution. (x + 2y) (dx – dy) = dx + dy
(x + 2y – 1) dx – (x + 2y + 1) dy = 0
dy x 2 y 1
dx x 2 y 1
a b
Hence
i.e., 1 2
A B
1 2
dy dz
Now put x + 2y = z so that 1 2
dx dx
Equation (1) becomes
1 d z 1 z 1
dz
( z 1)
3z 1
2
1
2 d x 2 z 1
dx
z 1
z 1
z 1
dz dx
3z 1
On integrating,
...(1)
(Case of failure)
1 4 1
dz dx
3 3 3z 1
z 4
log (3 z 1) x C
3 9
3z + 4 log (3z – 1) = 9x + 9C
3 (x + 2y) + 4 log (3x + 6y – 1) = 9x + 9C
3x – 3y + a = 2 log (3x + 6y – 1)
Ans.
EXERCISE 3.4
Solve the following differential equations :
dy 2 x 9 y 20
1.
Ans. (2x – y)² = C (x + 2y – 5)
dx 6 x 2 y 10
dy y x 1
1 y 3
a
2. dx y x 5
Ans. log[( y 3)² ( x 2)²] 2 tan
x2
dy x y 2
3. dx x y 6
Ans. (y + 4)² + 2 (x + 2) (y + 4) – (x + 2)² = a²
dy y x 2
4. dx y x 4 (AMIETE, Dec. 2009)
Ans. – (y – 3)² + 2(x + 1) (y – 3) + (x + 1)² = a
dy 2x 5y 3
5. dx 2x 4y 6
Ans. (x – 4y + 3) (2x + y – 3) = a
6. (2x + y + 1) dx + (4x + 2y – 1) dy = 0
Ans. 2 (2x + y) + log (2x + y – 1) = 3x + C
7. (x – y – 2) dx – (2x – 2y – 3) dy = 0
Ans. log (x – y – 1) = x – 2y + C
(U.P. B. Pharm (C.O.) 2005)
8. (6x – 4y + 1) dy – (3x – 2y + 1) dx = 0 (A.M.I.E.T. E., Dec. 2006)
Ans. 4x – 8y – log (12x – xy + 1) = c
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Differential Equations
147
dy
3y 2x 7
9. dx 7 y 3x 3 (A.M.I.E.T.E., Summer 2004)
dy 2 y x 4
10.
(AMIETE, Dec. 2010)
dx y 3 x 3
Ans. (x + y – 1)5 (x – y – 1)2 = 1
2Y (5 21) X 1
X x2
2
2
,
Ans. X 5 XY Y c
2Y (5 21) X 21 Y y 3
3.9 LINEAR DIFFERENTIAL EQUATIONS
A differential equation of the form
dy
PyQ
dx
... (1)
is called a linear differential equation, where P and Q, are functions of x (but not of y) or constants.
In such case, multiply both sides of (1) by e Pdx
Pdx dy
Pdx
e Py Q e
dx
The left hand side of (2) is
d Pdx
y.e
dx
d P dx
P dx
y.e
(2) becomes
Q.e
dx
Integrating both sides, we get
y.e
P dx
Q. e
This is the required solution.
Note. e
P dx
P dx
... (2)
dx C
is called the integrating factor..
y × [I.F.] = Q [I.F.] dx + C
Solution is
Working Rule
Step 1. Convert the given equation to the standard form of linear differential equation
dy
Py Q
i.e.
dx
Step 2. Find the integrating factor i.e. I.F. = e Pdx
Step 3. Then the solution is y ( I .F .) Q ( I .F .)dx C
Example 10. Solve:
Solution.
dy
y e x ( x 1)2
dx
dy
y
e x ( x 1)
dx x 1
( x 1)
Integrating factor = e
The solution is
(A.M.I.E.T.E., Summer 2002)
dx
x 1 e log( x 1) elog( x 1)1 1
x 1
1
1
y.
e x .( x 1).
dx e x dx
x 1
x 1
y
ex C
x 1
Ans.
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148
Differential Equations
Example 11. Solve a differential equation
dy
( x 3 x) (3 x 2 1) y x 5 2 x3 x.
(Nagpur University, Summer 2008)
dx
dy
Solution. We have ( x3 x ) (3 x 2 1) y x 5 2 x 3 x
dx
2
dy 3x 1
x 5 2 x3 x
dy 3x 2 1
3
y =
y = x² – 1
3
dx x x
dx x 3 x
x x
3 x 2 1
I.F. = e
x3 x dx
e log( x
3
x)
e log( x
3
x ) –1
Its solution is
y
3
x x
y
x3 x
=
x2 1
x( x
2
1)
dx C
= log x + C
Example 12. Solve sin x
y
3
x x
=
x x
x2 1
1
=
y 3
x x
y I.F. Q( I .F .) dx C
1
3
x
3
x
dx C
1
x dx C
y = (x³ – x) log x + (x³ – x) C
dy
x
2 y tan 3
dx
2
Ans.
(Nagpur University, Summer 2004)
x
tan 3
dy
dy
2
3 x
2
Solution. Given equation : sin x
2 y tan
y
dx
2
dx sin x
sin x
dy
Py Q
This is linear form of
dx
x
tan 3
2
2
P
and Q
sin x
sin x
I.F. e
Pdx
2
dx e 2 cosec x dx e
e sin x
y.(I.F.) I.F.(Q dx) C
Solution is
2log tan
x
2
tan 2
x
tan 4
1
2
dx C
C =
x
x
x
2
cos 2
2 sin cos
2
2
2
1
x
x
tan 4 .sec 2 dx C
=
2
2
2
x
1 2x
Putting tan t so that
sec dx dt on R.H.S. (1), we get
2
2
2
x
1
t5
2 x
y.tan 2
t 4 (2dt ) C
C
y tan
2 2
2 5
5 x
tan
x
2 C
y tan 2
2
5
EXERCISE 3.5
Solve the following differential equations:
x
y tan
=
2
2
x
tan
2
2
tan3
x
2
x
2
... (1)
1.
dy 1
y x3 3
dx x
Ans. xy
Ans.
x5 3x 2
C
5
2
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Differential Equations
149
2. (2y – 3x) dx + x dy = 0
dy
y cot x cos x
3.
dx
dy
y sec x tan x
4.
dx
2 dy
5. cos x y tan x
dx
dy
5
6. ( x a ) 3 y ( x a )
dx
dy
7. x cos x y ( x sin x cos x ) 1
dx
dy
8. x log x y 2 log x
dx
dy
2
9. x 2 y x log x
dx
Ans. y x² = x³ + C
sin 2 x
C
Ans. y sin x
2
Cx
1
Ans. y
sec x tan x
Ans. y tan x 1 Ce tan x
Ans. 2y = (x + a)5 + 2C (x + a)³
Ans. x y = sin x + C cos x
Ans. y log x = (log x)² + C
x4
x4
log x C
4
16
4
sin
C
Ans. r sin 2
2
2
Ans. y x
10. dr (2r cot sin 2 ) d 0
dy
1
y cos x sin 2 x
Ans. y sin x 1 Ce sin x
dx
2
2 dy
2 1/ 2
12. (1 x ) 2 xy x(1 x )
Ans. y 1 x 2 C (1 x 2 )
dx
dy
y sin x (A.M.I.E.T.E., Dec 2005)
13. sec x
Ans. y = –sin x – 1 + cesinx
dx
14. y y tan x cos x, y (0) 0 (A.M.I.E.T.E., June 2006)
Ans. y = x cos x
11.
1
15. Solve (1 + y2) dx = (tan–1 y – x) dy (AMIETE, Dec. 2009) Ans. x = – tan–1 y – 1 + ce tan y
16. Find the value of so that e2 is an integrating factor of differential equation x (1 – y)
1
dx – dy = 0.
(A.M.I.E.T.E., Summer 2005)
Ans.
2
dy
17. Slove the differential equation cot 3 x
–3y = cos 3x + sin 3x, 0 < x < .
dx
2
1
(AMIETE, Dec. 2009) Ans. y cos 3 x 6 x sin 6 x cos 6 x
12
y2
18. The value of so that e
is an integrating factor of the differential equation
y2
(e 2
(A.M.I.E.T.E. Dec., 2005)
xy ) dy dx 0 is
1
1
(d)
Ans. (c)
2
2
dy
a 2 is given by
19. The solution of the differential equation (y + x)2
dx
yc
y c
(a) y x a tan
(b) y x tan
a
a
c
(c) y x a tan ( y c)
(d) a ( y x ) tan y
Ans. (a)
a
(AMIETE, June 2010)
(a) –1
(b) 1
(c)
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150
Differential Equations
3.10 EQUATIONS REDUCIBLE TO THE LINEAR FORM (BERNOULLI EQUATION)
The equation of the form
dy
+ Py = Qy n
...(1)
dx
where P and Q are constants or functions of x can be reduced to the linear form on dividing
1
by yn and substituting n 1 z
y
On dividing bothsides of (1) by yn, we get
1 dy
1
n 1 P Q
...(2)
n
y dx y
1
1 dy
dz
(1 n ) dy dz
Put n 1 z , so that
n
n
y
dx dx
y dx 1 n
y
1 dz
dz
Pz Q or
P(1 n ) z Q(1 n)
1 n dx
dx
which is a linear equation and can be solved easily by the previous method discussed in
article 3.8 on page 144.
(2) becomes
Example 13. Solve x²dy + y(x + y) dx = 0
(U.P. II Semester Summer 2006)
Solution. We have, x² dy + y (x + y) dx = 0
1 dy 1
1
dy y
y2
2
= 2
2
dx
xy
y
x
dx x
x
1 dy dz
1
Put
z so that 2
y dx dx
y
The given equation reduces to a linear differential equation in z.
dz z
1
2
dx x
x
I.F. e
1
x dx e log x elog1/ x 1 .
x
Hence the solution is
1
1 1
z. 2 . dx C
x
x x
2
1
x
C
xy
2
dy
Example 14. Solve: x
y log y xy e x
dx
dy
x y log y xy e x
Solution.
dx
Dividing by xy, we get
1 dy 1
log y e x
y dx x
1 dy dz
Put
log y = z, so that
y dx dx
dz z
Equation (1) becomes,
ex
dx x
z
x 3 dx C
x
1
1
2 C
xy
2x
Ans.
(A.M.I.E., Summer 2000)
...(1)
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Differential Equations
151
1
dx
I.F. e x elog x x
z x x ex d x C
Solution is
z x x ex ex C
x log y x e x e x C
Ans.
dy tan y
(1 x )e x sec y.
dx 1 x
dy tan y
(1 x )e x sec y
Solution.
dx 1 x
dy sin y
cos y
(1 x ) e x
dx 1 x
dy dz
Put
sin y = z, so that cos y
dx dx
dz
z
(1 x)e x
(1) becomes
dx 1 x
Example 15. Solve:
(Nagpur University, Summer 2000)
...(1)
1
1
1
1
dx
I .F . e 1 x e log(1 x) elog x
1 x
1
1
x
x
z.
(1 x) e .
dx C e dx C
Solution is
1 x
1 x
sin y
ex C
Ans.
1 x
dy
Example 16. Solve: tan y tan x cos y cos 2 x
(Nagpur University, Summer 2000)
dx
dy
2
Solution. tan y tan x cos y cos x
dx
dy
sec y tan y sec y tan x cos 2 x
dx
dz
dy
sec y tan y
Writing
z = sec y, so that
dx
dx
dz
The equation becomes
z tan x cos 2 x
dx
I.F. e
elog sec x sec x
The solution of the equation is
tan x dx
z sec x cos 2 x sec x dx C
sec y sec x cos x dx C sin x C
sec y (sin x C ) cos x
Example 17.
Solution.
Ans.
dx
x y 1 y
dy
dy
x y (1 y )
dx
dy
1 y
y
dx
x x
(Nagpur University, Summer 2004)
dy 1
1
1 y
dx x
x
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152
Differential Equations
which is in linear form of
Its solution is
dy
Py Q.
dx
1
P 1 ,
x
I.F. e
Pdx
Q
1
e
1
x
1 x dx
e x log x
1
y ( x . e ) ( x . e ) dx C
x
= e x .elog x e x . x x e x
y (I.F.) I.F.(Q dx) C
x
x
x
y ( x . e ) e dx C
x
x
x
y (x . e ) = e + C
1 C
y e x
Ans.
x x
Example 18. Solve the differential equation.
y log y dx + (x – log y) dy = 0
(Uttarakhand II Semester, June 2007)
Solution. We have,
y log y dx + (x – log y) dy = 0
dx x log y
dx
x
log y
dy
y log y
dy y log y y log y
dx
x
1
dy y log y y
1
I.F. e
Its solution is
x.log y
y log y dy
e log (log y ) log y
1
y (log y ) dy
(log y )
C
Ans.
2 –1
Example 19. Solve: (1 + y²) dx = (tan y – x) dy.
(AMIETE, June 2010, 2004, R.G.P.V., Bhopal, April 2010, June 2008,
U.P. (B. Pharm) 2005)
Solution. (1 + y²) dx = (tan–1 y – x) dy
x.log y
dx tan 1 y x
dy
1 y2
This is a linear differential equation.
dx
x
tan 1 y
dy 1 y 2 1 y 2
1
I.F. e
Its solution is
Put
x . e tan
1
y
1 y2 dy
e tan 1 y
1
e tan
tan 1 y
1 y2
y
dy C
tan–1 y = t on R.H.S., so that
x . e tan
1
y
1
1 y2
dy dt
et .t dt C t .et et C e tan
x tan
1
y 1 Ce tan
1
y
1
y
tan
1
y 1 C
Ans.
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Differential Equations
153
Example 20. Solve : r sin
dr
cos r 2
d
(Nagpur University, Summer 2005)
Solution. The given equation can be written as
dr
cos r sin r 2
d
2 dr
r 1 tan sec
Dividing (1) by r 2 cos , we get r
d
dv
2 dr
Putting
r 1 v so that r d d in (2), we get
dv
v tan sec
d
I.F. e
Solution is
tan d
2.
3.
4.
5.
6.
7.
v sec sec , sec C
1 dy 1
2 x e x
y 2 dx y
dy
y
3 3 2 x4 y 4
dx
x
dy
y tan x y 2 sec x
dx
dy
2 y tan x y 2 tan 2 x, if y 1 at x 0
dx
dy
tan x tan y cos x sec y
dx
dy + y tan x . dx = y² sec x . dx
(x² y² + xy) y dx + (x² y² – 1) x dy = 0
8. (x² + y² + x) dx + xy dy = 0
9.
dy
y 3e x y 3
dx
10. (x – y²) dx + 2 x y dy = 0
y dy
x
11. e 1 e
dx
2
3 dy
y 4 cos x
12. x y x
dx
dy
2
x2
13. 3 dx x 1 . y 2
y
dy
14. cos x 4 y sin x 4 y sec x
dx
... (2)
elog sec sec
v sec sec2 d C
sec
tan C
r
1
r
sin C cos
EXERCISE 3.6
Solve the following differential equations:
1.
... (1)
r 1 (sin C cos )
Ans.
Ans. ex + x²y + Cy = 0
Ans.
1
y3
x5 Cx3
Ans. sec x = (tan x + C) y
1 2
tan 3 x
sec
x
1
Ans.
y
3
Ans. sin y sec x = x + C
Ans. y (x + C) + cos x = 0
Ans. x y = log C y
2 2
Ans. x y
Ans.
1
y2
x 4 2 x3
C
2
3
6 e x C e2 x
y2
log x C
x
e2 x
C
Ans. e x y
2
Ans.
Ans. x3 = y3(3 sin x – C)
x5 x 4 x 3
C
5
2
3
3
tan
x
y sec 2 x 2 tan x
C
3
3
2
Ans. y ( x 1)
Ans.
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154
15.
16.
17.
18.
19.
Differential Equations
dy
x sin 2 y x3 cos 2 y
dx
1 dy
2 x tan 1 y x 3
1 y 2 dx
e–y sec² y dy = dx + x dy
dy
( x y 1)
1
dx
dy
y3
2x
dx e y 2
2
1 2
( x 1) C e x
2
1 2
1
x2
Ans. tan y ( x 1) C e
2
Ans. x ey = tan y + C
Ans. tan y
Ans. x + y + 2 = C ey
Ans. e–2x y² + 2 log y + C = 0
20. dx – xy (1 + xy2) dy = 0
21.
dy y
y
log y 2 (log y )2
dx x
x
dy
xy xy 2
dx
dy
y x3 y 6
27. x
dx
22. 3
Ans.
2
1
y 2 2 Ce y / 2
x
(A.M.I.E.T.E., Summer 2004, 2003, Winter 2003, 2001)
1
1
Ans. x log y 2 C
2x
(A.M.I.E.T.E., June 2009) Ans. y 3 1 Ce x
(AMIETE, June 2010)
Ans.
1
5 5
y x
5
2 x2
2
/2
C
dx
23. General solution of linear differential equation of first order dy Px Q (where P and Q
are constants or functions of y) is
P . dx
P. dx
P . dy
P. dy
(a) ye
dx + c (b) xe
dy + c
Q e
Q e
(c) y Q e
P. dx
P. dy
dx + c
(d) x Q e
dy + c (AMIETE, June, 2010) Ans. (b)
3.11 EXACT DIFFERENTIAL EQUATION
An exact differential equation is formed by directly differentiating its primitive (solution)
without any other process
Mdx + Ndy = 0
is said to be an exact differential equation if it satisfies the following condition
M N
=
y
x
where M denotes the differential co-efficient of M with respect to y keeping x constant and N ,
y
x
the differential co-efficient of N with respect to x, keeping y constant.
Method for Solving Exact Differential Equations
Step I. Integrate M w.r.t. x keeping y constant
Step II. Integrate w.r.t. y, only those terms of N which do not contain x.
Step III. Result of I + Result of II = Constant.
Example 21. Solve :
(5x4 + 3x2y2 – 2xy3) dx + (2x3y – 3x2y2 – 5y4) dy = 0
Solution. Here, M = 5x4 + 3x2y2 – 2xy3,
N = 2x3y – 3x2y2 – 5y4
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Differential Equations
155
M
= 6x2y – 6xy2,
y
N
M
=
, the given equation is exact.
x
y
Since,
Now
N
= 6x2y – 6xy2
x
M dx (terms of N is not containing x) dy C
5x 3x y 2 xy dx 5 y dy C
4
2 2
3
(y constant)
4
x5 + x3y2 – x2y3 – y5 = C
2
2
Ans.
2
Example 22. Solve: 2 xy cos x 2 xy 1 dx sin x x 3 dy 0
(Nagpur University, Summer 2000)
Solution. Here we have
2 xy cos x
2 xy 1 dx sin x 2 x 2 3 dy 0
M dx + N dy = 0
Comparing (1) and (2), we get
M
M = 2xy cos x² – 2xy + 1
= 2x cos x² – 2x
y
N
N = sin x² – x² + 3
= 2x cos x² – 2x
x
M
N
Here,
=
y
x
So the given differential equation is exact differential equation.
Hence solution is
2
M dx
... (1)
... (2)
(terms of N not containing x ) dy C
y as const
Put
(2 xy cos x 2 2 xy 1) dx 3 dy C
2
[ y(2 x cos x ) y(2x) 1] dx 3 dy C
y 2 x cos x dx y 2 x dx 1 dx 3 y dy C
2
x2 = t so that 2x dx = dt
x2
x 3y C
2
y sin t – x² y + x + 3y = C
y sin x² – yx² + x + 3y = C
y cos t dt 2 y
Example 23. Solve :
Solution. We have,
x
1 e y
Ans.
x dy
(1 e x / y ) e x / y 1
0
y dx
(Nagpur University, Summer 2008, A.M.I.E.T.E. June, 2009)
x x dy
e y 1 0
y dx
x
1 e y
x
x
dx e y e y x dy 0
y
x
M 1 e
x
y
M
x
2 ey
y
y
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156
Differential Equations
x
y
x
x
y
x
x
x
N 1 y 1 y x y
x
x
e e 2 e 2 ey
N e e
x y
y
y
y
y
M
N
=
y
x
Given equation is exact.
x
Its solution is
1 e y dx (terms of N not containing x) dy C
x
x
1 e y dx 0 dy C
Ans.
x ye y C
x
Example 24. Solve: [1 log ( x y )] dx 1 dy 0
(Nagpur University, Winter 2003)
y
x
Solution. [1 log x y ] dx 1 dy 0
y
x
[1 log x log y ] dx 1 dy 0
y
which is in the form M dx + N dy = 0
x
M = [1 + log x + log y]
and
N 1
y
M
N
N 1
M 1
and
=
y
x
x y
y
y
Hence the given differential equation is exact.
Solution is
M dx N (terms not containing x) dy C
y constant
y constant
Now,
(1 log x log y) dx dy C
x log x dx log y dx y C
1
d
log x dx log x .(1) dx (log x) x dx (log x) x dx x log x x .x dx
x log x dx x log x x x[log x 1]
Equation (1) becomes
... (1)
x + x log x – x + x log y + y = C
x [log x + log y] + y = C x log xy + y = C
Ans.
EXERCISE 3.7
Solve the following differential equations (1 – 12).
1. (x + y – 10) dx + (x – y – 2) dy = 0
2. (y2 – x2) dx + 2x y dy = 0
3.
1 3e dx 3e
x/ y
x/ y
x2
y2
xy 10 x
2y C
2
2
x3
x y2 C
Ans.
3
Ans.
x
x/y
1 dy 0 (R.G.P.V. Bhopal, Winter 2010) Ans. x + 3y e = C
y
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Differential Equations
157
y2
C
2
Ans. y tan x + sec x + y2 = C
Ans. xy x 2
4. (2x – y) dx = (x – y) dy
5. (y sec2 x + sec x tan x) dx + (tan x + 2y) dy = 0
6. (ax + hy + g) dx + (hx + by + f) dy = 0
Ans. ax² + 2h xy + by2 + 2gx + 2fy + C = 0
8. (2xy + ey) dx + (x2 + xey) dy = 0
x5
x 2 y 2 xy 4 cos y C
5
Ans. x2y + xey = C
9. (x2 + 2ye2x) dy + (2xy + 2y2e2x) dx = 0
Ans. x2y + y2 e2x = C
7. (x4 – 2xy2 + y4) dx – (2x2y – 4xy3 + sin y) dy = 0
Ans.
1
10. y 1 cos y dx ( x log x x sin y ) dy 0 (M.D.U., 2010)
x
Ans. y (x + log x) + x cos y = C
11. (x3 – 3xy2) dx + (y3 – 3x3y) dy = 0, y(0) = 1
Ans. x4 – 6x2y2 + y4 = 1
12. The differential equation M (x, y) dx + N (x, y) dy = 0 is an exact differential equation if
M N
M N
M N
(a) y x = 0
(b) y x = 0 (c) y x 1 (d) None of the above
(A.M.I.E.T.E. Dec. 2010, Dec 2006) Ans. (b)
3.12 EQUATIONS REDUCIBLE TO THE EXACT EQUATIONS
Sometimes a differential equation which is not exact may become so, on multiplication by a
suitable function known as the integrating factor.
M N
y x
f ( x ) dx
Rule 1. If
is a function of x alone, say f (x), then I.F. e
N
Example 25. Solve (2x log x – xy) dy + 2y dx = 0
Solution.
M = 2y,
N = 2x log x – xy
M
N
2,
2(1 log x ) y
y
x
M N
1
y x 2 2 2 log x y (2 log x y )
Here,
f ( x)
N
2 x log x xy
x (2 log x y )
x
I.F. e
f ( x ) dx
... (1)
1
e
x dx e log x elog x1 x –1 1
x
1
On multiplying the given differential equation (1) by , we get
x
2y
2y
dx (2 log x y )dy 0
dx y dy c
x
x
1
2 y log x y 2 c
2
EXERCISE 3.8
Solve the following differential equations:
1. (y log y) dx + (x – log y) dy = 0
1 3 1 2
1
2
2. y y x dx 1 y x dy 0
3
2
4
Ans.
Ans. 2x log y = c + (log y)²
Ans.
yx 4 y 3 x 4 x 6
c
4
12
12
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158
Differential Equations
y
y2
x2
c
x
2
1
3
Ans. tan y x sin y c
x
Ans. y2 = cx – x log x
3. (y – 2x3) dx – x (1 – xy) dy = 0
Ans.
4. (x sec2y – x2 cos y) dy = (tan y – 3x4) dx
5. (x – y2) dx + 2xy dy = 0
N M
x y
Rule II. If
is a function of y alone, say f (y), then
M
I.F. e
4
Example 26. Solve (y + 2y) dx + (xy3 + 2y4 – 4x) dy = 0
f ( y ) dy
Solution. Here M = y4 + 2y;
N = xy3 + 2y4 – 4x
...(1)
M
N
4 y 3 2;
y3 4
y
x
N M
( y 3 4) (4 y 3 2) 3( y 3 2)
3
x y
f ( y)
4
3
M
y
y 2y
y ( y 2)
I.F. e
f ( y ) dy
3
e
y dy
e 3log y elog y
On multiplying the given equation (1) by
1
y3
3
y 3
y3
we get the exact differential equation.
2
4x
y 2 dx x 2 y 3 dy 0
y
y
2
y 2 dx 2 y dy c
y
EXERCISE 3.9
Solve the following differential equations:
1
2
x y 2 y 2 c
y
1. (3x2y4 + 2xy) dx + (2x3y3 – x2) dy = 0
Ans. x3 y 2
2. (xy3 + y) dx + 2(x2y2 + x + y4) dy = 0
Ans.
3. y(x2y + ex)dx – exdy = 0
Ans.
x2
c
y
x2 y 4
y6
xy 2
c
2
3
x3 e x
Ans.
c
3
y
x2
x
3 c
y y
Rule III. If M is of the form M = y f1 (xy) and N is of the form N = x f2(xy)
4. (2x4y4ey + 2xy3 + y) dx + (x2y4ey – x2y2 – 3x) dy = 0
Then
I.F. =
Ans. x 2 e y
1
M .x N . y
Example 27. Solve y (xy + 2x2y2) dx + x (xy – x2y2) dy = 0
Solution.
y (xy + 2x2y2) dx + x (xy – x2y2) dy = 0
... (1)
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Differential Equations
159
Dividing (1) by xy, we get
y (1 + 2xy) dx + x (1 – xy) dy = 0
M = y f1 (xy), N = x f2 (xy)
I.F.
On multiplying (2) by
... (2)
1
1
1
Mx Ny xy (1 2 xy ) xy (1 xy ) 3x 2 y 2
1
3x 2 y 2
, we have an exact differential equation
1
1
2
1
dy 0
2 dx
2
3y
3x y 3x
3xy
1
2
1
log x log y c
3 xy 3
3
1
2
1
3x y 3x dx 3 y dy c
2
1
2log x log y b
xy
Ans.
EXERCISE 3.10
Solve the following differential equations
1. (y – xy2) dx – (x + x²y) dy = 0
2. y (1 + xy) dx + x(1 – xy) dy = 0
2. y (1 + xy) dx + x (1 + xy + x2y2) dy = 0
4. (xy sin xy + cos xy) y dx + (xy sin xy – cos xy) x dy = 0
x
Ans. log xy A
y
y
Ans. xy log c xy 1
x
1
1
log y c
Ans.
2 x 2 y 2 xy
Ans. y cos xy = cx
Rule IV. For of this type of xm y n (ay dx bx dy) xm y n (a y dx b x dy) 0, the integrating factor
is xh yk.
Example 28. Solve
m h 1 n k 1
m h 1 n k 1
,
and
a
b
a
b
(y3 – 2x2y) dx + (2xy2 – x3) dy = 0
Solution.
(y3 – 2x2y) dx + (2xy2 – x3) dy = 0
where
y2 (ydx + 2xdy) + x2 (–2ydx – xdy) = 0
Here m = 0, h = 2, a = 1, b = 2,
m 2, n 0, a 2, b 1
0 h 1 2 k 1
2 h 1 0 k 1
and
1
2
2
1
2h + 2 = 2 + k + 1 and h + 3 = 2k + 2
2h – k = 1 and h – 2k = –1
On solving h = k = 1. Integrating Factor = x y
Multiplying the given equation by x y, we get
(xy4 – 2x3y2) dx + (2x2y3 – x4y) dy = 0
which is an exact differential equation.
( xy 4 2 x 3 y 2 )dx C
x2 y 4 2 x4 y 2
C
2
4
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160
Differential Equations
x2y4 – x4y2 = C
3
x2 y2 (y2 – x2) = C
2 2
Example 29. Solve (3y – 2xy ) dx + (4x – 3x y ) dy = 0.
3
Solution.
Ans.
(U.P., II Semester, June 2007)
2 2
(3y – 2xy ) dx + (4x – 3x y ) dy = 0
(3y dx + 4x dy) + xy2(–2y dx – 3x dy) = 0
Comparing the coefficients of (1) with
...(1)
xm y n (a y dx b x dy) x m y n (a y dx b x dy ) 0, we get
m = 0, n = 0, a = 3, b = 4
m 1, n 2, a 2, b 3
To find the integrating factor xh yk
m h 1 n k 1
m h 1 n k 1
and
a
b
a
b
0 h 1 0 k 1
1 h 1 2 k 1
and
3
4
2
3
h 1 k 1
h 2 k 3
and
4h – 3k + 1 = 0
3
4
2
3
and
3h – 2k = 0
h
... (2)
2k
3
... (3)
Putting the value of h from (3) in (2), we get
8k
k
– 3k 1 0
1 0
3
3
2k 2 3
Putting k = 3 in (2), we get h
2
3
3
I.F. = xhyk = x2y3
k=3
On multiplying the given differential equation by x2y3, we get
x2y3 (3y – 2xy3)dx + x2y3(4x–3x2y2) dy = 0
(3x2y4 – 2x3y6) dx + (4x3y3 – 3x4y5) dy = 0
This is the exact differential equation.
Its solution is
(3x 2 y 4 2 x 3 y6 )dx 0
x3 y 4
x4 6
y C
2
Ans.
EXERCISE 3.11
Solve the following differential equations.
1. (2y dx + 3x dy) + 2xy (3y dx + 4x dy) = 0
Ans. x2y3 (1 + 2xy) = c
3/2
2. (y2 + 2yx2) dx + (2x3 – xy) dy = 0
2
Ans. 4( xy )1/ 2
3
y
x
3. (3x + 2y2)y dx + 2x (2x + 3y2) dy = 0
Ans. x2y4 (x + y2) = c
4. (2x2y2 + y) dx – (x3y – 3x) dy = 0
Ans.
5. x (3y dx + 2x dy) + 8y4 (y dx + 3x dy) = 0
Ans. x3y2 + 4x2y6 = c
c
7 10 / 7 5 / 7 7 4 / 7 –12 / 7
x
y
x
y
c
5
4
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Differential Equations
161
Rule V.
If the given equation M dx + N dy = 0 is homogeneous equation and Mx + Ny 0, then
1
is an integrating factor..
Mx Ny
dy x 3 y 3
dx
xy 2
(x3 + y3) dx – (xy2) dy = 0
M = x3 + y3,
N = –xy2
Example 30. Solve
Solution.
Here
I.F.
Multiplying (1) by
1
x
4
... (1)
1
1
1
Mx Ny x ( x3 y 3 ) xy 2 ( y ) x 4
1
4
( x 3 y 3 ) dx
1
( xy 2 ) dy 0
x
x4
1 y3
y2
4 dx 3 dy 0, which is an exact differential equation.
x
x x
1 y3
y3
log x 3 c
Ans.
4 dx c
3x
x x
we get
EXERCISE 3.12
Solve the following differential equations:
1. x2y dx – (x3 + y3) dy = 0
Ans.
2. (y3 – 3xy2) dx + (2x2y – xy2) dy = 0
Ans.
x3
3 y3
log y c
y
3log x 2 log y c
x
x
Ans. 2log x 3log y c
y
Ans. x2y4 – x4y2 = c
3. (x2y – 2xy2) dx – (x3 – 3x2y) dy = 0
4. (y3 – 2yx2) dx + (2xy2 – x3) dy = 0
3.13 EQUATIONS OF FIRST ORDER AND HIGHER DEGREE
dy
dy
The differential equations will involve
in higher degree and
will be denoted by p.
dx
dx
The differential equation will be of the form f (x, y, p) = 0.
Case 1. Equations solvable for p.
Example 31. Solve : x2 = 1 + p2
Solution.
x2 = 1 + p2
p x2 1
p2 = x2 – 1
dy
x2 1
dx
dy x 2 1 dx
x 2
1
x 1 log x x 2 1 c
2
2
Case II. Equations solvable for y.
(i) Differentiate the given equation w.r.t. “x”.
(ii) Eliminate p from the given equation and the equation obtained as above.
(iii) The eliminant is the required solution.
Example 32. Solve: y = (x – a) p – p2.
Solution.
y = (x – a) p – p2
which gives on integration y
Ans.
... (1)
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162
Differential Equations
Differentiating (1) w.r.t. “x” we obtain
dy
dp
dp
p ( x a)
2p
dx
dx
dx
dp
dp
p p ( x a) 2 p
dx
dx
dp
dp
0 ( x a)
2p
dx
dx
dp
0
[ x a 2 p]
dx
On integration, we get p = c.
Putting the value of p in (1), we get
y = (x – a) c – c2
Case III. Equations solvable for x
dp
0
dx
Ans.
(i) Differentiate the given equation w.r.t. “y”.
(ii) Solve the equation obtained as in (1) for p.
(iii) Eliminate p, by putting the value of p in the given equation.
(iv) The eliminant is the required solution.
Example 33. Solve:
y = 2px + yp2
Solution.
y = 2px + yp2
2
2px = y – yp
Differentiating (2) w.r.t. “y” we get
dx 1
y dp
dp
2
p y
dy p p 2 dy
dy
2 1
y dp
dp
2
p y
p p p dy
dy
1
dp
1
p y 2 1
p
p
dy
dy dp
y dp
1
y
p
p dy
log p y = log c p y = c
Putting the value of p in (1), we get
c2
c
y 2 x y 2
y
y
2
y
=
c(2x
+
c)
Class IV. Clairaut’s Equation.
y
2 x yp
p
... (2)
1
y dp
dp
p 2
y
p
dy
p dy
1 p2
1 p 2 dp
y
p
p 2 dy
log y log p log c
c
p
y
y2 = 2 cx + c2
... (1)
The equation y = px + f (p) is known as Clairaut’s equation.
Ans.
... (1)
Differentiating (1) w.r.t. “x”, we get
dy
dp
dp
px
f ( p )
dx
dx
dx
dp
dp
dp
dp
p px
f ( p )
0x
f ( p )
dx
dx
dx
dx
dp
dp
[ x f ( p )]
0
0
p = a (constant)
dx
dx
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Differential Equations
163
Putting the value of p in (1), we have
y = ax + f (a)
which is the required solution.
Method. In the Clairaut’s equation, on replacing p by a (constant), we get the solution of the
equation.
Example 34. Solve : p = log (p x – y)
Solution. p = log (p x – y) or ep = p x – y or y = p x – ep
Which is Clairaut’s equation.
Hence its solution is y = a x –ea
Ans.
EXERCISE 3.13
Solve the following differential equations.
1. xp2 + x = 2yp
Ans. 2cy = c2x2 + 1
2. x(1 + p2) = 1
2
1
Ans. y c x x tan
c
2
Ans. y 3 , y c1 x
x
3. x2p2 + xyp – 6y2 = 0
dy dx x y
dx dy y x
5. y = px + p3
6. x2 (y – px) = yp2
1 x
x
Ans. xy = c, x2 – y2 = c
4.
Ans. y = ax + a3
Ans. y2 = cx2 + c2
Y
3.14 ORTHOGONAL TRAJECTORIES
Two families of curves are such that every curve of either
family cuts each curve of the other family at right angles.
They are called orthogonal trajectories of each other.
Orthogonal trajectories are very useful in engineering
problems.
For example:
(i) The path of an electric field is perpendicular to equipotential curves.
(ii) In fluid flow, the stream lines and equipotential lines
are orthogonal trajectories.
O
(iii) The lines of heat flow is perpendicular to isothermal curves.
Working rule to find orthogonal trajectories of curves
90°
X
Step 1. By differenciating the equaton of curves find the differential equations in the form
dy
f x, y, 0
dx
dy
dx
Step 2. Replace
by
(M1. M2 = –1)
dx
dy
dx
Step 3. Solve the differential equation of the orthogonal trajectories i.e., f x, y – 0
dy
Self-orthogonal. A given family of curves is said to be ‘self-orthogonal’ if the family of
orthogonal trajectory is the same as the given family of curves.
Example 35. Find the orthogonal trajectories of the family of curves xy = c.
Solution. Here, we have
xy = c
... (1)
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164
Differential Equations
Differentiating (1), w.r.t., “x”, we get
dy
y x
0
dx
dx
dy
On replacing
by , we get
dy
dx
dx
y
dy
x
y dy = x dx
2
dy
y
dx
x
dy x
dx y
... (2)
2
y
x
c
2
2
y2 – x2 = 2c
Integrating (2), we get
Ans.
Example 36. Show that the family of porabolas y2 = 2cx + c2 is “self-orthogonal.”
Solution. Here we have
y2 = 2cx + c2
... (1)
dy
dx
2
dy
dy
2
Putting the value of c in (1), we have y 2 y x y
dx dx
dy
Putting
p in (2), we get
dx
y2 = 2ypx + y2p2
Differentiating (1), we get 2 y
dy
2c
dx
c y
... (2)
... (3)
This is differential equation of give n family of parabolas.
For orthogonal trajectories we put
1
for p in (3)
p
1
1
y2 2 y x y2
p
p
y2p2 = – 2pyx + y2
2
y2
2 yx y 2
2
p
p
Rewriting, we get
y2 = 2ypx + y2p2
Which is same as equation (3). Thus (2) is D.E. for the given family and its orthogonal
trajectories.
Hence, the given family is self-orthogonal.
Proved.
EXERCISE 3.14
Find the orthogonal trajectories of the following family of curves:
1. y2 = cx3
Ans. (x + 1)2 + y2 = a2
3. x2 – y2 = c
2
2. x2 – y2 = cx
Ans. y (y2 + 3x2) = c
Ans. xy = c
2
4. (a + x) y = x (3a – x) Ans. (x2 + y2)5 = cy3 (5x2 + y2)
5. y = ce–2x + 3x, passing through the point (0, 3) Ans. 9x – 3y + 5 = –4e6(3 – y)
6. 16x2 + y2 = c
Ans. y16 = kx
7. y = tan x + c
Ans. 2x + 4y + sin 2x = k
8. y = ax2
Ans. x2 + 2y2 = c
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Differential Equations
2
2
9. x + (y – c) = c
165
2
2
Ans. x + y = cx
10. x2 + y2 + 2gx + 2fy + c = 0
3.15
2
Ans. x2 + y2 + 2fy – c = 0
POLAR EQUATION OF THE FAMILY OF CURVES
Let the polar equation of the family of curves be f (r , , c ) 0
... (1)
Working Rule
Step 1. On differentiating and eliminating the arbitrary constant c between (1) and
f (r , , c ) 0 we get the differential equation of (1) i.e.,
dr
F r , ,
... (2)
0
d
dr
2 d
Step 2. Replace
by r
in (2). Here we will get the differential equation of orthogonal
d
dr
trajectory i.e.,
d
F r, r 2
... (3)
0
dr
Step 3. Integrating (3) to get the equation of the orthogonal trajectory.
Example 37. Find the orthogonal trajectory of the cardioids r = a (1 – cos ).
Solution. We have, r = a(1 – cos )
... (1)
dr
a sin
Differentiating (1) w.r.t. , we get
... (2)
d
Dividing (2) by (1) to eliminate a, we get
2sin cos
1 dr
sin
2
2
cot
... (3)
r d 1 cos
2
1 1 2 sin 2
2
which is the differential equation of (1).
1 2 d
dr
2 d
Replacing
by r
in (3), we get – r
cot
r
dr
2
d
dr
d
cot
dr
2
dr
Separating the variables we get
tan d
r
2
2
Integrating (4), we get log r 2 log cos log c log c cos
2
2
r c cos 2
2
Which is the required trajectory.
Example 38. Find the orthogonal trajectory the family of curves
r2 = c sin2
Solution. We have
r2 = c sin2
dr
2c cos 2
Differentiating (1), we get 2r
d
2 dr
2 cot 2
Dividing (2) by (1), to eliminate ‘c’ we get
r d
r
... (4)
r
c
(1 cos )
2
Ans.
... (1)
... (2)
... (3)
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166
Differential Equations
2 2 d
dr
2 d
by r
in (3), we have r
2 cot 2
r
dr
d
dr
d
2 r
2 cot 2
dr
dr
tan 2 d
Separating the variables of (4), we obtain
r
1
Integrating (5), we get log r log cos 2 log c
2
2 log r = log c cos
r2 = c cos2
which is the required trajectory
Replacing
... (4)
... (5)
Ans.
EXERCISE 3.15
Find the orthogonal trajectory of the following families of the curves:
1. r ce
3. r a (1 cos )
Ans. r ke
Ans. r c (1 cos )
2. r c
4. r n sin n a n
2
2
4
Ans. r ke
Ans. r n cos n c n
5. r a cos 2
Ans. r 2 c sin
6. r 2a (sin cos ) Ans. r 2c (sin cos
7. r c (1 sin 2 )
Ans. r 2 k cos .cot
a
8. r
Ans. r 2 sin 3 (1 cos )
1 2 cos
3.16 ELECTRICAL CIRCUIT
We will consider circuits made up of
(i) Voltage source which may be a battery or a generator.
(ii)
Resistance, inductance and capacitance.
The formation of differential equation for an electric circuit depends upon the following laws.
(i) i
dq
,
dt
(ii) Voltage drop across resistance R = Ri
di
dt
q
(iv) Voltage drop across capacitance C =
C
Kirchhoff’s laws
(iii) Voltage drop across inductance L = L.
I. Voltage law. The algebraic sum of the voltage drop around any closed circuit is equal
to the resultant electromotive force in the circuit.
II. Current law. At a junction or node, current coming is equal to current going.
(i) L - R series circuit. Let i be the current flowing in the circuit containing resistance R
and inductance L in series, with voltage source E, at any time t.
di
di R
E
Ri L
By voltage law
=E
...(1) (M.U. II Semester, 2009)
i
dt
dt L
L
This is the linear differential equation
R
I.F. = e
L dt
R
eL
t
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Differential Equations
167
Its solution is
R
t
i. e L
i. e L =
R
R
R
E t
= e L dt A
L
L
R
t
E L Lt
e A
L R
E
R
i
At t = 0,
i= 0
i
Rt
–
Ae L
–
E
...(2)
A–
+
E
R
i
E
R
O
t
Rt
E
L
Thus, (2) becomes i =
1 e
R
(ii) C-R series circuit. Let i be current in the circuit containing resistance R, L, and capacitance
C in series with voltage source E, at any time t.
By voltage law
q
=E
C
dq q
R
=E
dt C
dq
i dt
Ri
di
Ri E0 sin wt
dt
Example 39. Solve the equation L
where L, R and E0 are constants and discuss the case when t increases indefinitely.
Solution. L
di
Ri E0 sin wt
dt
di R
E
i = 0 sin wt
dt L
L
R
I.F. = e
Solution is
R
t
i.e L
R
t
i. e L
=
L dt
E0
L
R
eL
R
E0 sin wt
t
e L sin wt dt A
eL
E0
=
L
2
t
b
sin b x – tan –1
a
a2 b2
ea x
Lw
sin wt – tan –1
A
R
R
w2
L2
R
– t
E0
Lw
L
sin wt – tan –1
Ae
2
2 2
R
R L w
As t increases indefinitely, then Ae
i=
R
t
e a x sin bx dx
R
i=
so
L
–
Rt
L
tends to zero.
E0
Lw
sin wt – tan –1
2
2 2
R
R L w
Ans.
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168
Differential Equations
EXERCISE 3.16
1. A coil having a resistance of 15 ohms and an inductance of 10 henries is connected to 90 volts supply.
Determine the value of current after 2 seconds. (e–3 = 0.05)
Ans. 5.985 amp.
2. A resistance of 70 ohms, an inductance of 0.80 henry are connected in series with a battery of 10 volts.
175
–
t
1
2
1 – e
7
3. A circuit consists of resistance R ohms and a condenser of C farads connected to a constant e.m.f. E; if
q
q
is the voltage of the condenser at time t after closing the circuit Show that
E – Ri and hence
C
C
t
–
show that the voltage at time t is E 1 – e CR .
Determine the expression for current as a function of time after t = 0.
Ans. i
t
4. Show that the current i
q – R.C.
e
during the discharge of a condenser of charge Q coulomb through
CR
a resistance R ohms.
5. A condenser of capacity C farads with voltage v0 is discharged through a resistance R ohms. Show that
if q coulomb is the charge on the condenser, i ampere the current and v the voltage at time t.
1
–
dq
, hence show that v = v e Rc .
0
dt
q = Cv, v = Ri and i = –
6. Solve L
di
Ri E cos wt
dt
Ans. i
E
L2 w2 R2
( R cos wt Lw sin wt – Re
–
Rt
L )
7. A circuit consists of a resistance R ohms and an inductance of L henry connected to a generator of
E cos (wt + ) voltage. Find the current in the circuit. (i = 0, when t = 0).
Ans. i
E
2
2 2
R Lw
cos [ wt – tan –1
Lw
]–
R
E
2
2 2
R L w
.e
–
R
t
L
Lw
cos – tan –1
R
3.17 VERTICAL MOTION
Example 40. A body falling vertically under gravity encounters resistance of the atmosphere.
If the resistance varies as the velocity, show that the equation of motion is given by
du
= g – ku
dt
where u is the velocity, k is a constant and g is the acceleration due to gravity. Show that as
dx
t increases, u approaches the value g/k. Also, if u =
where x is the distance fallen by the
dt
body from rest in time t, show that
gt
g
–
(1 – e – kt )
x=
k k2
Solution. Let the mass of the falling body be unity.
du
Acceleration =
dt
du du
Force acting downward = 1.
dt dt
Force of resistance = ku
du
g – ku
Net force acting downward = g – ku
...(1)Proved.
dt
du
= dt
g – ku
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Differential Equations
169
du
g – ku
Integrating, we get
=
dt
1
log ( g – ku ) log A log ( g – ku ) – 1/ k A
k
A(g – ku)–1/k = et (g – ku) = Ak e–kt
t= –
u=
If t increases very large then
g Ak – kt
–
e
k
k
Ak – kt
e 0
k
g
u=
k
Given
when t
Proved.
u
=
dx
dt
du d 2 x
dt dt 2
du
and u in (1), we get
dt
Putting the values of
d2x
dx
=g
(D2 + kD) x = g
dt
dt
A.E. is m(m + k) = 0
m = 0, m = – k
C.F. = A1 + A2e–kt
1
1
g t
g
P.I. = 2
2D k
D kD
2
k
1
t
2D
t
1
t
2D
g 1
=
g
g = 1
2
D
k
k
k
k
k
1
k
gt
x = A1 A2 e – kt
k
Putting the values of t = 0 and x = 0 in (2), we get
0 = A1 + A2
A2 = – A1
gt
(2) becomes
x = A1 – A1 e – kt
k
dx
g
On differentiating (3), we get
A1 ke – kt
dt
k
dx
g
g
On putting
0, when t = 0 in (4), we get 0 A1k
A1 – 2
dt
k
k
Putting the value of A1 in (3), we get
tg
k
...(2)
...(3)
...(4)
gt
g
gt
– 2 (1 – e – kt )
x
Proved.
k
k
k
k
k
Example 41. The acceleration and velocity of a body falling in the air approximately satisfy
the equation :
Acceleration = g – kv2, where v is the velocity of the body at any time t, and g, k are constants.
Find the distance traversed as a function of the time, if the body falls from rest.
x–
g
2
g
2
e – kt
Show that value of v will never exceed
g
.
k
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170
Differential Equations
Solution Acceleration = g – k v2
dv
g – k v2
dt
1
2 g g k . v
1
dv
g – k v2
dt .
dv dt
g – k .v
1
On integrating, we get
1
1
1
log ( g k . v) –
log ( g – k . v) t A
2 g k
2 gk
1
2 gk
g k. v
log
=t+A
g – k. v
On putting t = 0, v = 0 in (1), we get
Equation (1) becomes
1
log 1 0 A A 0
2 gk
g – k.v
g k .v
1
g k .v
log
2 gk
...(1)
2
= e
t
log
g k .v
g – k .v
2 gk t
gk t
g – k .v
By componendo and dividendo, we have
k .v
g
=
v =
e
2 gkt
e2
gkt
–1
1
e
gk t
–e
– gk t
e
gk t
e–
gk t
tan h gk t
g
tan h gk t
k
Whatever the value of t may be tanh gk t 1.
Hence the value of v will never exceed
dx
=
dt
g
.
k
Proved.
g
tanh gk t
k
g
k
tanh
Integrating again, we get
x =
when t = 0,
B =0
1
x = log cosh gk t
k
x = 0 then
gk t dt =
1
log cosh gk t B
k
Ans.
EXERCISE 3.17
1. A moving body is opposed by a force proportional to the displacement and by a resistance proportional
to the square of velocity. Prove that the velocity is given by
cx
c
VdV
V 2 ae –
– K1 x – K 2 V 2
Hint. Equation of motion is m
b ab2
dx
2. A particle of mass m is projected vertically upward with an initial velocity v0. The resisting force at any
time is K times the velocity. Formulate the differential equation of motion and show that the distance s
covered by the particle at any time t is given by
v0
g
g
– Kt
)– t
s = 2 (1 – e
K
K
K
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Differential Equations
171
3. A particle falls in a vertical line under gravity (supposed constant) and the force of air resistance to its
motion is proportional to its velocity. Show that its velocity cannot exceed a particular limit.
g
Ans. V
K
n2
4. A body falling from rest is subjected to a force of gravity and an air resistance of
times the square of
g
g
velocity. Show that the distance travelled by the body in t seconds in
log cosh nt.
n2
5. A body of mass m, falling from rest is subject to the force of gravity and an air resistance proportional
to the square of the velocity Kv2. If it falls through a distance x and possesses a velocity v, at the instant,
prove that
a2
2kx
log 2
where
a – v2
m
mg
a2
k
(A.M.I.E.T.E., June 2009)
HEAT CONDUCTION
Example 42. The rate at which a body cools is proportional to the difference between the
temperature of the body and that of the surrounding air. If a body in air at 25°C will cool
from 100° to 75° in one minute, find its temperature at the end of three minutes.
Solution. Let temperature of the body be T°C.
dT
k (T – 25)
dt
or
log (T – 25) = kt + log A
or
dT
k dt
T – 25
T – 25
kt
log
A
T – 25 = A e kt
...(1)
When t = 0, then T = 100, from (1) A = 75
When t = 1, then T = 75 and A =75, From (1)
(1) becomes
When t = 3,
2 k
=e
3
T = 25 + 75 ekt
then T = 25 + 75 e3k = 25 + 75 × 8 / 27 = 47.22
Ans.
Example 43. The rate at which the ice melts is proportional to the amount of ice at the instant.
Find the amount of ice left after 2 hours if half the quantity melts in 30 minutes.
Solution. Let m be the amount of ice at any time t.
dm
dm
km
k dt
dt
m
dm
...(1)
m k dt C log m = kt + C
At t = 0, m = M
log M = 0 + C
C = log M
On putting the value of C, (1) becomes,
log m = kt + log M
...(2)
m =M/2
when t =1/2 hour
M k
log M
log
2 2
M
k
2M 2
1 k
1
log
or k 2 log
2 2
2
log
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172
Differential Equations
1
On putting the value of k in (2), we have log m= 2 log t+ log M
...(3)
2
1
On putting t= 2 hours in (3), we have log m = 4 log + log M
2
4
m
m
1
M
1
log or
or m
log
M
M 16
16
2
1
After 2 hours, amount of ice left =
of the amount of ice at the beginning.
Ans.
16
CHEMICAL ACTION:
Example 44. Under certain conditions, cane sugar is converted into dextrose at a rate, which is
proportional to the amount unconverted at any time. If out of 75 grams of sugar at t = 0, 8 grams are
1
converted during the first 3 minutes, find the amount converted in 1 hours.
2
Solution. Let M be the amount of cane sugar initially, m be the amount of cane sugar
converted into dextrose.
Then according to problem,
dm
dm
K ( M – m ) or
Km KM
dt
dt
which is a linear differential equation.
I.F. = e Kdt = e k.t
Solution is
m e k.t = KMekt dt = Mekt + C
m = M + Ce–k.t
(i) At t = 0,
m= 0, M = 75
(1) becomes
m = 75 –75 e –k.t
0 = 75 + C
C = –75
(ii) At t = 30, m = 8
8 = 75 –75 e–30k
67 = 75e–30k
e–30k =
(iii) At t = 90, (2) becomes
67
75
...(1)
...(2)
...(3)
3
67
m = 75 –75 e– 90 k = 75 –75 from(3)
...(4)
75
3
67
300763
= 75 –
2 = 75 –
= 75 – 53.45 = 21.55
Ans.
5625
75
Example 45. Uranium disintegrates at a rate proportional to the amount present at any
instant. If m1 and m2 grams of uranium are present at time t 1 and t 2 respectively, show that half
life of uranium is
(t1 – t2 )log 2
m
log 1
m2
Solution. Let m be the amount of uranium at any time t.
dm
– km
dt
m2 dm
t2
m1
m1 m – k t1 dt log m k (t 2 – t1 )
...(1)
2
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Differential Equations
173
m
2
m
t2
dm
– k dt
0
m
m
log 2
log
– log m = – kt kt = log 2 = log 2 k
2
t
Substituting the value of k in (1), we get
Let the mass m reduce to
log
1.
2.
3.
m
in time t.
2
Also
m1 log 2
(t2 – t1 ), t (t 2 – t1 ) log 2
m2
t
m
log 1
m2
Proved.
EXERCISE 3.18
Radium decomposes at a rate proportional to the amount present. If 5% of the original amount
disappears in 50 years, how much will remain after 100 years?
Ans. 90.25%
If a thermometer is taken outdoors where the temperature is 0°C from a room in which the
temperature is 21°C and the reading drops to 10°C is 1 minute, how long after its removal will
the reading be 5°C ?
Ans. 2 minutes, 13 seconds.
In one dimensional steady state heat conduction for a hollow cylinder with constant thermal
conductivity k in the region a r b , the temperature T r at a distance r (a r b) is
given by
d dTr
r
0,
dr dr
with Tr = T1 where r = a and Tr = T2 where r = b. Use this to determine steady state temperature
T1 – T2
T2 log r1 – T1 log r2
distribution Tr in the cylinder in terms of r. Ans. Tr log(r / r ) log r
log(r1 / r2 )
1 2
MISCELLANEOUS QUESTION
Example 46. If the population of a country doubles in 50 years, in how many years will it
treble, assuming that the rate of increase is proportional to the number of inhabitants?
Solution. Let
t = time in years,
y = population after t years
P= original population (when t= 0).
The rate of increase of population is proportional to the population, so that
dy
= ky, where k is a constant. or k dt = dy/y
dt
Integrating,
kt = c + log y
... (1)
When
t = 0, y = P
When
t = 50, y = 2P
Substituting in (1),
Solving,
0 = c + log P, and 50k = c + log 2P
c = – log P
50k = – log P + log 2P = log 2 or
k=
1
log 2
50
The value of t when population has trebled is obtained by putting y = 3P in (1). We get
kt = c + log 3P = – log P + log 3P = log 3
t=
50
1
log 3 =
· log 3 years.
log 2
k
Ans.
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174
Differential Equations
3.18 LINEAR DIFFERENTIAL EQUATIONS OF SECOND ORDER WITH CONSTANT
COEFFICIENTS
The general form of the linear differential equation of second order is
d2y
dy
Qy R
dx
dx
where P and Q are constants and R is a function of x or constant.
2
P
Differential operator. Symbol D stands for the operation of differential i.e.,
dy
d2y
Dy
, D2 y
dx
dx 2
1
stands for the operation of integration.
D
1
stands for the operation of integration twice.
D2
d2y
dx
2
P
dy
Qy R can be written in the operator form.
dx
D2y + P Dy + Q y = R
(D2 + PD + Q) y = R
3.19 COMPLETE SOLUTION = COMPLEMENTARY FUNCTION + PARTICULAR
INTEGRAL
Let us consider a linear differential equation of the first order
dy
Py Q
dx
Its solution is
ye
Pdx
...(1)
(Q e
Pdx
) dx C
Pdx
Pdx
Pdx
y Ce e
(Qe ) dx
y = cu + v (say)
Pdx
where u e
and v e
Pdx
Q e
Pdx
...(2)
dx
Pdx
du
Pe
Pu
(i) Now differentiating u e Pdx w.r.t. x, we get
dx
du
d (cu )
Pu 0
P (cu ) 0
dx
dx
dy
Py 0
which shows that y = c.u is the solution of
dx
(ii) Differentiating v e
Pdx
(Qe
Pdx
dx with respect to x, we get
Pdx
Pdx
Pdx
Pdx
dv
Pe
(Qe ) dx e Qe
dx
dv
Pv Q
dx
dy
dv
Pv Q which shows that y = v is the solution of dx Py Q
dx
Solution of the differential equation (1) is (2) consisting of two parts i.e. cu and v.
cu is the solution of the differential equation whose R.H.S. is zero. cu is known as complementary
function. Second part of (2) is v free from any arbitrary constant and is known as particular integral.
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Differential Equations
175
Complete Solution = Complementary Function + Particular Integral.
y = C.F.+ P.I.
3.20 METHOD FOR FINDING THE COMPLEMENTARY FUNCTION
(1) In finding the complementary function, R.H.S. of the given equation is replaced by zero.
(2) Let y = C1 emx be the C.F. of
d2y
dx
2
P
2
dy
Qy 0
dx
...(1)
d y
dy
mx
and
(m2 + Pm + Q) = 0
2 in (1) then C1e
dx
dx
m2 + Pm + Q = 0. It is called Auxiliary equation.
(3) Solve the auxiliary equation :
Case I : Roots, Real and Different. If m1 and m2 are the roots, then the C.F. is
Putting the values of y,
y C1em1x C2 em2 x
Case II : Roots, Real and Equal. If both the roots are m1, m1 then the C.F. is
y (C1 C2 x) em1 x
Equation (1) can be written as
(D – m1)(D – m1)y = 0
Replacing
(D – m1)y = v in (2), we get
(D – m1)v = 0
dv
– m1v 0
dx
dv
m1dx
v
... (2)
... (3)
log v m1 x log c2
v c2em1 x
v c2em1x
( D –1) y c2 em1 x
This is the linear differential equation.
From (3)
Solution is
I.F. e – m1 dx e – m1 x
y.e – m1x (c2 em1 x ) (e – m1 x ) dx c1 c2 dx c1 c2 x c1
y (c2 x c1 )em1 x
C.F. (c1 c2 x) em1 x
d2 y
dy
8 15 y 0.
Example 47. Solve:
2
dx
dx
Solution. Given equation can be written as
(D2 – 8D + 15) y = 0
2
Here auxiliary equation is m – 8m + 15 = 0
(m – 3) (m – 5) = 0
Hence, the required solution is
y = C1 e3x + C2e5x
2
d y
dy
6 9y 0
Example 48. Solve:
dx
dx 2
Solution. Given equation can be written as
m = 3, 5
Ans.
(D2 – 6D + 9) y = 0
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176
Differential Equations
A.E. is m2 – 6m + 9 = 0
(m – 3)2 = 0
m = 3, 3
Hence, the required solution is
y = (C1 + C2x) e3x
2
d y
dy
4 5 y 0,
Example 49. Solve:
2
dx
dx
dy d 2 y
y = 2 and
when x = 0.
dx dx 2
Solution. Here the auxiliary equation is
m2 + 4m + 5 = 0
Its root are 2 i
The complementary function is
y = e–2x (A cos x + B sin x)
On putting y = 2 and x = 0 in (1), we get
2=A
On putting A = 2 in (1), we have
y = e–2x [2 cos x + B sin x]
On differentiating (2), we get
dy
e 2 x [2sin x B cos x ] 2e2 x [2 cos x B sin x ]
dx
= e–2x [(– 2B – 2) sin x + (B – 4) cos x]
d2y
dx 2
Ans.
...(1)
...(2)
e 2 x [(2 B 2) cos x ( B 4)sin x]
= e–2x
– 2e–2x [(– 2B – 2) sin x + (B – 4) cos x]
[( – 4B + 6) cos x + (3B + 8) sin x]
dy d 2 y
dx dx 2
e–2x [(–2B –2) sin x + (B – 4) cos x] = e–2x [(– 4B + 6) cos x + (3B + 8) sin x]
On putting x = 0, we get
B – 4 = – 4B + 6
B=2
(2) becomes,
y = e–2x [2 cos x + 2 sin x]
y = 2e–2x [sin x + cos x]
But
Ans.
Exercise 3.19
Solve the following equations :
1.
3.
d2 y
dx 2
d2y
2
dx
d2y
3
d 2 y dy
dy
30 y 0 Ans. y = C1e5x + C2e–6x
2 y 0 Ans. y = C1 ex + C2 e2x 2.
dx
dx 2 dx
8
dy
16 y 0
dx
Ans. y = (C1 + C2x) e4x
Ans. y C1 cos x C2 sin x
2 y 0
dx 2
5. ( D 2 2 D 2) y 0, y(0) 0, y(0) 1 (A.M.I.E.T.E., June 2006) Ans. y = e–x sin x
4.
6.
7.
d3 y
dx
3
d4y
dx 4
2
d2y
dx
32
2
4
d2y
dx 2
dy
8y 0
dx
Ans. y = C1e2x + C2cos 2x + C3sin 2x
256 0 (A.M.I.E.T.E., Dec. 2004) Ans. y= (C + x) cos 4x + (C3 + C4x) sin 4x
1
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Differential Equations
4
8.
9.
d y
4
dx
d4y
dx 4
3
–4
d y
dx
d2y
3
dx 2
177
2
8
d y
dx
2
8
dy
4y 0
dx
Ans. y = ex [(C1 + C2 x) cos x + (C3 + C4 x) sin x]
0, y (0) y (0) y (0) 0, y (0) 1
10. The equation for the bending of a strut is EI
d2y
dx 2
Ans. y = x – sin x
Py 0
P
x
EI
y
Ans.
P 1
sin
EI 2
a sin
1
If y = 0 when x = 0, and y = a when x , find y..
2
11.
12.
d3 y
dx
3
d3 y
dx
3
6
d2y
dx
d2y
dx
2
2
12
dy
8 y 0, y(0) = 0, and y(0) 0 and y(0) 2
dx
(A.M.I.E.T.E. Dec. 2008)
Ans. y = x2e–2x
4dy
1
4 y 0, y(0) = 0, y(0) 0, y(0) 5, Ans. y = e x cos 2 x sin 2 x
dx
2
13. ( D8 6 D6 – 32 D 2 ) y 0
(A.M.I.E.T.E., Summer 2005)
Ans. y = C1 + C2x + C3 e
2x
C4 e
2x
(iv )
14. Show that non-trivial solutions of the boundary value problem y
C5 cos 2 x C6 sin 2 x
– w4 y 0, y(0) 0 y(0),
nx
where Dn are constants. (AMIETE, Dec. 2005)
L
y(L) = 0, y ( L) 0 are y ( x ) Dn sin
n 1
15. Solve the initial value problem y 6 y 11y 6 y 0, y(0) = 0, y(0) 1, y(0) –1.
(A.M.I.E.T.E., Dec. 2006)
Ans. y = 2e–x – 3e–2x+ e–3x.
16. Let y1, y2 be two linearly independent solutions of the differential equation yy – ( y ) 2 0.
Then, c1 y1 + c2 y2, where c1, c2 are constants is a solution of this differential equation for
(a) c1 = c2 = 0 only.
(b) c1 = 0 or c2 = 0
(c) no value of c1, c2. (d) all real c1, c2
(A.M.I.E.T.E., Dec. 2004)
3.21 RULES TO FIND PARTICULAR INTEGRAL
1
1 ax
eax
e If f (a) = 0 then 1 e ax x 1 e ax
(i)
f (D)
f (a)
f (D)
f (a)
1
1
If f (a ) 0 then
eax x 2
eax
f ( D)
f (a)
1
(ii)
x n [ f ( D )]1 x n
Expand [f (D)]–1 and then operate.
f (D)
1
1
1
1
cos ax
cos ax
sin ax
sin ax and
(iii)
2
2
2
f (D )
f (a 2 )
f (D )
f (a )
1
1
sin ax x
sin ax
If f (– a2) = 0 then
f (D 2 )
f ( a 2 )
1
1
ax
ax
(iv) f ( D) e ( x ) e f ( D a) ( x )
1
( x ) e ax e ax ( x ) dx
(v)
Da
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178
3.22
Differential Equations
1
1 ax
e ax
e
f ( D)
f (a )
We know that, D.eax = a.eax,
D2eax = a2.eax,…............., Dn eax = an eax
ax
n
n–1
Let f (D) e = (D + K1D + … + Kn) eax = (an + K1an–1 +…+ Kn)eax = f (a) eax.
1
Operating both sides by
f ( D)
1
1
f ( D) e ax
f (a) e ax
f (D)
f (D)
1
e ax
f ( D)
If f (a) = 0, then the above rule fails.
e ax f (a)
Then
1
1
1
e ax x
e ax x
e ax
f ( D)
f ( D)
f (a)
1
1 ax
e ax
e
f ( D)
f (a)
1
1
e ax x .
e ax
f ( D)
f (a )
1
1
e ax = x 2
e ax
f ( D)
f (a )
Example 50. Solve the differential equation
If f (a ) 0 then
d2x
g
g
x L
l
dt 2 t
where g, l, L are constants subject to the conditions,
dx
0 at t = 0.
dt
d2x g
g
x L
l
dt 2 t
x = a,
Solution. We have,
m2
A.E. is
g
0
l
C.F. = C1 cos
g
t C2 sin
l
g
2 g
D x L
l
l
m i
g
l
g
t
l
g
g
g
1
L L
e0 t L
L
g l
g
g
l
l
2
D
D
0
l
l
l
General
solution
is
=
C.F.
+
P.I.
P.I. =
1
2
g
g
x C1 cos
t C2 sin
tL
l
l
[D = 0]
...(1)
g
g
dx
g
g
C1
sin
cos
t
t C2
l
dt
l
l
l
Put t = 0
and
dx
0
dt
0 C2
g
l
C2 = 0
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Differential Equations
179
g
tL
l
x C1 cos
(1) becomes
Put x = a and t = 0 in (2), we get
a = C1 + L
or
...(2)
C1 = a – L
g
On putting the value of C1 in (2), we get x (a L) cos
t L
l
d2 y
dy
9 y 5e3 x
dx
dx
Solution.
(D2 + 6D + 9)y = 5e3x
Auxiliary equation is m2 + 6m + 9 = 0
C.F. = (C1 + C2x) e–3x
Example 51. Solve :
2
6
1
P.I. =
D 2 6D 9
(m + 3)2 = 0
.5.e3 x 5
e3 x
(3)2 6(3) 9
y (C1 C2 x )e 3 x
The complete solution is
d2y
2
m = – 3, – 3,
5e3 x
36
5e3 x
36
dy
9 y 6e3 x 7e 2 x log 2
dx
dx
Solution.
(D2 – 6D + 9)y = 6e3x + 7e–2x – log 2
2
A.E. is (m – 6m + 9) = 0
(m – 3)2 = 0,
Example 52. Solve :
Ans.
Ans.
6
m = 3, 3
3x
C.F. (C1 C2 x) e
1
1
1
6e3 x 2
7 e 2 x 2
( log 2)
P.I. = 2
D 6D 9
D 6D 9
D 6D 9
1
1
1
6e3 x
7e 2 x log 2 2
e0 x
= x
2D 6
4 12 9
D 6D 9
2
= x
1
7
7
1
1
6 e3 x e2 x log 2 3x 2 e3 x e 2 x log 2
2
25
9
25
9
Complete solution is y (C1 C2 x ) e3 x 3 x 2 e3 x
7 2 x 1
e log 2
25
9
Ans.
EXERCISE 3.20
Solve the following differential equations:
ex
12
1. [D2 + 5D + 6] [y] = ex
Ans. C2 e 2 x C2 e 3 x
2
2. d y 3 dy 2 y e3 x
dx
dx 2
Ans. C 1e x C2 e 2 x
3. (D3 + 2D2 – D – 2) y = ex
Ans. C 1e x C 2 e – x C3 e – 2 x
d2y
dy
2 y sinh x
dx
dx
d2y
dy
4 5 y 2 cosh x
5.
2
dx
dx
4.
2
2
e3 x
2
(A.M.I.E.T.E. June 2010, 2007)
x x
e
6
x
x
Ans. e x [C1 cos x C2 sin x ] e e
10
2
1
e x
2 x
x
Ans. e (C1 cos x C2 sin x ) e
10
2
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180
Differential Equations
6. (D3 – 2D2 – 5D + 6) y = e3x
7.
d3 y
dx 3
d2y
d2y
4
Ans. C1e x C2 e – 2 x C3 e3 x
dy
4 y ex
dx
x
Ans. C1e C2 cos 2 x C3 sin 2 x
dx 2
dy
6 9 y e3 x
8.
2
dx
dx
3
d y
d2y
dy
3
3 y e x
9.
2
dx
dx
dx
2
d y dy
6 y e x cosh 2 x
10.
2
dx
dx
Ans. (C1 C2 x)e3 x
x ex
5
x2 3x
e
2
x3 x
e
6
1
1
xe3 x e x
10
8
2 x
Ans. (C1 C2 x C3 x )e
Ans. C1e3 x C2 e –2 x
x 2 x ex
2x
x
Ans. C1e (C2 C3 x)e e
9
4
Ans. (C1 + C2x + C3 x2) ex + 2e3x
11. (D – 2) (D + 1)2 y = e2x + ex
12. (D – 1)3 y = 16 e3x
3.23
x.e3 x
10
1
xn [ f (D)]1 xn .
f ( D)
Expand [f (D)]–1 by the Binomial theorem in ascending powers of D as far as the result of
operation on xn is zero.
d2y
a2 R
(l x)
Example 53. Solve the differential equation
p
dx 2
dy
where a, R, p and l are constants subject to the conditions y = 0,
0 at x = 0.
dx
d2y
a2
a2
2
2
2
Solution.
a
y
R
(
l
x
)
(
D
a
)
y
R (l x )
p
p
dx 2
A.E. is m2 + a2 = 0 m ia
C.F. = C1 cos ax + C2 sin ax
1
1
a2
a2 R 1 1
R D2
R (l x) =
(l x) 1 2 (l x)
P.I. = 2
p a2 D2
p a
D a2 p
1 2
a
R D2
R
= 1 2 (l x) (l x)
p a
p
y = C1 cos ax C2 sin ax
a2 y
R
(l x)
p
On putting y = 0, and x = 0 in (1), we get 0 = C1
...(1)
R
l
p
C1
Rl
p
dy
R
On differentiating (1), we get dx a C1 sin ax a C2 cos ax p
dy
On putting
0 and x = 0 in (2), we have
dx
R
R
0 = a C2
C2
p
a. p
On putting the values of C1 and C2 in (1), we get
R sin ax
R
R
R
l cos a.x l x
sin a.x (l x) y =
y = l cos a.x
p a
p
a. p
p
...(2)
Ans.
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Differential Equations
181
EXERCISE 3.21
Solve the following equations :
Ans. C1e x C2 e 4 x 1 (11 4 x)
8
Ans. (C1 + C2 x) e–x + x – 2
1. (D2 + 5D + 4) y = 3 – 2x
2.
d2y
dx 2
2
dy
yx
dx
3
x
4 [ A cos
23
23
1
x B sin
x ] [8x 2 28 x 13]
4
4
32
1
4. (D2 – 4D + 3) y = x3
Ans. C1e x C2 e3 x (9 x 3 36 x2 78 x 80).
27
d3 y d 2 y
dy
1 3
25
2
2 x
3x
2
1 x .
5. 5 3 2 6
Ans. A Be C e 2 x x x
dx
36
3
dx
dx
4
1 4
d y
x
x
4 y x4
6.
Ans. e (C1 cos x C2 sin x ) e (C3 cos x C4 sin x ) ( x 6)
4
4
dx
3. (2D2 + 3D + 4) y = x2 – 2x
7.
dy
dx 2
2p
Ans. e
dy
( p 2 q 2 ) y ecx p.q x 2
dx
eCx
2
4p x
6 p 2 2q 2
x
( p C )2 q 2 p 2 q 2
p 2 q 2 ( p 2 q 2 )2
Ans. C1 + C2x + C3 cos 2x + C4 sin 2x + 2x2 (x2 – 3)
Ans. e px [C1 cos qx C2 sin qx]
8. D2 (D2 + 4) y = 96 x2
1
1
sin ax
sin ax
2
f (D )
f (a2 )
3.24
pq
2
f (D )
cos ax
cos ax
f ( –a 2 )
D (sin ax) = a.cos ax, D2 (sin ax) = D (a cos ax) = – a2. sin ax
D4 (sin ax) = D2.D2 (sin ax) = D2 (– a2 sin ax) = (– a2)2 sin ax
(D2)n sin ax = (– a2)n sin ax
Hence, f (D2) sin ax = f ( – a2) sin ax
1
2
f (D )
f ( D 2 ) sin ax
sin ax f (– a 2 )
Similarly,
If
If f (a 2 ) 0 then,
1
2
cos ax
1
f (D2 )
1
2
f (D )
. f ( a 2 ).sin ax
sin ax
1
f ( D2 )
sin ax
sin ax
f (a 2 )
cos ax
f (D )
f ( a2 )
f (– a2) = 0 then above rule fails.
1
sin ax
sin ax x
2
f (D )
f ( a 2 )
1
2
f (D )
sin ax x 2
sin ax
f ( a 2 )
Example 54. Solve : (D2 + 4) y = cos 2x
(R.G.P.V., Bhopal June, 2008, A.M.I.E.T.E. Dec 2008)
Solution. (D2 + 4) y = cos 2x
Auxiliary equation is m2 + 4 = 0
m 2i ,
C.F. = A cos 2x + B sin 2x
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182
Differential Equations
1
x1
x
cos 2 x sin 2 x sin 2 x
2D
22
D 4
4
x
Complete solution is y A cos 2 x B sin 2 x sin 2 x
4
P.I. =
Example 55. Solve :
3
d3 y
3
dx
3
1
cos 2 x = x.
2
d2 y
3
dx
4
Ans.
dy
2 y ex cos x (U.P., II Semester, Summer 2006, 2001)
dx
2
Solution. Given (D – 3D + 4D – 2) y = ex + cos x
A.E. is m3 – 3m2 + 4m – 2 = 0
(m – 1) (m2 – 2m + 2) = 0, i.e., m = 1, 1 i
C.F. = C1ex + ex (C2 cos x + C3 sin x)
1
1
ex 3
cos x
P.I.
2
2
( D 1) ( D 2 D 2)
D 3D 4 D 2
1
1
ex
cos x
=
( D 1) (1 2 2)
(1) D 3(1) 4 D 2
1
1
1
3D 1
cos x
ex
cos x = x e x
=
1
( D 1)
3D 1
9D 2 1
x
= e .x
( 3sin x cos x)
1
x
= e .x (3sin x cos x)
9 1
10
Hence, complete solution is
y = C1e x e x (C2 cos x C3 sin x ) x e x
x
Example 56. Solve : ( D 3 1) y cos 2 e x
2
Solution.
A.E. is m3 + 1 = 0
Ans.
(Nagpur University, Summer 2004)
x
( D 3 1) y cos 2 e x
2
(m + 1) (m2 – m + 1) = 0
m
or
1
(3sin x cos x )
10
(1) 1 4 1 i 3
2
2
m=–1
1
3
m i
2
2
x
3
3
x C3 sin
x
C.F. = C1e x e 2 C2 cos
2
2
1 2 x x
1
1
x
cos 2 3 e x
[Put D = – 1]
cos 2 e = 3
D3 1
2
D 1
D 1
1 1 cos x
1
e x
= 3
2
2
D 1
3D 1
1 1
1 1
1
e0 x
cos x
e x = 1 1 1 cos x 1 e x
=
3
3
2 D 1
2 D 1
3(1) 2 1
2 2 D 1
4
P.I. =
=
1 1 ( sin x cos x) 1 x
1 1 ( D 1) cos x 1 x
e
e =
2 2
4
2 2 ( D 1) ( D 1) 4
( D 2 1)
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Differential Equations
183
1 1 sin x
1
1
1
cos x e x
2
2
2 2 ( D 1) 2 ( D 1)
4
1 sin x cos x 1 x
1
1
sin
x
1
1
1
e
cos x e x =
Put
D2 = – 1 =
2
4
4
4
2 2 (1 1) 2 (1 1)
4
1 1
x
P.I. = (cos x sin x e )
2 4
Hence, the complete solution is
=
x
3
3 1 1
y = C1e x e 2 C2 cos
x C3 sin
x (cos x sin x e x )
2
2
2 4
Ans.
EXERCISE 3.22
Solve the following differential equations :
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
d2y
1
sin 4 x
dx
10
2
d x
dx
1
2 3x sin t
Ans. e t [ A cos 2t B sin 2t ] (cos t sin t )
2
dt
4
dt
d2x
dx
dx
2 5 x sin 2t , given that when t = 0, x = 3 and
0
dt
dt
dt 2
53
55
1
Ans. e t cos 2t sin 2t (4cos 2t sin 2t )
34
17
17
d2y
dy
dy
7 6 y 2sin 3x, given that y = 1,
0 when x = 0.
dx
dx
dx 2
13
27
1
Ans. e6 x e x (7 cos 3 x sin 3 x )
75
25
75
(D3 + 1) y = 2cos2 x
1
x
3
3
1
Ans. C1e x e 2 C2 cos
x
C
sin
x 1 (8sin 2 x cos 2 x)
3
2
2
65
x
2
2
(D + a ) y = sin ax
(A.M.I.E.T.E., June 2009) (Ans. C1 cos ax C2 sin ax cos ax
2a
x2
4
2 2
4
(D + 2a D + a ) y = 8 cos ax
Ans. (C1 C2 x C3 cos ax C4 sin ax) 2 cos ax
a
d2y
dy
3 2 y sin 2 x
(A.M.I.E.T.E., Summer 2002)
dx
dx 2
1
Ans. C1e x C2 e 2 x (3cos 2 x sin 2 x )
20
d2y
1
y sin 3 x cos 2 x
Ans. C1 cos x C2 sin x [ sin 5 x 12 x cos x ]
dx 2
48
d2y
dy
2 3 y 2e 2 x 10sin 3x given that y (0) = 2 and y(0) 4
2
dx
dx
29 3 x 1 x 2 2 x 1
Ans.
e e e [cos 3 x 2sin 3 x ]
12
12
3
3
2
d y
dy
2
3 2 y 4cos x
(R.G.P.V., Bhopal, I Semester, June 2007)
dx
dx 2
Ans. C1e x C2 e 2 x e 2 x 1 (3sin 2 x cos 2 x ) 1
10
d2y
dy
2
2 3 y cos x x
dx
1
1
4
2
dx 2
Ans. e x [C1 cos 2 x C2 sin 2 x ] (cos x sin x ) ( x 2 x )
4
3
3
9
2
6 y sin 4 x
Ans. C1 cos 6 x C2 sin 6 x
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184
Differential Equations
1
13. ( D 3 3D 2 4 D 2) y e x cos x
Ans. (C1 C2 cos x C3 sin x ) e x
(3sin x cos x)
3
2
10
14. (D – 4D + 13 D) y = 1 + cos 2x
1
x
Ans. C1 e 2 x (C2 cos 3 x C3 sin 3 x )
(9 sin 2 x 8 cos 2 x )
290
13
15. (D2 – 4D + 4) y = e2x + x3 + cos 2x
1
1
1
Ans. (C1 C2 x ) e2 x x 2 e 2 x (2 x 3 6 x2 9 x 6) sin 2 x
2
8
8
d2y
2
n y h sin px
16.
( P n)
dx 2
where h, p and n are constants satisfying the conditions
b
dy
ph
h sin px
b for x = 0
y = a,
Ans. a cos nx
sin nx 2
2
2
dx
(n p 2 )
n n( n p )
17. y y 2 y 6sin 2 x 18cos 2 x, y (0) = 2, y(0) 2
3.25
Ans. – e–2x + 3 cos 2x
1
1
.e ax ( x ) e ax .
.( x )
f ( D)
f ( D a)
D[eax ( x)] eax D ( x) aeax ( x) eax ( D a ) ( x)
ax
2
ax
D 2 [eax ( x)] D[eax ( D a) ( x)] = e ( D aD) ( x) ae ( D a) ( x)
= eax ( D 2 2a D a 2 ) ( x) eax ( D a)2 ( x)
D n [eax ( x)] eax ( D a )n ( x)
Similarly,
f ( D)[eax ( x)] eax f ( D a) ( x)
e ax ( x)
1
.[eax f ( D a) ( x)]
f ( D)
Put f ( D a) ( x) X , so that ( x)
Substituting these values in (1), we get
e ax
...(1)
1
.X
f ( D a)
1
1
X
[e ax . X ]
f ( D a)
f ( D)
1
1
[eax . ( x )] e ax
( x)
f ( D)
f ( D a)
Example 57. Solve : (D2 – 4D + 4) y = x3 e2x
Solution. (D2 – 4D + 4) y = x3 e2x
A.E. is m2 – 4m + 4 = 0 (m – 2)2 = 0
m = 2, 2
C.F. = (C1 + C2 x) e2x
1
1
x 3 e2 x e 2 x
x3
P.I. = 2
2
D 4D 4
( D 2) 4( D 2) 4
4
5
1
1
x
2x
3
2x
2x x
x
e
.
e
= e
D 4
20
D2
2x
2x
The complete solution is y (C1 C 2 x) e e .
Example 58. Solve the differential equation :
d3 y
dx
3
7
d2y
dx
2
10
x5
20
Ans.
dy
e2 x sin x (AMIETE, June 2010, Nagpur University, Summer 2005)
dx
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Differential Equations
d3 y
Solution.
A.E. is
185
dx
3
7
d2y
dx
2
10
dy
e2 x sin x
dx
m3 – 7m2 + 10 m = 0
m (m – 2) (m – 5) = 0
D3y – 7D2y + 10 Dy = e2x sinx
(m – 2) (m2 – 5m) = 0
m = 0, 2, 5
C.F = C1e0 x C2e2 x C3e5 x
P.I. =
1
3
2
D 7 D 10 D
e2 x
e2 x
e2x
2x
e 2 x sin x e
1
3
( D 2) 7 ( D 2)2 10 ( D 2)
. sin x
1
.sin x
D 6 D 12 D 8 7 D 2 28 D 28 10 D 20
1
1
2x
sin x
sin x e
2
3
2
( 1 ) D (12 ) 6 D
D D 6D
3
2
1
1
sin x e2 x
sin x e2 x 1 7 D sin x e2 x 1 7 D sin x
D 1 6D
1 7D
1 49 D2
1 49 ( 12 )
1 7D
e2 x
sin x
(sin x 7 cos x)
50
50
Complete solution is
y = C.F. + P.I.
e2 x
2x
5x
y C1 C2 e C3 e
2
Example 59. Solve ( D 6 D 9) y
e2 x
(sin x 7 cos x )
50
Ans.
e 3 x
.
x3
(Nagpur University, Summer 2002, A.M.I.E.T.E., June 2009)
Solution A.E. is m2 + 6m + 9 = 0
(m + 3)2 = 0
m = – 3, – 3
C.F. = (C1 + C2x) e– 3x
P.I. =
1
e 3 x
D2 6 D 9 x3
3 x
= e
3 x
= e
e 3 x
1
1
( D 3) 2 6( D 3) 9 x3
1
2
1
3 x
= e
1
D 6 D 9 6 D 18 9 x
D2
2
1
3
x
1
3
x
1 x 3 x
x
e x
e
e
D 2
(2) (1)
2
2x
( x 3 )
e 3 x
Ans.
2x
Example 60. Solve (D2 + 5D + 6) y = e–2x sec2x (1 + 2 tan x) (A.M.I.E.T.E., Summer 2003)
Solution. (D2 + 5D + 6) y = e–2x sec2x (1 + 2 tan x)
Auxiliary Equation is m2 + 5m + 6 = 0
(m + 2) (m + 3) = 0 m = –2, and m = –3
Hence, complementry function (C.F.) = C1e–2x + C2e–3x
Hence, the solution is y = (C1 C2 x )e 3 x
P.I. =
1
2
D 5D 6
e 2 x sec 2 x (1 2 tan x ) e 2 x
1
2
( D 2) 5( D 2) 6
sec 2 x (1 2 tan x)
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186
Differential Equations
2 x
= e
1
sec 2 x(1 2 tan x )
2
D 4 D 4 5 D 10 6
2
1
2 tan x sec 2 x
2
2 x sec x
e
sec
x
(1
2
tan
x
)
e
2
D2 D
D2 D
D D
1
1
e 2 x
sec 2
2 tan x sec2 x
D ( D 1)
D ( D 1)
1
1 2
1
1
2
e 2 x
sec x
2 tan x sec x
D
D
1
D
D
1
1
1
1
2 x 1
2
2
e sec x
sec x 2 tan x sec2 x
2 tan x sec 2 x
D
D
1
D
D
1
e 2 x tan x e x ex .sec 2 x dx tan 2 x e x 2e x tan x sec 2 x dx
Now, e 2 x e x sec 2 x dx e x sec2 x e x .2 sec x sec x tan x . dx
2 x
e x sec 2 x 2 ex sec 2 x .tan x dx
2 x
e
x
2
x
x
2
x
x
2
P.I. = e tan x x .e sec x 2e e sec x tan x dx tan x 2e e sec x tan x dx
e2 x [tan x sec2 x tan 2 x] e2 x [tan x (sec2 x tan 2 x] e2 x (tan x 1)
Complete solution is
y C.F . P.I . C1e2 x C2 e3x e2 x (tan x 1)
Example 61. Solve the differential equation (D2 – 4D + 4) y = 8x2 e2x sin 2x
(U.P. II Semester, Summer 2008, Uttrakhand 2007, 2005, 2004; Nagpur University June 2008)
Solution. (D2 – 4D + 4) y = 8x2 e2x sin 2x
A.E. is (m2 – 4m + 4) = 0 (m – 2)2 = 0
m = 2, 2
2x
C.F. = (C1 + C2x) e
1
P.I. =
= 8e 2 x
2
D 4D 4
1
( D 2 2)2
8 x 2 e 2 x sin 2 x = 8
x 2 sin 2 x = 8e 2 x
1
D2
1
( D 2) 2
x 2 e 2 x sin 2 x
x 2 sin 2 x
2
1 2 ( cos 2 x)
cos 2 x
x sin 2 x cos 2 x
sin 2 x
2x 1 x
x
2
x
2
8
e
cos 2 x
=
D
2
4
8
D 2
2
4
x2 sin 2 x 2 x cos 2 x
sin 2 x x cos2 x 1 sin 2 x sin 2 x
= 8e2 x
(1)
4
8 2
2 2
4
8
2 2 2
2x
= 8e
= e2x [ – 2x2 sin 2x – 2x cos 2x + sin 2x – 2x cos 2x + sin 2x + sin 2x]
= e2x [– 2x2 sin 2x – 4x cos 2x + 3 sin 2x] = – e2x [4x cos 2x + (2x2 – 3) sin 2x]
Complete solution is, y = C.F. + P.I.
y = (C1 + C2x) e2x – e2x [4x cos 2x + (2x2 – 3) sin 2x]
Ans.
EXERCISE 3.23
Solve the following equations :
2x
3x
1. (D2 – 5D + 6) y = ex sin x Ans. y C1e C2 e
ex
(3cos x sin x )
10
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Differential Equations
2.
3.
4.
5.
6.
7.
8.
9.
187
e2 x
dy
2x
y C1e2 x C2 e5 x
(3cos x sin x)
10
y
e
sin
x
Ans.
10
dx
dx 2
d3 y
dy
xe x
x
2 x
x
2
4
y
e
cos
x
y
C
e
e
(
C
cos
x
C
sin
x
)
(3sin x cos x)
Ans.
1
2
3
dx
20
dx 3
(D2 – 4D + 3) y = 2xe3x + 3e3x cos 2x
1 3x 2
3 3x
x
3x
Ans. y C1e C2 e e ( x x ) e (sin 2 x cos 2 x )
2
8
d2 y
dy
e x
2 y 2
Ans. y = (C1 + C2 x) e–x – e–x log x
dx
dx 2
x
e3 x 2 12 x 62
2x
2 x
x
(D2 – 4) y = x2 e3x
Ans. y C1e C2 e
5
5
25
4x
e
5
[18 x 2 30 x 19] e3 x
(D2 – 3D + 2) y = 2x2 e4x + 5e3x
Ans. y C1e x C2 e 2 x
54
2
d2y
x
2
4 y x sinh x
Ans. y C1e 2 x C2 e 2 x sinh x cosh x
3
9
dx 2
k – ht
d2y
dy
2h (h2 p 2 ) y ke ht cos pt Ans. y e ht [ A cos pt B sin pt ]
te sin pt
2
dt
2p
dt
d2y
7
1
x n sin ax .
f ( D)
1
1
1
x n (cos ax i sin ax)
x n eiax eiax
xn
f ( D)
f ( D)
f ( D ia)
1
1
x n sin ax Imaginary part of e iax
xn
f ( D)
f ( D ia )
3.26 TO FIND THE VALUE OF
Now
1
1
x n cos ax Real part of e iax
xn
f ( D)
f ( D ia )
d2y
dy
2 y x sin x
dx
dx 2
Solution. Auxiliary equation is m2 – 2m + 1 = 0 or m = 1, 1
C.F. = (C1 + C2 x) ex
1
x sin x (eix = cos x + i sin x)
P.I. = 2
D 2D 1
1
1
x eix
x(cos x i sin x) = Imaginary part of 2
= Imaginary part of 2
D
2
D
1
D 2D 1
1
1
ix
x
= Imaginary part of eix
x = Imaginary part of e
2
2
D
2(1
i ) D 2i
( D i ) 2( D i ) 1
1
1
1 2
1
(1
i
)
D
D
= Imaginary part of eix
x
– 2i
2i
1
i
= Imaginary part of (cos x i sin x) 1 (1 i) D x = Imaginary part of (i cos x sin x) [ x 1 i]
2
2
1
1
1
P.I. = x cos x cos x sin x
2
2
2
1
x
Complete solution is y = (C1 C2 x)e ( x cos x cos x sin x)
Ans.
2
Example 62. Solve
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188
Differential Equations
EXERCISE 3.24
Solve the following differential equations :
1. (D2 + 4)y = 3x sin x
2.
3.
d2y
dx 2
d2y
2
Ans.C1 cos 2x + C2 sin 2x + x sin x –
1 3
cos 3x x sin 3x 5cos x
10 5
1
x x
2
[ x sin x cos x ] e (2 x 3x 9)
2
12
y x sin 3 x cos x
Ans. C1e x C2 e x
y x sin x e x x 2 e x
Ans. C1e x C2 e x
dx
4. (D4 + 2D2 + 1) y = x2 cos x
2
cos x
3
1 3
1
x sin x ( x 4 9 x 2 ) cos x
12
48
3.27 GENERAL METHOD OF FINDING THE PARTICULAR INTEGRAL OF ANY
FUNCTION (x)
1
( x ) y
P.I. =
...(1)
Da
1
( D a)
( x) ( D a ) y
or
Da
( x) ( D – a ) y
or
( x) Dy – ay
dy
ay ( x ) which is the linear differential equation.
dx
Ans. (C1 C2 x ) cos x (C3 C4 x ) sin x
a dx
a dx
ye
e ( x ) dx
Its solution is
or
yeax e ax ( x) dx
ax
ax
y = e e ( x) dx
1
( x ) = e ax e –ax ( x ) dx
D–a
2
d y
9 y sec 3x.
dx 2
Solution. Auxiliary equation is m2 9 0 or m 3i ,
C.F. = C1 cos 3x + C2 sin 3x
1 1
1
1
1
sec 3x
sec 3x
sec 3x =
P.I. = 2
6i D 3i D 3i
( D 3i ) ( D 3i )
D 9
1
1
1 1
sec 3 x
sec 3 x
=
6i D 3i
6i D 3i
Example 63. Solve
Now,
...(1)
1
ax
ax
1
sec 3 x e3ix e 3ix sec3 x dx
D a ( x) e e ( x) dx
D 3i
i
3ix cos 3 x i sin 3 x
dx e3ix (1 i tan 3 x) dx e3ix ( x log cos 3 x )
= e
cos 3 x
3
1
i
sec 3 x e 3ix ( x log cos 3 x)
D 3i
3
Putting these values in (1), we get
Changing i to – i, we have
P.I. =
=
1 3ix
i
i
e x log cos 3 x e 3ix x log cos3 x
6i
3
3
x 3ix e3ix log cos 3x xe3ix e 3ix
e
log cos3 x
6i
18
6i
18
x
1
x e3ix e 3ix 1 e3ix e 3ix
.
log cos 3x = sin 3 x cos 3 x log cos 3 x
3
9
3
2i
9
2
x
1
Hence, complete solution is y C1 cos 3 x C2 sin 3 x sin 3 x cos3 x log cos3 x
3
9
=
Ans.
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Differential Equations
189
EXERCISE 3.25
Solve the following differential equations :
d2y
a y sec ax
1.
(R.G.P.V., Bhopal April, 2010)
dx 2
x
1
Ans. C1 cos ax C2 sin ax sin ax 2 cos ax log cos ax
a
a
d2y
y cosec x
2.
Ans. C1 cos x + C2 sin x – x cos x + sin x log sin x
dx 2
1
3. (D2 + 4) y = tan 2x
Ans. C1 cos 2 x C2 sin 2 x cos 2 x log (sec 2 x tan 2 x)
4
d2y
y ( x cot x )
4.
(A.M.I.E. Winter 2002)
dx 2
Ans. C1 cos x + C2 sin x – x cos 2x – sin x log (cosec x – cot x)
3.28 CAUCHY EULER HOMOGENEOUS LINEAR EQUATIONS
an x n
dny
an 1 x n 1
d n 1 y
... (1)
.... a0 y ( x )
dx n
d xn 1
where a0, a1, a2, ... are constants, is called a homogeneous equation.
d
x ez ,
z log e x,
D
Put
dz
dy dy dz 1 dy
dy dy
dy
.
x
x
Dy
dx dz dx x dz
dx dz
dx
Again,
d2y
dx
2
d dy d 1 dy
1 dy 1 d 2 y dz
2
dx dx dx x dz
x dz x dz 2 dx
=
2
1 dy 1 d 2 y 1 1 d 2 y dy 1
2 d y
2
x
(D2 D) y
;
(
D
D
)
y
dx2
x2 dz x dz 2 x x2 dz 2 dz x2
x2
d2 y
x3
d3 y
D ( D 1) ( D 2) y
Similarly.
dx 2
dx 3
The substitution of these values in (1) reduces the given homogeneous equation to a differential
equation with constant coefficients.
or
D ( D 1) y
Example 64. Solve:
x2
Solution. We have,
x2
d2y
2
dx
d2y
dx
2
2x
dy
4 y x4
dx
2x
dy
4 y x4
dx
(A.M.I.E. Summer 2000)
... (1)
d
dy
d2y
, x
Dy , x 2
D ( D 1) y in (1), we get
dz
dx
dx 2
or
D ( D 1) y 2 Dy 4 y e4 z
( D 2 3D 4) y e4 z
Putting x e z ,
D
A.E. is
m2 3m 4 0
(m 4) (m 1) 0
C.F. = C1 e z C2 e4 z P.I. =
= z
1
2
D 3D 4
e4 z
m = –1, 4
[Rule Fails]
1
1
z e4 z
e4 z z
e4 z
2D 3
2 (4) 3
5
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190
Differential Equations
Thus, the complete solution is given by
C1
1
z e4 z
C2 x 4 x 4 log x
y
Ans.
x
5
5
2
dy
2 d y
x
y sin ( log x 2 ) (Nagpur University, Summer 2005)
Example 65. Solve x
2
dx
dx
2
dy
2 d y
x
y sin ( log x 2 )
Solution. We have, x
... (1)
2
dx
dx
y C1 e z C2 e 4 z
Let x = ez,
so that z = log x,
D
d
dz
(1) becomes
D (D – 1) y + Dy + y = sin (2z)
A.E. is m2 + 1 = 0
or
(D2 + 1) y = sin 2z
m=±i
C.F. = C1 cos z + C2 sin z
1
P.I =
2
D 1
1
1
sin 2 z sin 2 z
41
3
sin 2 z
1
sin 2 z
3
1
2
= C1 cos ( log x ) C 2 sin ( log x ) sin ( log x )
3
y = C.F. + P.I. = C1 cos z C2 sin z
2
Example 66. Solve: x
Solution. We have, x3
d3 y
dx
3
d y
3x 2
d2y
dx
2
d2y
2
dy
x 2 log x (Nagpur University, Summer 2003)
dx
x
dy
x3 log x
dx
dx
d
Let x = ez so that z = log x, D
dz
The equation becomes after substitution
[D (D – 1) (D – 2) + 3D (D – 1) + D ] y = z e3z
Auxiliary equation is m3 = 0
m = 0, 0, 0.
C.F. = C1 + C2 z + C3 z2 = C1 + C2 log x + C3 (log x)2
P.I. =
1
D
3
. z e3 z e3 z .
dx
3
3
3x
1
( D 3)3
Ans.
D3y = ze3z
.z
3
1 D
e3z
e3z
x3
(1 D) z
( z 1)
(log x 1)
1 z
27
3
27
27
27
x3
y C1 C2 log x C3 (log x)2
(log x 1)
Complete solution is
27
3z
= e
Ans.
3.29 LEGENDRE'S HOMOGENEOUS DIFFERENTIAL EQUATIONS
A linear differential equation of the form
(a bx)n
dn y
a1 (a bx)n 1
d n 1 y
... an y X
... (1)
dx n
dx n 1
where a, b, a1, a2, .... an are constants and X is a function of x, is called Legendre's linear
equation.
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Differential Equations
191
Equation (1) can be reduced to linear differential equation with constant coefficients by the
substitution.
a + bx = ez
z = log (a + bx)
b
dy
dy dy dz
.
so that
= a bx . dz
dx dz dx
where
dy
dy
b
b Dy ,
dx
dz
( a bx )
d2 y
Again
dx
2
d2y
(a + bx)
dy
= b Dy
dx
dy
b
d 2 y dz
.
.
(a bx )2 dz (a bx ) dz 2 dx
b2
dy
b
d2y
b
. 2 .
(a bx ) dz (a bx ) dz (a bx )
2
b2
dx 2
b2
=
(a bx)2
d
dz
d dy d b
dy
.
dx dx dx a bx dz
=
D
dy
d2y
b2 2
dz
dz
2
dy
2 d y
2
2
= b 2 b ( D y D y ) b 2 D ( D 1) y
dz
dz
(a bx) 2
d2y
dx 2
Similarly, (a bx)3
b 2 D ( D 1)
d3y
b3 D ( D 1) ( D 2) y
dx3
......................................................................
(a bx)n
dn y
n
b n D ( D 1) ( D 2) ..... ( D n 1) y
dx
Substituting these values in equation (1), we get a linear differential equation with constant
coefficients, which can be solved by the method given in the previous section.
2
Example 67. Solve (1 x )
2
Solution. We have, (1 x)
Put 1 + x = ez
or
d2y
2
dx
d2y
2
(1 x)
dy
y sin 2 {log (1 x )}
dx
(1 x)
dy
y sin 2 { log (1 x ) }
dx
dx
log (1 + x) = z
dy
d
d2y
Dy and (1 x)2
D ( D 1) y, where D
2
dx
dz
dx
Putting these values in the given differential equation, we get
D (D – 1) y + D y + y = sin 2z
or
(D2 – D + D + 1)y = sin 2z
2
(D + 1)y = sin 2z
A.E. is m2 + 1 = 0
m=±i
C.F. = A cos z + B sin z
1
1
1
sin 2 z
sin 2 z sin 2 z
P.I. = 2
4 1
3
D 1
(1 x)
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192
Differential Equations
Now, complete solution is y = C.F. + P.I.
1
sin 2 z
3
1
y A cos {log (1 x)} B sin {log (1 x )} sin 2 {log (1 x)}
3
y A cos z B sin z
Ans.
EXERCISE 3.26
Solve the following differential equations:
2
1. x
d2y
dx
4x
2
dy
42
6y 4
dx
x
2
3
Ans. C1 x C2 x
2. ( x 2 D 2 3x D 4) y 2 x 2
2
3. x
4.
d2 y
dx
d2y
dx
2
2
x
d2y
dx
2
dy
y log x (AMIETE, June 2010)
dx
1 dy 12 log x
x dx
x2
2x
Ans. (C1 + C2 log x) x + log x + 2
Ans. C1 C2 log x 2 (log x)3
C1
x2
2
C2 x 3
log x
x
3
3
(A.M.I.E. Winter 2001, Summer 2001)
Ans.
dy
2 y x 2 sin (5 log x)
dx
2
2
Ans. c1 x c2 x x log x
d2y
x4
Ans. (C1 C2 log x) x2 x2 (log x) 2
5. ( x 2 D 2 x D 3) y x2 log x
2
6. x
1
1
[ 15 cos (5 log x ) 23 sin (5 log x) ]
754
dy
sin (logx) 1
y log x
(AMIETE, Dec. 2009)
dx
x
dx
1 382
54
2 3
C2 x 2 3
cos log x
Ans. y C1 x
sin (log x) + 6 log x cos (log x) +
x 61
61
1
5 log x sin (log x)] +
6x
2
dy
2 d y
(1 x)
y 2 sin log (1 x)
8. (1 x)
dx
dx 2
Ans. y C1 cos log (1 x) C2 sin log (1 x) log (1 x ) cos log (1 x)
2
7. x
2
3x
2
9. Which of the basis of solutions are for the differential equation x
1 1
(c) , 2 ,
x x
(b) I n x, e x
(a) x, x I n x,
2
10. The general solution of x
d2y
2
d2y
dx
(d)
2
x
1
2
dy
y0
dx
ex , x In x
x
(A.M.I.E., Winter 2001) Ans. (a)
dy
9 y 0 is
dx
x) x3 (c) (C1 + C2x) x3
(d) (C1 + C2 ln x) e x3
(AMIETE, Dec. 2009) Ans. (b)
5x
dx
(b) (C1 + C2n
(a) (C1 + C2x) e3x
d2y
dy 1
into a linear differential equation with constant coefficients,
dx x
dx
the required substitution is
(a) x = sin t
(b) x = t2 + 1
(c) x = log t
(d) x = et
(AMIETE, June 2010) Ans. (d)
11. To transform x
2
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Differential Equations
193
3.30 METHOD OF VARIATION OF PARAMETERS
d2 y
dy
b
cy X
dx
dx 2
Let complementary function = Ay1 + By2, so that y1 and y2 satisfy
To find particular integral of a
d2y
dy
cy0
dx
dx
Let us assume particular integral y = uy1 + vy2,
a
b
2
... (1)
... (2)
... (3)
where u and v are unknown function of x.
Differentiation (3) w.r.t. x, we have y' = uy1' + vy2' + u'y1 + v'y2 assuming that u,v satisfy the
equation
u'y1 + v'y2 = 0
then
... (4)
y' = uy1' + vy2'
Differentiating (5) w.r.t.x, we have
... (5)
y'' = uy1'' + u'y1' + vy2'' + v'y2'
Substituting the values of y, y' and y'' in (1), we get
a(uy1'' + u'y1' + vy2'' + v' y2') + b (uy1' + vy2') + c (uy1 + vy2) = X
u(ay1'' + by1' + cy1) + v(ay2'' + by2' + cy2) + a (u' y1' + v' y2') = X
... (6)
y1 and y2 will satisfy equation (1)
and
ay1'' + by1' + cy1 = 0
... (7)
ay2'' + by2' + cy2 = 0
... (8)
Putting the values of expressions from (7) and (8) in (6), we get
u' y1' + v'y2' = X
... (9)
Solving (4) and (9), we get
u'
0
y2
X
y2 '
y
v' 1
y1
u
y
1
0
X
y1
y2
y1 '
y2 '
y1
y2
y1 '
y2 '
y2 X
y1 y2 ' y1 ' y2
y1 X
y1 y2 ' y1 ' y2
y2 X
dx
y2 ' y1 ' y2
v
y
1
y1 X
dx
y2 ' y1 ' y2
General solution = complementary function + particular integral.
Working Rule
Step 1. Find out the C.F. i.e., A y1 + B y2
Step 3. Find u and v by the formulae
y2 X
u
dx,
y1 y2 ' y1 ' y2
A.E. is
v
d2y
y cosec x.
dx 2
(D2 + 1) y = cosec x
Example 68. Solve
Solution.
Step 2. Particular integral = u y1 + v y2
m2 + 1 = 0
y1 X
dx
y1 y2 ' y1 ' y2
(Nagpur University, Summer 2005)
m=±i
C.F. = A cos x + B sin x
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Differential Equations
Here
y1 = cos x,
y2 = sin x
P.I. = y1 u + y2 v
y2 . cosec x dx
sin x . cosec x dx
y
'
.
y
cos
x
(cos
x ) ( sin x ) (sin x )
1
2
1
2
y . y'
u
where
sin x .
v
cos
2
1
dx
sin x
x sin 2 x
y1 . X dx
cos x . cosec x dx
cos x (cos x ) ( sin x ) (sin x )
1
2 y '1 y2
1
cos x .
sin x
cot x dx
dx
log sin x
2
2
1
cos x sin x
y . y'
dx x
P.I. = uy1 + vy2 = – x cos x + sin x (log sin x)
General solution = C.F. + P.I.
y = A cos x + B sin x – x cos x + sin x .(log sin x)
Ans.
Example 69. Apply the mehtod of variation of parameters to solve
d2y
dx 2
y tan x
(A. M. I. E. T. E., Dec. 2010, Winter 2001, Summer 2000)
d2y
y tan x
dx 2
(D2 + 1)y = tan x
Solution. We have,
m2 = – 1
A.E. is
C. F.
or
m=±i
y = A cos x + B sin x
Here,
y1 = cos x,
y2 = sin x
y1 . y'2 – y'1 . y2 = cos x (cos x) – (– sin x) sin x = cos2 x + sin2 x = 1
P. I. = u. y1 + v. y2 where
y2 tan x
dx
y1 . y '2 y '1 . y2
sin x tan x
dx
1
sin 2 x
dx
cos x
1 cos 2 x
dx
cos x
( cos x sec x ) dx sin x log ( sec x tan x )
y tan x
v
dx cos x tan x dx sin x dx cos x
1
y . y' y' . y
u
1
1
2
1
2
P. I. = u. y1 + v. y2
= [ sin x – log (sec x + tan x) ] cos x – cos x sin x = – cos x log ( sec x + tan x)
Complete solution is
y = A cos x + B sin x – cos x log (sec x + tan x)
Ans.
Example 70. Solve by method of variation of parameters:
d2y
dx
2
y
2
1 ex
(Uttarakhand, II Semester, June 2007, A.M.I.E.T.E., Summer 2001)
(Nagpur University, Summer 2001)
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Differential Equations
195
2
d y
Solution.
2
y
2
dx
1 ex
2
(m – 1) = 0
m2 = 1,
m=±1
A. E. is
C. F. = C1 ex + C2 e–x
P.I. = uy1 + vy2
y 1 = e x,
Here,
y2 = e–x
y1 . y'2 – y'1 . y2 = – ex . e–x – ex.e–x = – 2
and
u
y2 X
e x
2
dx
dx
y1. y '2 y '1 . y2
2 1 e x
e x
=
1 e
e
v
x
x
dx =
dx
e
e
dx
x
e x
x
1
x
(1 e )
1
e
x
dx
1 e
1
x
dx e x log (e x 1)
y1 X
ex 2
ex
dx
dx
dx log (1 ex )
=
y1 . y '2 y '1 . y2
2 1 e x
1 ex
P.I. = u. y1 v. y2 [ e x log (e x 1) ] e x e x log (1 e x )
= 1 e x log (e x 1) e x log (e x 1)
Complete solution = y C1ex C2e x 1 ex log (e x 1) e x log (e x 1)
Ans.
EXERCISE 3.27
Solve the following equations by variation of parameters method.
1.
2.
3.
4.
d2 y
2
dx
d2y
dx 2
d2y
2
dx
d2y
dx 2
4 y e2 x
y sin x
3
dy
2 y sin x
dx
y sec x tan x
5. y´´ 6 y´ 9 y
x 2 x e2 x
e
4
16
x
1
Ans. y C1 cos x C2 sin x cos x sin x
2
4
1
x
2x
(3 cos x sin x )
Ans. y C1e C2 e
10
2x
2 x
Ans. y C1e C2 e
e3 x
x2
Ans. y C1 cos x C2 sin x x cos x sin x log sec x sin x
(AMIETE, June 2010, 2009)
Ans. y = (C1 + x C2) e3x – e3x log x
3.31 SIMULTANEOUS DIFFERENTIAL EQUATIONS
If two or more dependent variables are functions of a single independent variable, the equations involving their deriavatives are called simultaneous equations, e.g.
dx
4y = t
dt
dy
2 x = et
dt
The method of solving these equations is based on the process of elimination, as we solve
algebraic simultaneous equations.
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196
Differential Equations
Example 71. The equations of motions of a particle are given by
dx
dy
y 0
x = 0
dt
dt
Find the path of the particle and show that it is a circle.
(R.G.P.V. Bhopal, Feb. 2006, U.P. II Semester summer 2009)
d
D in the equations, we have
dt
Dx + y = 0
–x + Dy = 0
On multiplying (1) by w and (2) by D, we get
Dx + 2y = 0
–Dx + D2y = 0
On adding (3) and (4), we obtain
2 y + D2 y = 0
(D2 + 2) y = 0
Now, we have to solve (5) to get the value of y.
A.E. is m2 + 2 = 0
m2 = – 2
m = ± i
y = A cos wt + B sin wt
Dy = – A sin t + B cos wt
On putting the value of Dy in (2), we get – x – A sin t + B cos t = 0
x = – A sin t + B cos t
x = – A sin t + B cos t
On squaring (6) and (7) and adding , we get
x2 + y2 = A2(cos2t + sin2t) + B2 (cos2t + sin2t)
x2 + y2 = A2 + B2
This is the equation of circle.
Example 72. Solve the following differential equation
Solution. On putting
dx
dy
= y + 1,
=x+1
dt
dt
Solution. Here, we have
Dx – y = 1
– x + Dy = 1
Multiplying (1) by D, we get
D2x – Dy = D.1
Adding (2) and (3), we get
(D2 –1) x = 1 + D.1
(D2 –1) x = 1 or (D2 – 1)x = e0
A.E. is
m2 – 1 = 0 m = ± 1
C.F. = c1et + c2 e– t
1
1 0
. e0
e 1
P.I. = 2
0
1
D 1
x = C.F. + P.I. = c1et + c2 e– t – 1
d
dx
(c1et c2 e t 1) 1
1
From (1), y =
y=
dt
dt
y = c1et – c2 e– t – 1
and
x = c1et + c2e–t – 1
...(1)
...(2)
...(3)
...(4)
...(5)
...(6)
...(7)
Proved.
(U.P. II Semester, 2009)
... (1)
...(2)
...(3)
[D. (1) = 0]
Ans.
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Differential Equations
197
Example 73.
where y (0) = 0, x (0) = 2
t
t
...(5)
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198
Differential Equations
Example 74. Solve:
dx
4x 3y = t
dt
dy
2 x 5 y = et
dt
Solution. Here, we have
(D + 4)x + 3y = t
[U.P. II Semester, 2006]
...(1)
d
...(2) D
dt
To eliminate y, operating (1) by (D + 5) and multiplying (2) by 3 then subtracting, we get
t
(D + 5) (D + 4) x + 3 (D + 5) y – 3 (2x) – 3 (D + 5) y = (D + 5)t – 3e
t
[(D + 4) (D + 5) – 6]x = (D + 5)t – 3e
t
(D2 + 9D + 14)x = 1 + 5t – 3e
Auxiliary equation is
m2 + 9m + 14 = 0 m = – 2, – 7
C.F. = c1e–2t + c2e–7t
2x + (D + 5)y = e
t
1
(1 5t 3et )
D 9 D 14
1
1
1
e0 t 5 2
t 3 2
et
= 2
D 9 D 14
D 9 D 14
D 9 D 14
1
1
1
e0t 5 .
t 3 2
et
= 2
9D D2
0 9(0) 14
1 9(1) 14
14 1
14 14
P.I. =
2
1
1
5 9D D2
1 t 1
5 9D D
1 t
=
1
1
t e
t e
14 14 14 14
8
14 14 14 14
8
1
5
9 1 t 5
31 1 t
t
e
=
t e
14 14 14 8
14
196 8
5
31 1 t
2 t
7 t
e
x = c1e c2 e t
14
196 8
dx
4x
3y = t
[From (1)]
dt
d 2 t
5
31 1 t
5
31 et
7 t
t–
e 4 c1e2t c2 e 7 t
t
= t c1e c2 e
dt
14
196 8
14
196 8
3y = t 2c1e2t 7c2 e 7t
y=
Hence,
1
2c1e 2t 3c2 e 7t
3
5 1 t
10
31 1 t
e 4c1e 2t 4c2 e 7t
t
e
14 8
7
49 2
3
27 5 t
– t
e
7
98 8
2 t
7 t
x = c1e c2 e
y=
5
31 1 t
t
e
14
196 8
2 2 t
1
9
5 t
c1e c2 e 7 t – t
e
3
7
98 24
Ans.
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... (5)
Differential Equations
199
dx
2y ,
dt
Solution. Here, we have
dx
dt
dy
dt
dz
dt
dx
From (1), we have
dt
Example 75. Solve
d2x
dt
2
d3x
dt
3
dy
dz
2z ,
2x
dt
dt
(Uttarakhand, II Semester, June 2007)
= 2y
Dx = 2y
...(1)
= 2z
Dy = 2z
...(2)
= 2x
Dz = 2x
...(3)
= 2y
=
2 dy
= 2 (2z) = 4z
dt
dy
Using (2), dt 2 z
dz
= 4 (2x) = 8x
dt
dz
Using (3), dt 2 x
= 4
3
d x
8x = 0
dt 3
m3 – 8 = 0
m– 2=0
(D3 – 8) x = 0
(m – 2) (m2 + 2m + 4) = 0
m=2
m2 + 2m + 4 = 0
m=
A.E. is
or
So the C.F. of x is
2 4 16
2 i 12
=
= 1 i 3
2
2
x = C1 e2t et ( A cos 3 t B sin 3 t )
...(4)
B
tan A
tan 1 B
A
[A = C2 cos , B = C2 sin ]
x = C1 e2t et [C2 cos cos 3 t C2 sin sin 3 t ]
x = C1 e2t et C2 cos ( 3 t ) = C1 e2t C2 et cos ( 3 t )
From (3), we have
dz
= 2x
dt
dz
2t
t
= 2C1 e 2C2 e cos ( 3 t )
dt
2t
z = C1 e 2C2
e t
1 3
e t
cos ( 3 t )
2
cos 3 t
3
1 3
4
2t
t
z = C1 e C2 e cos 3 t
3
2t
z = C1 e 2C2
[On putting the value of x]
e ax cos bx dx
cos (bx )
a2 b2
2
1 3
tan
1
3
2 4
...(5)
3 3
e ax
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200
Differential Equations
dy
= 2z
dx
From (2), we have
dy
4
2t
t
= 2C1 e 2 C2 e cos 3 t
dt
3
4
y = 2C1 e2 t dt 2C2 e t cos 3 t
dt
3
e x
4
y = C1 e 2t 2 C2
cos ( 3 t
)
3
1 3
e t
4 2
cos 3 t
y = C1 e 2t 2 C2
3
3
13
2
2t
t
y = C1 e C2 e cos 3 t
3
Relations (4), (5) and (6) are the required solutions.
Example 76. Solve the following simultaneous equations :
d2x
dt
2
Solution. We have,
3 x 4 y = 0,
d2x
dt 2
d2y
dt 2
x y =0
[On putting the value of z]
3 2
tan 1
1 3
...(6)
Ans.
(U.P. II Semester, Summer 2005)
3x 4 y = 0
d2y
x y =0
dt 2
(D2 – 3)x – 4y = 0
x+
(D2
...(1)
+ 1) y = 0
...(2)
Operating equation (2) by (D2 – 3), we get
(D2 – 3)x + (D2 – 3) (D2 + 1) y = 0
...(3)
Subtracting (3) from (1), we get
– 4y – (D2 – 3) (D2 + 1) y = 0 – 4y – (D4 – 2D2 – 3) y = 0
(D4 – 2D2 – 3 + 4) y = 0
(D2 – 1)2 y = 0
A.E. is
(D4 – 2D2 + 1) y = 0
(m2 – 1)2 = 0 (m2 – 1) = 0
y = (c1 + c2t)et + (c3 + c4t)e–t
m=±1
...(4)
From (2), we have
x = – (D2 + 1)y
= – D2y – y
= – D2 [(c1 + c2t) et + (c3 + c4t)e–t] – [(c1 + c2t)et + (c3 + c4t)e–t]
= – D [{(c1 + c2t )et + c2et} + {(c3 + c4t) (–e–t) + c4e–t}] – [(c1 + c2t)et + (c3 + c4t)e–t]
= – [(c1 + c2t) et + c2et + c2et + (c3 + c4t) (e–t) – c4e–t – c4e–t] – [(c1 + c2t) et + (c3 + c4t)e–t]
= – [(c1 + c2t + 2c2 + c1 + c2t) et + (c3 + c4t – 2c4 + c3 + c4 t) e–t]
= – [(2c1 + 2c2 + 2c2t)et + (2c3 – 2c4 + 2c4t)e–t]
Relations (4) and (5) are the required solutions.
...(5)
Ans.
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Differential Equations
201
EXERCISE 3.28
Solve the following simultaneous equations:
1.
2.
3.
dx
dy
2 x 3y 0 ,
3x 2 y 0
dt
dt
d2y
dt 2
x,
d 2x
Ans. x = c1 et – c2 e–5t , y = c1 et + c2 e–5t
y
dt 2
Ans. x = c1 et + c2 e– t + (c3 cos t + c4 sin t)
y = c1 et + c2 e– t – (c3 cos t + c4 sin t)
dx
dy
5x 2 y t ,
2x y 0
dt
dt
So that x = y = 0 when t = 0
1
1
(1 6t ) e 3t
(1 3t )
27
27
(AMIETE, June 2009, U.P., II Semester, June 2008)
Ans. x =
Ans. y =
4.
5.
dy 2
dx
t x
yt,
dt
dt
dx
2 y sin t 0
dt
Ans. x = c1 cos t + c2 sin t + t2 –1 ; y = –c1 sin t + c2 cos t + t
dy
2 x cos t 0
dt
4
7.
dy
x and
dx
8.
dx
y t,
dt
9.
10.
Ans. x = c1 cos 2t + c2 sin 2t – cos t ; y = c1 sin 2t – c2 cos 2t – sin t
dx dy
3 x sin t ;
dt dt
6.
2
2
(2 3t ) e 3t
(2 3t )
27
27
dx
y cos t
dt
dx
y e2t
dt
Ans. x = c1 e–t + c2 e–3t, y = c1 e–t + 3 c2 e–3t + cos t
t
t
Ans. x = C1 e C2 e
dy
2x 3 y 1
dx
dx
dy
y 0, t
x0
dt
dx
given x(1) = 1 and y(–1) = 0
t
t
Ans. x = c1 e
2 2t
1
e , y C1 et C2 e t e 2t
3
3
1
3
5
3
c2 e 2t t , y c1 et c2 e 2t t
2
2
4
2
Ans. x = c1 t + c2 t
–1,
y = c2 t
–1
– c1 t
dx
dy
y sin t ,
x cos t , given that x = 2, y = 0 when t = 0
(U.P., II Semester, 2004)
dt
dt
Ans. x = et + e–t, y = sin t – et + e–t
11.
(D – 1) x + Dy = 2t + 1; (2D + 1) x + 2 Dy = t
2
t2 4
Ans. x t , y t C
3
2 3
12.
dx 2
( x y ) 1,
dt t
(U.P., II Semester, Summer (C.O.) 2005)
dy 1
( x 5 y) t
dt t
13.
(D2 – 1)x + 8Dy = 16et and Dx + 3(D2 +1)y = 0
Ans.
y = c1 cos
x =
14.
4
3
Ans. x = At Bt
t
3
3 c1 sin
c2 sin
t
3
t
3
c3 cosh 3 t c4 sin h 3 t 2et
– 3 c2 cos
t
3
3 3 c3 sinh 3 t 3 3 c4 cosh 3 t 6et 3t.
dx 2
( x y ) 1,
dt t
dy 1
( x 5 y ) t.
dt t
2t 2
t
t 2 3y
4 1
3
, y = At Bt
2
15
20
15 10
(Q. Bank U.P.T.U. 2001)
(U.P. II Semester, 2005)
Ans. x = At
4
Bt 3
t 2 3t
1
2t 2
t
, y = At 4 Bt 3
15 10
2
15 20
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202
Differential Equations
3.32 EQUATION OF THE TYPE
dn y
dx n
= f ( x)
This type of exact differential equations are solved by successive integration.
d2 y
Example 77. Solve
= x 2 sin x.
dx 2
d2y
Solution. We have
...(1)
x 2 sin x
2
dx
Integrating the differential equation (1), we get
dy
= x2(– cos x) – (2x) (– sin x) + (2) (cos x) + c1
dx
dy
= x2 cos x + 2x sin x + 2 cos x + c1
dx
Integrating again, we have y = [(– x2) (sin x) – (– 2x) (– cos x) + (– 2) (– sin x)]
+ [(2x) (– cos x) – 2(– sin x)] + 2 sin x + c1x + c2
= – x2 sin x – 4x cos x + 6 sin x + c1x + c2
Ans.
d3 y
Example 78. Solve
x log x.
dx 3
d3 y
Solution. We have, 3 x log x
...(1)
dx
d2 y
x2
1
(log x ) ( x ) – x dx c1
Integrating the differential equation (1), we get
=
2
2
x
dx
d2 y
x2
x log x – x c1
=
...(2)
2
dx 2
Again integrating (2), we have,
x2
x3
1 x2
x2
dy
(log x) – dx –
c1 x c2
=
2
6
x 2
2
dx
dy
x3 x 2
x 2 x2
log x –
–
c1 x c2
=
dx
6
2
4
2
Again integrating (3), we obtain
...(3)
x4
x3
1 x3
x3 x 3
x2
(log x )
–
dx –
–
c1
c2 x c3
24
6
x 6
12 6
2
x 4 x3
x3 x3 x 3 c1 x 2
log x –
–
–
c2 x c3
y=
24 6
18 12 6
2
y=
y=
x 4 x3
11x3
x2
log x –
c1
c2 x c3
24 6
36
2
Ans.
EXERCISE 3.29
Solve the following differential equations:
1.
d5 y
2.
dx5
d2 y
3.
d4y
dx 2
dx 4
x6
c x 4 c2 x3 c3 x 2
1
c4 x c5
720
24
6
2
x
Ans. y
x ex
Ans. y = (x – 2) ex + c1 x + c2
x e – x – cos x
Ans. y
x5
x3
e – x – cos x c1
120
6
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Differential Equations
d y
4.
x2
5.
d3 y
dx
6.
203
2
dx
3
d3 y
dx 3
2
Ans. y –
log x
1
(log x) 2 log x – c1x c2
2
log x
Ans. y
1
[6 x3 log x – 11x3 c1x 2 c2 x c3 ]
36
sin 2 x
Ans. y
x3 sin 2 x c1 x 2
c2 x c3
12
16
2
3.33 EQUATION OF THE TYPE
Multiplying by 2
dn y
dx n
= f ( y)
dy d 2 y
dy
dy
2 f ( y)
, we get 2
dx dx 2
dx
dx
...(1)
2
dy
Integrating (1), we have = 2 f ( y ) dy c ( y ) (say)
dx
dy
dy
dx
= ( y )
dx
( y)
Example 79. Solve
Solution. We have
d2y
2
dx
d2 y
y , under the condition y 1.
dy
( y)
=x+c
dy
2
at x = 0
dx
3
y
dx 2
dy
dy d 2 y
dy
Multiplying (1) by 2 , we get 2
= 2 y
2
dx
dx dx
dx
2
4 3/ 2
dy
Integrating (2), we get y c1
3
dx
dy
2
On putting y = 1 and
, we have c1 = 0
dx
3
...(1)
...(2)
...(3)
2
2
dy
2 3/ 4
dy
4
– 3/ 4
dy
dx
Equation (3) becomes y 3/ 2 or
y or y
dx
3
3
3
dx
1
1/ 4
2
Again integrating y 2 x c2
...(4)
4y4
x c2
1
3
3
4
On putting x = 0, y = 1, we get c2 = 4
2
x4
(4) becomes
4y1/4 =
Ans.
3
2
d y
dy
sec2 y tan y under the condition y = 0 and
1 when x = 0.
Example 80. Solve
dx
dx 2
2
d2y
dy
2y tan y 2 dy d y = 2 sec 2 y tan y
Solution.
=
sec
2
2
dx
dx
dx dx
dy
dy d 2 y
2
2 dx dx2 = 2 sec y tan y dx
2
dy
dy
2
tan 2 y c1
= tan y c1 or
dx
dx
dy
1, we get c1 = 1
On putting y = 0, and
dx
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204
Differential Equations
dy
= tan 2 y 1 sec y
dx
cos y dy = dx
On integrating we get sin y = x + c
On putting y = 0, x = 0, we have c = 0
sin y = x y = sin–1 x
Now,
2
Example 81. Solve
d y
dx 2
dy
1 when x = 0.
dx
(U.P., II Semester, Summer 2003)
2( y 3 y ), under the condition y 0,
d2y
Solution.
dx
Integrating, we get
On putting y = 0 and
Ans.
2
= 2(y3 + y) or 2
dy d 2 y
dy
4( y 3 y )
dx dx 2
dx
2
y4 y2
dy
4
2
= 4 4 2 c1 y 2 y c1
dx
dy
= 1 in (1), we get 1 = c1
dx
...(1)
2
dy
Equation (1) becomes = y4 + 2y2 + 1 = (y2 + 1)2
dx
dy
dy
= y2 + 1 or
dx
dx
1 y2
Again integraiting, we get tan–1 y = x + c2
On putting y = 0 and x = 0 in (2), we have 0 = c2
Equation (2) is reduced to tan–1 y = x y = tan x
...(2)
Ans.
2
dx
Example 82. A motion is governed by d x 36 x –2 , given that at t = 0, x = 8 and
0, find
2
dt
dt
the displacement at any time t.
d2x
Solution. We have
dt
2
= 36x–2 2
d 2 x dx
–2 dx
= 2 36 x
2
dt
dt
dt
...(1)
2
dx
–1
Integrating (1), we hae – 72 x c1
dt
72
dx
c1 or c1 9
Putting x = 8 and
0 in (2), we get 0 = –
8
dt
2
2
72
– 72 9 x
dx
dx
–
9
or
(2) becomes
=
x
x
dt
dt
x dx
x dx
3
dt
c
=
= 3t + c2
x –8
2
x2 – 8 x
1 2x – 8 8
dx
= 3t + c2
2 x 2 – 8x
1
2
2x – 8
2
x – 8x
dx + 4
1
( x – 4) 2 – (4) 2
x 2 – 8 x 4 cos h –1
x–4
3t c2
4
...(2)
dx
( x – 8)
= 3
dt
x
dx 3t c2
...(3)
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Differential Equations
205
On putting x = 8 and t = 0 in (3), we get c2 = 0
x–4
(3) becomes
x 2 – 8 x 4 cos h –1
3t
4
EXERCISE 3.30
d2y
1.
y3
3.
sin3 y
4.
5.
dx
2
a
d2y
dx
2
2. e 2 y
Ans. c1 y2 = (c1 x + c2)2
Ans.
d2y
dx 2
1
Ans. c1 ey = cosh (c1 x + c2)
1 c1
Ans. sin [( x c2 ) (1 c1 )]
cos y 0
c1
cos y
a4
A particle is acted upon by a force x 3 per unit mass towards the origin where x is the distance
x
from the origin at time t. If it starts that it will arrive at the origin in time
.
4
In the case of a stretched elastic string which has one end fixed and a particle of mass m attached to the
d 2s
mg
(s – l )
e
dt
where l is the natural length of the string and e its elongwith due to a weight mg. Find s and v determining
the constants, so that s = s0 at the time t = 0 and v = 0 when t = 0.
g
g
2
2 1/ 2
Ans. v – e [( s0 – l ) – ( s – l ) ] , s – l ( s0 – l ) cos e . t
other end, the equation of motion is
3.34
2
–
EQUATIONS WHICH DO NOT CONTAIN 'y' DIRECTLY
The equation which do not contain y directly, can be written
dn y dn –1y
dy
f n , n – 1 , ..... ,
dx
dx
dx
x = 0
...(1)
d n –1 P
dy
d 2 y dP d 3 y d 2 P
P i.e., 2
, 3 2 etc. in (1), we get f n – 1 , ....... P, x = 0
dx
dx dx
dx
dx
dx
3
2
d y dy dy
Example 83. Solve
0
dx 2 dx dx
On substituting
d 2 y dP
dy
, equation (1) becomes
P and
dx
dx 2 dx
dP
dP
P P3 = 0 or
P(1 P 2 ) 0
dx
dx
dP
dP
2
– dx 1 – P dP = – dx
= – P (1 P ) or
2
2
dx
P (1 P )
P 1 P
1
P
On integrating, we have log P – log (1 P 2 ) = – x + c1 or log
– x c1
2
1 P2
Solution. On putting
P
1 P
2
= e– x + c1 or
P2(1 – a2 e–2x) = a2 e–2x P
1 P2
a 2 e –2 x P 2 (1 P 2 ) a 2 e – 2 x
a e– x
1 – a2 e– 2x
dy
a e– x
dx
1 – a 2 e– 2 x
a e– x
dx
1 – a 2 e– 2 x
y = – sin–1 (a e– x) + b
dy =
On integration, we get
P2
Ans.
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206
Differential Equations
1/ 2
dy 2
Example 84. Solve
1 –
dx 2 dx
d2y
(U.P. Second Sem., 2002)
1/ 2
dy 2
1 –
Solution. We have,
=
dx 2
dx
2
dP
dy
dP d y
Putting P
2 in (1), we get
=
dx
dx
dx dx
d2y
... (1)
1 – P2
dP
1 – P2
dx
On integrating, we have
sin–1 P = x + c P = sin (x + c)
dy
= sin (x + c)
dx
On integrating, we have
y = – cos (x + c) + c1
2
Example 85. Solve x
d y
dx 2
Ans.
2
dy
dy
x –
0
dx
dx
(U.P. II Semester, 2010)
d 2 y dP
dy
in the given equation, we get
P and
dx
dx 2 dx
1 dP 1 1
dP
–
–1
x
x P2 – P = 0 2
dx
P dx P x
1
1 dP dz
Again putting z so that – 2
P
P dx dx
– dz z
dz z
Equation (1) becomes
– –1
1
dx
x
dx x
Solution. On putting
...(1)
1
I.F. = e
x dx
elog x x
x2
1
x2
c1 or
x
c1
2
P
2
2x
dy
2x
2x
x x 2 2c1
2
dy 2
dx
P = 2
dx x 2c1
P
2
x 2c1
x 2c1
On integrating, we have
y = log (x2 + 2c1) + c2
Hence, solution is
zx=
x dx c1 or z x
Ans.
EXERCISE 3.31
Solving the following differential equations:
1.
2.
3.
(1 x 2 )
d2y
dx
2
x
dy
ax 0
dx
2
d2y
dy
1 0
2
dx
dx
4
3
d y
d y
– cot x 3 0
4
dx
dx
(1 x 2 )
Ans. y c2 – ax c1 log [ x (1 x 2 ) ]
Ans. y –
x 1 k2
log (1 kx ) a
k
k2
Ans. y = c1 cos x + c2x2 + c3x + c4
2
4.
5.
d3y d2 y d2 y
2
–a
dx3 dx 2 dx 2
d 2 y dy
2
e x / 2 x 2 – x3
dx
dx
2x
2
2 5/ 2
c2 x c3
Ans. 15 c1 y 4(c1 x a )
–x
Ans. y e
2
/2
c1
x2
c2
2
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Differential Equations
3.35
207
EQUATIONS THAT DO NOT CONTAIN 'x' DIRECTLY
The equations that do not contain x directly are of the form
dn y d n –1y
dy
f n , n – 1 , ...... , y 0
dx dx
dx
dy
d 2 y dP dP dy dP
On substituting
P, 2
P in the equation (1), we get
dx
dx dx dx dy
dx
...(1)
dP n – 1
n – 1 ,..... P, y = 0
dy
Equation (2) is solved for P. Let
...(2)
P = f1 ( y )
Example 86. Solve y
dy
dy
f1 ( y ) or
dx
dx
f1 ( y )
dy
f1 ( y)
=x+c
2
d2y
dy
dy
2
dx
dx
dx
...(1)
dy
d 2 y dP dP dy
dP
P, 2
P
in equation (1)
dx
dx dy dx
dy
dx
dP
yP
P 2 = P y dP 1 – P
dy
dy
dp
dy
– log (1 – P ) log y log c1
=
1– P
y
1
dy c1 y – 1
1
or
= c1 y P 1 –
c1 y
dx
c1 y
1– P
c1 y
1
dy = dx 1
dy dx
c1 y – 1
c1 y – 1
1
y log (c1 y – 1) = x + c1
c1
Solution. Put
Example 87. Solve y
Solution. Put
Ans.
2
d2y
dy
y2
dx 2 dx
...(1)
dy
d 2 y dP dP dy
dP
P, 2
P
in (1)
dx
dx dy dx
dy
dx
dP
dP P 2
P 2 = y2 or P
y
dy
dy
y
dP dz
1 dz z
dz 2 z
Put P2 = z or 2P
in (2),
y or
2y
dy dy
2 dy y
dy
y
yP
...(2)
2
y dy
Hence, the solution is
2
e2 log y elog y y 2
I.F. = e
z y2 = 2 y. ( y 2 ) dy c
P2 y2 =
y4
c
2
2 P2 y2 = y4 + k or
2yP
y4 k
[Put 2c = k]
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208
Differential Equations
2y
1
dt
2
2
t k
sin h –1
dy
=
dx
y2
k
y 4 k or 2
y dy
y4 k
dx
= dx [Put y2 = t, 2y dy = dt]
1
2
sin h –1
t
k
=x+c
2 x c or y 2 k sin h ( 2 x c)
=
Ans.
EXERCISE 3.32
Solve the following differential equations:
1.
2
d2y
dy
1 Ans. y2 = x2 + ax + b
2
dx
dx
3. 2 y
d2y
2
dy
dy
– 2 0 Ans. cy + 2 = d ecxa
2
dx
dx
dx
2
d2y
3
d 2 y dy dy
dy
2
2 (x + b) 4. y
–
3
–
4
y
0
0 Ans. y = a – sin–1 (b e–x)
Ans.
y
=
a
sec
dx
dx 2
dx 2 dx dx
5.
y
6.
y
3.36
2. y
d2y
2
d2y
2
dy
2
dx
dx
dy
dy
1 – dx cos y y dx sin y
Ans. x = c1 + c2 log y + sin y
dy
– y 2 log y
dx 2 dx
Ans. log y = b. ex + a e– x
EQUATION WHOSE ONE SOLUTION IS KNOWN
If y = u is given solution belonging to the complementary function of the differential equation.
Let the other solution be y = v. Then y = u. v is complete solution of the differential equation.
d2y
dy
Qy R ....(1), be the differential equation and u is the solution included in
dx
dx
the complementary function of (1)
Let
2
P
d 2u
dx 2
P
du
Qu = 0
dx
y = u. v
dy
du
dv
u
= v
dx
dx
dx
d2y
dx 2
= v
d 2u
2
...(2)
dv du
d 2v
u 2
dx dx
dx
dx 2
dy d 2 y
Substituting the values of y, , 2 in (1), we get
dx dx
v
d 2u
dx 2
2
dv du
d 2v
dv
du
u 2 Pv
u Q u. v R
dx dx
dx
dx
dx
On arranging
d 2u
d 2v
du
dv
du dv
Qu + u
P 2 R
v 2 P
2
dx
dx
dx dx
dx
dx
The first bracket is zero by virtue of relation (2), and the remaing is divided by u.
d 2v
2
dx
d 2v
dx 2
P
R
dv 2 du dv
=
u
dx u dx dx
2 du dv
R
P
dx = u
u
dx
...(3)
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Differential Equations
209
dv
d 2 v dz
= z, so that
dx
dx 2 dx
dz
2 du
R
Equation (3) becomes dx P u dx z u
This is the linear differential equation of first order and can be solved (z can be found), which
will contain one constant.
dv
On integration z , we can get v.
dx
Having found v, the solution is y = uv.
Note: Rule to find out the integral belonging to the complementary function
Let
Rule
Condition
u
1
1+P+Q =0
ex
2
1–P+Q =0
e–x
4
P Q
=0
a a2
P + Qx = 0
5
2 + 2Px + Qx2 = 0
x2
6
n (n – 1) + Pnx + Qx2 = 0
xn
1
3
eax
x
Example 88. Solve y – 4xy + (4x2 – 2)y = 0 given that y= ex2 is an integral included in the
complementary function.
(U.P., II Semester, 2004)
Solution. y – 4xy + (4x2 – 2)y = 0
...(1)
On putting y = v.ex2 in (1), the reduced equation as in the article 3.36
d 2v
2 du dv
P
=0
u dx dx
dx
[P = – 4x, Q = 4x2 – 2, R = 0]
2
d 2v
2 dv
2
– 4 x 2 (2 x e x )
=0
x
dx
e
dx
2
d 2v
dx
2
[– 4 x 4 x]
dv
=0
dx
y = uv
d 2v
dx
2
0
dv
c, v c1 x c2
dx
2
[u e x ]
2
x
y = e (c1 x c2 )
Ans.
2
dy
( x 1) y 0
dx
dx
given that y = ex is an integral included in the complementary function.
Example 89. Solve x
Solution. x
d2y
2
d y
2
– (2 x – 1)
– (2 x – 1)
dy
( x – 1) y 0
dx
dx
2 x – 1 dy x – 1
–
y =0
[1 + P + Q = 0]
2
x dx
x
dx
By putting y = vex in (1),we get the reduced equation as in the article 3.36.
d2y
d 2v
2 du dv
P
=0
u
dx dx
dx
2
...(1)
...(2)
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210
Differential Equations
dv
z in (2), we get dz – 2 x – 1 2 e x z = 0
dx
dx
x
e x
dz – 2 x 1 2 x
dz z
z =0
0
dx
x
dx x
dx
dz
log z – log x log c1
= –
x
z
c1
dv c1
dx
or
or dv c1
v c1 log x c2
z=
x
dx x
x
x
y = u. v = e (c1 log x + c2)
Putting u = ex and
Example 90. Solve x 2
2
Solution. x
d2y
dx
2
2
d y
– 2 x [1 x ]
dx 2
– 2 x (1 x)
Ans.
dy
2(1 x ) y x 3
dx
dy
2(1 x ) y x3
dx
2
2 x (1 x ) dy 2(1 x ) y
=x
dx
dx
x2
x2
2 x (1 x) 2(1 x)
x0
Here
P + Qx = –
x2
x2
Hence y = x is a solution of the C.F. and the other solution is v.
Putting y = vx in (1), we get the reduced equation as in article 3.36
d y
2
–
...(1)
d 2v
x
2 du du
P
=
u
u
dx
dx
dx
2
d 2v
x
– 2 x (1 x) 2 dv
(1)
=
2
x dx
x
dx
x
dz
d 2v
dv
– 2z 1
–2
=1
2
dx
dx
dx
2
dv
dx z
which is a linear differential equation of first order and I.F. = e
Its solution is z e–2x =
e
–2 x
– 2 dx e – 2 x
dx c1
e –2 x
–1
c1 or z
c1 e 2 x
–2
2
dv
– x c1 2 x
1
1
2x
2x
e c2
= – c1 e or dv – c1 e dx
v=
dx
2
2
2
2
– x c1 2 x
e c2
y = uv x
Ans.
2
2
Example 91. Verify that y = e2 x is a solution of (2x + 1) y´´ – 4 (x + 1) y´ + 4y = 0. Hence
find the general solution.
z e–2x =
Solution. We have (2 x 1)
d2 y
2
4 ( x 1)
dy
4y 0
dx
dx
y = e2x, y´ = 2e2x, y´´ = 4e2x
Substiting the values of y, y´ and y´´ in (1), we get
(2x + 1) 4e2x – 4 (x + 1) 2e2x + 4e2x = 0
or
[8x + 4 – 8x – 8 + 4] e2x = 0
... (1)
0=0
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Differential Equations
211
y1 = e2x is a solution
4 ( x 1)
4
y´
y =0
Equation (1) in the standard form is y´´
(2 x 1)
(2 x 1)
4 ( x 1)
1
.
So
P (x)
Then (x) 2 e P dx
(2 x 1)
y
Thus
1
2
4 ( x 1)
4x 2
dx
P dx
dx
(2 x 1)
2x 1 2x 1
= 2x + 1n (2x + 1)
Now
Then
(x)
Integrating by parts
1
(e 2 x ) 2
2x 1
e
2x
e2x
+ 1n (2x + 1)
Now
e2 x
=
v (x) =
( e2 x ) 2
. (2 x 1)
(x) dx =
2x 1
e2 x
dx
e 2 x
e 2 x
2.
2
4
The required second solution
1
1 1
2x 2 x 1
. 2x
y2 (x) = y1 (x) v (x) = e
= – x – 1 = – (x + 1)
2
2 e 2 x
e
Then the general solution is
y (x) = c1 y1 (x) + c2y2 (x) = c1e2x – c2 (x + 1)
Ans.
2
2
3
x
Example 92. Solve x y – (x + 2x)y + (x + 2)y = x e given that y = x is a solution.
v (x) = (2x + 1)
Solution. x2 y – (x2 + 2x)y + (x + 2)y = x3 ex
y –
x2 2x
2
y
x2
2
y x ex
x
x
On putting y = vx in (1), the reduced equation as in the article 3.36.
2 du dv
R
d 2 v x 2 2 x 2 dv
xe x
P
–
(1)
=
=
u dx dx
u
x dx
x
dx 2
dx 2
x2
2
dz
d v dv
x
– z ex
–
=e
2
dx
dx
dx
which is a linear differential equation
– dx
–x
I.F. = e e
z e–x = e x . e – x dx c
...(1)
d 2v
dv
z
dx
dv
= e x. x + c e x
dx
v = x.ex – ex + c ex + c1 v = (x – 1) ex + c ex + c1
y = vx = (x2 – x + cx) ex + c1x
z e–x = x + c or z = ex. x + c ex
Ans.
2
dy
2 y ( x 1)e x
dx
d y 2 x 5 dy
2y
( x 1) e x
–
Solution.
x 2 dx x 2
x2
dx 2
P Q
1 2 = 0, Choosing a = 2
Here
a a
Example 93. Solve ( x 2)
d y
dx 2
– (2 x 5)
...(1)
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212
Differential Equations
2x 5
2
=0
2x 4 4 x 8
Hence y = e2x is a part of C.F.
Putting y = e2xv in (1), the reduced equation as in the article 3.36.
1–
d 2v
d 2v
2 du
P
2
u
dx
dx
2x 5
2
2 x 2e 2 x
x2 e
–
2
dx
d 2v
( x 1) e x
dv
=
u ( x 2)
dx
( x 1) e x
dv
= 2x
dx
e ( x 2)
x 1 –x
2x 5
dv
–
4
= x 2e
dx
x2
dx
x 1 –x
dz 2 x 3
e
z =
x
2
dx x 2
which is a linear differential equation,
2
I.F. = e
2x 3
dx
x2
e
dv
z
dx
1
2 – x 2 dx
e 2 x – log( x 2)
e2 x x 1 – x
x 2 x 2 e dx c
e x ( x 1)
1
x 1
dx
c
e
–
dx c =
=
2
( x 2)2
x 2 ( x 2)
Its solution is
z.
e2 x
x2
e2 x
=
x2
e x dx
e x dx
x 2 – ( x 2)2 c
ex
e x dx
e x dx
ex
–
c
c
2
2
x2
x2
( x 2)
( x 2)
dv
z = e– x + c (x + 2)e–2x
= e–x + c(x + 2)e–2x
dx
( x 2) e – 2 x e – 2 x
–x
–x
–2 x
–
e
c
–
c1
v = e dx c ( x 2)e dx c1 =
–2
4
ce –2 x
[2 x 5] c1
= – e– x
4
y = u.v
c e –2 x
2x
–x
e
–
e
(2 x 5) c1 y = – e x c (2 x 5) c1 e 2 x
y=
4
4
EXERCISE 3.33
=
Ans.
Solve the following differential equations:
d2y
dy
1.
(3 – x) 2 – (9 – 4 x )
(6 – 3x ) y 0, given y = ex is a solution.
dx
dx
c1 3 x
3
2
x
Ans. y e (4 x – 42 x 150 x – 183) c2e
8
d 2 y dy
2.
x 2 –
(1 – x ) y x 2 e – x given y = ex is an integral included in C.F..
dx
dx
1
x
–x
2
–x
Ans. y c2 e c1 (2 x 1) e – (2 x 2 x 1)e
4
d2y
dy
3.
(1 – x 2 ) 2 x
– y x (1 – x 2 )3 / 2 , given y = x is part of C.F..
dx
dx
x
2 3/ 2
– c1 [ (1 – x 2 ) x sin –1 x] c2 x.
Ans. y – (1 – x )
9
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Differential Equations
4.
5.
6.
7.
8.
sin 2 x
d2y
213
2
d y
dx 2
2 y , given that y = cot x is a solution
Ans. cy = 1 + (c1 – x) cot x
x3
dy
1
–x
xy x, given y = x is a part of C.F..
Ans. y 1 c1x 2 e 3 dx c2 x
2
dx
dx
x
d2y
dy
( x sin x cos x ) 2 – x cos x
y cos x 0 given y = x is solution.
dx
dx
Ans. y = c2x – c1 cos x
2
1 c1
1
dy
2d y
x
x
– y 0, given that y x is one integral.
Ans. y c2 x
2
x x
x
dx
dx
2
d
y
dy
x 2 2 2 x – 12 y x3 log x
(U.P., II Semester 2004)
dx
dx
2
dv
z]
dx
3
3 c2 x
log x (7 log x – 2)
Ans. y c1x 4
x 98
NORMAL FORM (REMOVAL OF FIRST DERIVATIVE)
[Hint. (n (n – 1) + pnx + Qx2 = 0), n = 3, satisfies this equation. Put y v x 3 ,
3.37
d2y
dy
Qy R
dx
dx
Put y = uv where v is not an integral solution of C.F.
du
du
dy
u
= v
dx
dx
dx
Consider the differential equation
d2 y
= u
2
P
d2 v
2
du dv
d 2u
v 2
dx dx
dx
d x2
d x2
dy d 2 y
On putting the values of y, , 2 in (1) we get
dx dx
d 2v
dv du
d 2u
du
du
v 2 Pu
v Q.uv = R
u 2 2
dx dx
dx
dx
dx
dx
2
2
d v
d u du
dv
dv
Q.v = R
v 2
Pv 2 u 2 P
dx
dx
dx
dx
dx
R
d 2 u du
2 dv u d 2 v
dv
P
Q.v =
P
u dx v dx 2
dx
dx 2 dx
v
Here in the last bracket on L.H.S. is not zero y = v is not a part of C.F.
Here we shall remove the first derivative.
P
...(1)
...(2)
2 dv
dv
1
–1
= 0 or
– P dx or log v
P dx
v dx
v
2
2
1
– P dx
v= e 2
2
In (2) we have to find out the value of the last bracket i.e., d v P dv Qv
dx
dx 2
1
dv
v e – 1/ 2 pdx
P – 2 P dx
1
= – e
– Pv
dx
2
2
2
1 dP
P dv
1 dP
P 1
1 dP
1
d v
–
v–
–
v – – Pv –
v P2 v
2 =
2 dx
2 dx
2 dx
2 2
2 dx
4
dx
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214
Differential Equations
d 2v
1 dP 1 2
1 dP
1
dv
1
– P
v P 2 v P – Pv Qv = v Q –
Qv = –
2 dx 4
2 dx
4
dx
dx
2
Equation (1) is transformed as
d 2 u u
1 dP P 2 R
v
Q
–
–
2 dx
4 = v
dx 2 v
1
d 2u
1 dP P 2
P dx
u
Q
–
–
= Re2
2
2
dx
4
dx
1
2
P dx
1 dP P 2
d u
R
2
Q
Q
–
–
Q
u
R
R
e
or
=
R
where
,
1
1
1
2
1
2
dx
4
v
dx
2
P
y = uv
and
ve
–
1
P dx
2
Ans.
d 2 dy
cos x cos 2 x. y 0
dx
dx
d 2 dy
2
Solution. We have,
cos x cos x. y 0
dx
dx
Example 94. Solve
Here,
d2y
dx 2
dy
d2 y
dy
(cos 2 x) y = 0
– 2 tan x. y = 0
2
dx
dx
dx
P = – 2 tan x, Q = 1, R = 0
cos 2 x – 2 cos x sin x
1 dP P 2
1
4 tan 2 x
–
= 1 – (– 2 sec 2 x) –
2 dx
4
2
4
2
= 1 + sec x – tan2x = 1 + 1 = 2
Q1 = Q –
1
R1 = R e 2 P dx 0
v=
e
–
1
P dx
2
e
–
1
(– 2 tan x ) dx
2
e
tan x dx
elog sec x sec x
Normal equation is
d 2u
dx 2
d 2u
dx 2
Q1u = R1
2u = 0
or
(D2 + 2) u = 0 D i 2
u = c1 cos 2 x c 2 sin 2 x
y = u.v
= [c1 cos 2 x c2 sin 2 x] sec x
dy
– 2( x 2 x)
( x 2 2 x 2) y 0
dx
dx 2
d 2 y 2( x 2 x ) dy x 2 2 x 2
Solution. We have,
–
y 0
dx
dx 2
x2
x2
2
Example 95. Solve x
Ans.
2
d y
...(1)
1
x2 2 x 2
, R0
Here p = – 2 1 , Q
x
x2
In order to remove the first derivative, we put y = u.v in (1) to get the normal equation
d 2v
Q1v = R1
...(2)
dx 2
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Differential Equations
215
1
where v e – 2 pdx
Q1 = Q –
1
1
– – 21 dx
2
x
e
=
1
1 x dx
e
e x . elog x x e x
2
2
2
1
1
2
1 dp p 2 x 2 2 x 2 1 2 4
1
= 1 2 – 2 –1– 2 –
–
–
–
1
x x
x
x
x
2 dx
4
2 x2 4
x
x2
1
R1 = R e 2
p dx
0
On putting the values of Q1 and R1 in (2), we get
d 2u
dx 2
0(u ) = 0
du
= c1 u = c1x + c2
dx
y = u.v = (c1x + c2) x ex
2
Example 96. Solve
d y
dx
2
d2y
– 4x
d 2u
dx 2
0
Ans.
2
dy
(4 x 2 – 1) y – 3e x sin 2 x (U.P. II Semester, (C.O.) 2004)
dx
2
dy
(4 x 2 – 1) y – 3 e x sin 2 x
dx
dx
2
2
Here p = – 4x, Q = 4x – 1, R = – 3ex sin 2x
Solution. We have,
2
4x
1
...(1)
1
– pdx
– – 4 x dx
2
2 x dx
e 2
e
ex
In order to remove the first derivative, v e 2
2
d u
Q1u = R1
On putting y = uv, the normal equation is
dx 2
1 dp p 2
1
16 x 2
–
(4 x 2 – 1) – (– 4) –
where Q1 = Q –
= 4x2 – 1 + 2 – 4x2 = 1
2 dx
4
2
4
...(2)
2
R – 3e x sin 2 x
– 3 sin 2 x
R1 =
2
v
ex
d 2u
u – 3 sin 2 x
dx 2
(D2 + 1)u = – 3 sin 2x
2
A.E. is D + 1 = 0 D = i C.F. = c1 cos x + c2 sin x
1
– 3 sin 2 x
(– 3 sin 2 x)
sin 2 x
P.I. =
D2 1
– 4 1
u = c1 cos x + c2 sin x + sin 2x
y = u. v = (c1 cos x + c2 sin x + sin 2x)ex2
Equation (2) becomes
Ans.
EXERCISE 3.34
Solve the following differential equations:
d2 y
1.
– 2 tan x. y – 5 y 0
dx 2
2
d2 y
dy
2.
– 4 x (4 x 2 – 3) y e x
2
dx
dx
3.
4.
5.
2
d y
1 2
( x 2 x)
e2
dy
( x 2 2) y
dx
dx
d2y
dy 2 2
– 2x
n 2 y 0
dx
dx 2
x
d 2 y 2 dy
– n2 y 0
dx 2 x dx
2
– 2x
Ans. y = (a e2x + e–3x)sec x
Ans. y = (c1ex + c2 e–x – 1)
x2
Ans. y = (c1 cos 3 x c2 sin 3
x2 1 x 2
e .e
x) e 2 4
Ans. y = (c1 cos nx + c2 sin nx)x
nx
– nx
)
Ans. y (c1 e c2 e
1
x
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216
Differential Equations
2
6.
7.
d2y
1 dy 1
1
6
1
2/3 – 4/3 – 2 y 0
2
dx 4 x
dx
x
x
x3
2
d y
1 dy
y
–
2 (– 8 x x ) 0
2
dx
x
dx
4x
Ans. y (c1 x3 c2
3
– x3
x –2 ) e 4
2
–1
Ans. y (c1x c2 x ) e
x
3.38 METHOD OF SOLVING LINEAR DIFFERENTIAL EQUATIONS BY CHANGING
THE INDEPENDENT VARIABLE
d2y
dy
P
Qy = R
Consider,
...(1)
2
dx
dx
Let us change the independent variable x to z and z = f (x)
2
d 2 y dz
dy d 2 z
2
2 =
dz dx 2
dz dx
dx
dy
d2y
Putting the values of
and
in (1), we get
dx
dx 2
d 2 y dz 2 dy d 2 z
dy d 2 z
2
P
Qy = R
2
2
dz dx
dz dx
dz dx
dy
dy dz
=
dx
dz dx
d2y
2
dz d 2 z dy
d 2 y dz
2
Qy = R
P
dz 2 dx
dx dx dz
dz d 2 z
R
P
2
2
2
dx
dx
d y
dy Qy = dz 2 d y P dy Q y = R
...(2)
1
1
1
2
dz
d z2
dz dz 2
dz 2
dz
dx
dx
dx
dz d 2 z
P 2
dx dx
Q
R
,
where
P1 =
Q1 =
R1 =
2 ,
2
2
dz
dz
dz
dx
dx
dx
Equation (2) is solved either by taking P1 = 0 or Q1 = a constant.
Equation (2) can be solved by by two methods, by taking
First Method,
P1 = 0
Second Method,
Q = constant
Working Rule
2
Step 1. Coefficient of d y should be made as 1 if it is not so.
dx 2
Step 2. To get P, Q and R, compare the given differential equation with the standard form
y + P y + Qy = R.
Step 3. Find P1, Q1 and R1 by the following formulae.
d2y
dz
p
R
dx
dx 2
, R =
P1 =
2
1
2
dz
dz
dx
dx
Step 4. Find out the value of z by taking
First Method, P1 = 0
Second Method. Q1 = constant
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Differential Equations
217
d2y
dy
P1
Q1 y = R1
dz
dz 2
On solving this equation we can find out the value of y in terms of z.
Then write down the solution in terms of x by replacing the value of z.
Step 5. We get a reduced equation
Example 97. Solve
d2y
2
cot x
dy
4 y cosec 2 x = 0
dx
dx
2
d
y
dy
Solution. We have,
cot x
4 y cosec 2 x = 0
2
dx
dx
Here, P = cot x, Q = 4 cosec2 x and R = 0
Changing the independent variable from x to z, the equation becomes
d2y
dx 2
P1
dy
Q1 y = 0
dz
P
where
P1 =
Case I. Let us take P1 = 0
dz d 2 z
p
dx dx 2
dz
dx
Put
(3) becomes
2
...(1)
...(2)
dz d 2 z
dx dx 2 Q = Q
, 1
2
2
dz
dz
dx
dx
= 0 or P
dz d 2 z
d2z
dz
2 =0
cot x
=0
2
dx dx
dx
dx
...(3)
d2z
dv
dz
= v,
2 =
dx
dx
dx
dv
dv
(cot x ) v = 0
= – cot x. dx
dx
v
log v = – log sin x + log c = log c log c cosec x v = c cosec x
dz
x
= c cosec x dz = (c cosec x) dx z = c log tan
dx
2
Case II.
Now let us take Q1 = Constant.
Q1 =
Q
dz
dx
2
=
4 cosec2 x
2
2
c cosec x
=
4
c2
which is constant
Hence the equation (2) reduces to
4
d2y
dy 4
d2y
4
0
2 y = 0 or
2 y =0
P1 0, Q1 c 2
2
2
dz c
dz
dz
c
2 4
4
2
m2 2 = 0 m = i
A.E. is
D 2 y = 0,
c
c
c
x
2z
2z
C.F. = c1 cos
c2 sin
z c log tan
2
c
c
x
x
y = c1 cos 2 log tan c2 sin 2 log tan
Ans.
2
2
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218
Differential Equations
2
1
Example 98. Solve x 6 d y 3x 5 dy a 2 y = 2
2
x
dx
dx
1
d 2 y 3 dy
y
Solution. We have,
a2 6 = 8
x
dx 2 x dx
x
3
a2
Here
P = and Q 6
x
x
On changing the independent variable x to z, the equation (1) is reduced to
d2 y
P1
2
dz
Using Second Method
dy
Q1 y = R1
dz
Let Q1 = a2 (constant)
Q1 =
...(2)
Q
dz
dx
2
=
a2
dz
x6
dx
2
= constant = a2 (say)
2
dz
x 6 = 1 x3 dz = 1 dz = 1
dx
dx
x3
dx
2
3
d z
On differentiating twice, we have
2 =
x4
dx
3 1 3
dz d 2 z
P
x x3 x 4
dx dx 2
P1 =
=
= 0 R1 =
2
2
1
dz
3
x
dx
On putting the values of P1, Q1 and R1 in (2), we get
d2y
2
a 2 y = – 2z
dz
A.E. is m2 + a2 = 0, m = ± i a,
P.I. =
...(1)
dz =
dx
x3
z=
x 2
c
2
1
1
x8
=
= 2 = – 2z
2
1
x
dz
6
x
dx
R
(D2 + a2) y = – 2 z
C.F. = c1 cos az + c2 sin az
2 1
1
D2
( 2 z ) = 1 1 D ( 2 z ) =
1
( 2 z ) = 2z = 1
2
2
2
2
D a
a
a
a
a
a2
a 2 x2
y = C.F. + P.I.
a
a
1
y = c1 cos 2 c2 sin 2 2 2
Ans.
2x
2x
a x
1
2
Example 99. Solve
2
d2 y
2
dx
d2 y
1 dy
4 x 2 y = x4
x dx
(U.P., I Semester Summer 2003, 2002)
1 dy
4 x 2 y = x4
x
dx
dx
1
Hence
P = , Q = 4x2, R = x4
x
On changing the independent variable x to z, the equation (1) is transformed as
Solution. We have,
d2y
2
dy
P1
Q1 y = R1
dz
dz 2
Using Second Method
Let
Q1 = 1 (constant)
...(1)
...(2)
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Differential Equations
219
but
Q1 =
2
Q
2
=
4x 2
2
dz
dx
dz
= 4x2
= 2x
dx
dz
dx
= constant = 1 (say)
dz
dx
dz = 2x dx z = x2 + c x2 = z – c
...(3)
2
dz d z
1
P 2
(2 x ) 2
dx dx
x
P1 =
=
=0
2
(2 x)2
dz
dx
R1 =
R
=
x4
=
zc
x2
=
4
4
[Using (3)]
2
4x2
dz
dx
On putting the value of P1, Q1 and R1 in (2), we get
d2 y
zc
zc
dy
zc
y =
(1)
y
=
(D2 + 1) y =
2
2
dz
4
4
dz
4
dz
A.E. is m2 + 1 = 0 m = ± i
C.F. = c1 cos z + c2 sin z
1 z c
zc
zc
2 –1 z c =
P.I. = 2
=
(1 D 2 )
= (1 + D )
4
D 1 4
4
4
Now complete solution = C.F. + P.I.
d2y
(0)
z c
x2
y = c1 cos x 2 c2 sin x 2
4
4
EXERCISE 3.35
y = c1 cos z c2 sin z
Ans.
Solve the following differential equations:
d2y
1.
x4
2.
cos x
3.
4.
dx 2
d2 y
dx 2
x
d y
6.
dx 2
d2y
8.
dx 2
tan x
2
–
dy
a2 y 0
dx
dy
sin x – 2 y cos3 x 2 cos5 x
dx
dy
y cos 2 x 0
dx
dy
– 4 x 3 y 8x 2 sin x 2
dx
2
5.
7.
d2y
d2y
dx
2 x3
dx 2
d2y
dx 2
d2 y
dx
2
(3sin x – cot x )
(tan x – 1)2
– cot x
dy
2 y sin 2 x e – cos x sin 2 x
dx
dy
– n ( n – 1) y sec 4 x 0
dx
dy
– y sin 2 x cos x – cos3 x
dx
(3 sin x – cot x )
dy
2 y sin 2 x e – cos x sin 2 x
dx
Ans. y c1 cos
Ans. y c1 e
2 sin x
c2e –
a
a
c2 sin
x
x
2 sin x
sin 2 x
Ans. y = c1 cos (sin x) + c2 sin (sin x)
x
cos x
Ans. y c1 e c2e
1 – cos x
e
6
2cos x
c2 ecos x
Ans. y c1 e
1 – cos x
e
6
2
Ans. y = C1 e–n tan x + C2 e(n – 1) tan x
Ans. y = C1 e– cos x + C2 ecos x – cos x
cos x
C2 e 2 cos x
Ans. y C1e
1 – cos x
e
6
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220
Differential Equations
3.39 Applications of Differential Equations of Second Order
Example 100. The differential equation satisfied by a beam uniformly loaded (W kg/metre),
with one end fixed and the second end subjected to tensile force P, is given by
d2y
1
= Py – Wx 2
2
dx 2
Show that the elastic curve for the beam with conditions
dy
at x 0, is given by
y= 0
dx
W
Wx 2
P
(1
–
cosh
nx
)
where n 2
y=
2P
EI
Pn2
E.I .
Solution. We have, E.I .
d2y
2
= Py –
1
W . x2
2
dx
2
d y
P
W
–
y = –
x2
2 E.I .
dx 2 E.I .
P
P
0 m2
n2
E.I .
E.I .
C.F. = c1 enx + c2 e– nx
A.E. is m2 –
P.I. =
P
W
2
x2
D –
y–
E.I .
2 E.I .
2 P
mn
n
EI
W 2
W
1
. x2
–
x –
2
2
P
2
E
.
I
.
2
E
.
I
.
2
D
–
n
D –
E .I .
1
–1
D2
W
D2 2
W
2
2
2
1
–
.
x
1
. x =
=
x 2
2
2n 2 . E.I .
n 2
2n2 E.I .
n 2
2 n E.I .
n
W
2
2
y = c1 enx c2 e – nx 2
x 2
2n E.I .
n
W
...(1)
Differentiating (2) w.r.t. x, we get
dy
W
(2 x )
= n c1 enx – n c2 e – nx 2
dx
2n E.I .
dy
0 in (3), we get
Putting x = 0,
dx
0 = n c1 – n c2 c1 = c2
Putting x = 0, y = 0 in (2), we get
W
2
W
0 c1 c2 4
0 = c1 c2 2
2n E .I . n 2
n E.I .
W
W
Putting c1 = c2 in (4), we get 0 2 c1 4
c1 –
,
n E.I .
2 n 4 E.I .
P
n 2 E.I . P
Now,
n2 =
E.I .
W
c1 = c2 –
2 n2 P
Putting the values of c1 and c2 in (2), we get
–W
W 2
2
(e nx e – nx )
y=
x 2
2
2
P
2n P
n
...(2)
...(3)
...(4)
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Differential Equations
y
=
–W
n2 P
221
cosh nx
W 2
W
x
2P
P n2
y=
W
P. n 2
(1 – cosh nx )
W x2
2P
Ans.
EXERCISE 3.36
1.
A beam of length l and of uniform cross-section has the differential equation of its elastic curve as
w l2
– x2
2 4
dx
where E is the modulus of elasticity, I is the moment of inertia of the cross-section, w is weight per unit
length and x is measured from the centre of span.
E.I .
d2y
2
dy
0 . Prove that the equation of the elastic curve is
dx
1 2 l 3. x2 x 4
5w. l 4
y
–
2 E.I . 8
12 384 E.I .
A horizontal tie rod of length l is freely pinned at each end. It carries a uniform load w kg per unit length
and has a horizontal pull P. Find the central deflection and the maximum bending moment, taking the
If at x 0,
2.
Ans. w sec h al – 1 where a 2 P
a
2
EI
A light horizontal strut AB is freely pinned at A and B. It is under the action of equal and opposite
l
compressive forces P at its ends and it carries a load W at its centre. Then for 0 x ,
2
origin at one of its ends.
3.
EI
d2y
dx 2
Py
1
Wx 0
2
dy
W sin ax
l
P
0 at x . Prove that y
– x , where a 2
dx
2 P a cos al
2
EI
2
A horizontal tie-rod of length 2l with concentrated load W at its centre and ends freely hinged satisfies
dy
d2 y
W
0. Prove that
Py – x. With conditions x = 0, y = 0 and x l ,
the differential equation EI
dx
2
dx 2
the deflection and bending moment M at the centre (x = l) are given by W ( nl – tan nl ) and
2 Pn
W
M –
tan h nl , where n2 EI = P.
2n
Also y = 0 at x = 0 and
4.
Example 101. The voltage V and the current i at a distance x from the sending end of the
transmission line satisfy the equations.
dV
di
Ri ,
GV
dx
dx
where R and G are constants. If V = V0 at the sending end (x = 0) and V = 0 at receiving end
(x = 1). Show that
sinh n(l x )
V V0
sinh nl
When n2 = RG
Solution.
dV
Ri
dx
. . . (1)
di
GV
dx
. . . (2)
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222
Differential Equations
When x = 0,
V = V0
When x = l V
=0
Putting the value of i from (1) into (2), we get
d dV 1
GV
dx dx R
d 2V
RGV
dx 2
d 2V
( RG )V 0
(D2 – RG)V = 0
(RG = n2)
dx 2
A. E. is
m2 – n2 = 0,
m=n
V = Ae nx + Be– nx
Now, we have to find out the value of A and B with the help of given conditions.
On putting x = 0 and V = V0 in (3), we get V0 = A + B
On putting x = 1 and V = 0 in (3), we get 0 = Ae nl + Be– nl
. . . (3)
... (4)
V0
V0 e 2 nl
B
,
1 e 2nl
1 e 2nl
Substituting the values of A and B in (3), we have
On solving (4) and (5), we have A
V
V0 e nx V0 e 2 nl e nx V0 [e nx e2 nl nx ] V0 [e nl nx ) e ( nl nx ) ]
sinh n(l x)
V0
nl
nl
2 nl
2 nl
2 nl
e e
1 e
1 e
1 e
sinh nl
Proved.
EXERCISE 3.37
1.
An e.m.f. E sin pt is applied at t = 0 to a circuit containing a condenser C and inductance L in
series. The current x satisfies the equation
dx 1
L xdt E sin pt
dt C
1
2
If p
and initially the current x and the charge q are zero, show that the current in the
LC
dq
circuit at time t is given by E t sin pt, where x
.
dt
2l
Fill in the blanks:
dy
2. The integrating factor of cos 2 x y tan x is
...............
dx
dy
3. The integrating factor of x( x 1) ( x 2) y x 2 (2 x 1) is .............
dx
dy
4. Solution of ( x y 1) 1 is ............
dx
5. Mdx + Ndy = 0 is an exact differential equation if .......
6. P. I. of (D2 + 4)y = sin 3x is ...........
7. P. I. of (D2 – 2D + 1)y = ex is ..........
8.
9.
d 2 y xdy
y x is .............
dx 2 dx
If the C.F. of ay+ by + cy + X is Ay1 + By2, then P. I. = uy1 + vy2 where u = ....... and v= .......
2
On putting x = eZ, the transformed differential equation of x
Ans. 2. etan x 3.
M N
x 1
4. x + y + 2 = cey 5. dy x
2
x
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4
4
Determinants and Matrices
DETERMINANTS AND MATRICES
4.1
INTRODUCTION
In Engineering Mathematics, solution of simultaneous equations is very important. In this
chapter we shall study the system of linear equations with emphasis on their solution by means of
determinants.
4.2
DETERMINANT
The notation of determinants arises from the process of elimination of the unknowns of
simultaneous linear equations.
Consider the two linear equations in x,
a1 x + b1 = 0
... (1)
a2 x + b2 = 0
... (2)
b1
a1
Substituting the value of x in (2); we get the eliminant
x
From (1)
b
a2 1 b2 0
a1
or
a1b2 – a2b1 = 0
From (1) and (2) by suppressing x, the eliminant is written as
a1
b1
a2 b2
... (3)
... (4)
0
when the two rows of a1, b1 and a2, b2 are enclosed by two vertical bars then it is called a
determinant of second order.
a1
b1
and
a2
b2
Column 1
Column 2
a1 b1
Row 1
....
a2 b2
Row 2
....
Each quantity a1, b1, a2, b2 is called an element or a constituent of the determinant.
From (3) and (4), we know that both expressions are eliminant, so we equate them.
a1 b1
a1 b1
a1 b2 a2 b1
= a1b2 – a2b1
or
a2 b2
a2 b2
223
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224
Determinants and Matrices
a1b2 – a2b1 is called the expansion of the determinant of
Example 1. Expand the determinant
+
3
3 2
6 7
a1
b1
a2 b2
.
.
–
2
Solution.
Ans.
= (3) × (7) – (2) × (6) = 21 – 12 = 9.
6
7
EXERCISE
Expand the following determinants :
4 6
1. 2 5
Ans. 8
2.
8 5
3. 3 1
Ans. – 7
4.
4.1
3 7
2 4
5 2
4
3
Ans. – 26
Ans. 23
4.3. DETERMINANT AS ELIMINANT
Consider the following three equations having three unknowns, x, y and z.
a1 x + b1 y + c1 z = 0
a2 x + b2 y + c2 z = 0
a3 x + b3 y + c3 z = 0
From (2) and (3) by cross-multiplication, we get
...(1)
...(2)
...(3)
y
x
z
k (say)
b2 c3 b3 c2 a3 c2 a2 c3 a2 b3 a3 b2
x = (b2 c3 – b3 c2) k
y = (a3 c2 – a2 c3) k
and
z = (a2 b3 – a3 b2) k
Substituting the values of x, y and z in (1), we get the eliminant
a1 (b2c3 – b3c2) k + b1 (a3c2 – a2c3) k + c1 (a2b3 – a3b2) k = 0
or
a1 (b2c3 – b3c2) – b1 (a2c3 – a3c2) + c1 (a2b3 – a3b2) = 0
...(4)
From (1), (2) and (3) by suppressing x, y, z the remaining can be written in the determinant as
a1
b1
c1
a2 b2 c2 0
a3 b3
c3
...(5)
This is determinant of third order.
As (4) and (5) both are the eliminant of the same equations.
a1 b1 c1
a2 b2 c2 a1 (b2c3 b3c2 ) b1 (a2c3 a3c2 ) c1 (a2b3 a3b2 ) 0
a3 b3 c3
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Determinants and Matrices
a1
b1
c1
a2 b2 c2 a1
or
a3 b3
4.4.
225
c3
b2
c2
b3
c3
b1
a2 c2
a3
c1
c3
a2 b2
a3
b3
MINOR
The minor of an element is defined as a determinant obtained by deleting the row and column
containing the element.
Thus the minors a1, b1 and c1 are respectively.
b2
c2
b3
c3
b1
c1
a2 c2
,
a3
c3
and
a2 b2
a3
b3
Thus
a1
a2 b2 c2
a3 b3
c3
= a1 (minor of a1) – b1 (minor of b1) + c1 (minor of c1).
4.5. COFACTOR
Cofactor = (– 1)r+c Minor
where r is the number of rows of the element and c is the number of columns of the element.
The cofactor of any element of jth row and ith column is
(– 1)i+j minor
1+1
Thus the cofactor of a1 = (– 1) (b2c3 – b3c2) = + (b2c3 – b3c2)
The cofactor of b1
= (– 1)1+2 (a2c3 – a3c2) = – (a2c3 – a3c2)
The cofactor of c1
= (– 1)1+3 (a2b3 – a3b2) = + (a2b3 – a3b2)
The determinant
= a1 (cofactor of a1) + a2 (cofactor of a2) + a3 (cofactor of a3).
Example 2. Write down the minors and cofactors of each element and also evaluate the
determinant.
1
3 –2
4 –5
6
3
Solution. M11 = Minor of element (1)
5
2
3
2
1
4 5
6
3
5
2
5 6
( 5) 2 6 5 10 30 40
5 2
Cofactor of element (1) = A11 = (– 1)1 + 1 M11 = (– 1)2 (– 40) = – 40
M12 = Minor of element (3)
1 3 2
4 6
4 5 6
4 2 3 6 8 18 10
3 2
3
5
2
Cofactor of element (–2) = A12 = (– 1)1 + 2 (– 10) = 10
M13 = Minor of element (– 2)
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226
Determinants and Matrices
1 3 2
=
4 5
3
4 5
6
3
2
5
5
4 5 (5) 3 20 15 35
Cofactor of element (– 2) = A13 = (– 1)1+3 M13 = (–1)4 35 = 35
M21 = Minor of element (4)
1 3 2
3 2
3 2 (2) 5 6 10 16
= 4 5 6
5 2
3 5 2
Cofactor of element (4) = A21 = (– 1)2+1 M21 = (– 1)2+1 (16) = – 16
M22 = Minor of element (– 5)
1 3 2
1 2
1 2 (2) 3 2 6 8
= 45 6
3 2
3 5 2
Cofactor of element (– 5) = A22 = (– 1)2+2 M22 = (– 1)2+2 (8) = 8
M23 = Minor of element (6)
1
3 2
1 3
4
5
6
1 5 3 3 5 9 4
=
3 5
3
5
2
Cofactor of element (6) = A23 = (–1)2+3 M23 = (– 1)2+3 (– 4) = 4
M31 = Minor of element (3)
1
3
=
2
3 2
4 5 6
3 6 (2) (5) 18 10 8
5 6
3 5 2
Cofactor of element (3) = A31 = (– 1)3+1 M31 = (– 1)3+1 8 = 8
M32 = Minor of element (5)
1
3 2
1 2
4 5 6
1 6 (2) 4 6 8 14
4 6
35 2
Cofactor of element (5) = A32 = (– 1)3+2 M32 = (– 1)3+2 14 = – 14
M33 = Minor
of element (2)
=
1
=
3 2
1 3
4 5 6
1 (5) 4 3 5 12 17
4 5
3 5 2
Cofactor of element (2) = A33 = (– 1)3+3 M33 = (– 1)3+3 (– 17) = – 17.
1
3 2
4 5
6 = 1 × (cofactor of 1) + 3 × (cofactor of 3) + (– 2) × [cofactor of (– 2)].
3
2
5
= 1 × (– 40) + 3 × (10) + (– 2) × (35)
= – 40 + 30 – 70
= – 80
Ans.
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Determinants and Matrices
227
Example 3. Find :
(i) Minors
(ii) Cofactors of the elements of the first row of the determinant
2 3 5
4 1 0
6
2 7
Solution.
(i) The minor of the element (2) is
235
1 0
2 7
4 1 0
6 2 7
(1) (7) (0) (2) 7 0 7
The minor of the element (3) is
23 5
4 0
6 7
4 1 0
6 2 7
(4) (7) (0) (6) 28 0 28
The minor of the element (5) is
23 5
4 1
6 2
4 1 0
6 2 7
(4) (2) (1) (6) 8 6 2
The cofactor of (2) = (– 1)1+1 (7) = + 7
The cofactor of (3) = (– 1)1+2 (28) = – 28
The cofactor of (5) = (– 1)1+3 (2) = + 2.
6 2 3
Example 4. Expand the determinant 2 3 5
(ii)
4 2 1
6 2 3
Solution.
Ans.
2 3 5 = 6 (cofactor of 6) + 2 (cofactor of 2) + 3 (cofactor of 3).
4 2 1 = 6 (3 × 1 – 5 × 2) – 2 (2 × 1 – 4 × 5) + 3 (2 × 2 – 3 × 4)
=
=
=
=
6 (3 – 10) – 2 (2 – 20) + 3 (4 – 12)
6 (– 7) – 2 (– 18) + 3 (– 8)
– 42 + 36 – 24
– 30.
1 0 4
3 5 –1 .
Example 5. Evaluate the determinant
0 1 2
Ans.
(i) With the help of second row, (ii) with the help of third column.
Solution.
1 0 4
(i) 3 5 1 = 3 × (cofactor of 3) + 5 × (cofactor of 5) + (– 1) (cofactor of – 1).
0 1 2
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Determinants and Matrices
0 4
1 4
1 0
= 3 × (– 1)2+1 1 2 + 5 × (– 1)2+2 0 2 + (– 1) × (– 1)2+3 0 1
= – 3 × (0 – 4) + 5 (2 – 0) + (1 – 0)
= 12 + 10 + 1 = 23
1 0
(ii)
Ans.
4
3 5 1 = 4 × (cofactor of 4) + (– 1) (cofactor of (– 1)) + 2 × (cofactor of 2)
0 1 2
3 5
1 0
1 0
= 4 × (– 1)1+3
+ (– 1) (– 1)2+3
+ 2 × (– 1)3+3
0 1
0 1
3 5
= 4 × (3 – 0) + (1 – 0) + 2 (5 – 0)
= 12 + 1 + 10 = 23
Ans.
0 1 2 3
1 0 2 0
Example 6. Expand the fourth order determinant
2 0 1 3
1 2 1 0
0 2 0
Solution. Given determinant = (0) (–1)1 + 1
1 2 0
1 2
0 1 3 1 (–1)
2 1 3
2 1 0
1 1 0
1 0 0
+ 2 (–1)1+ 3
Now
1
= 0 2
1
1 2 0
2 1 3
1 1 0
1 0 0
2 0 3
1 2 0
1 0 2
2 0 1
1 2 1
1 0 2
2 0 3 3 (–1)1 4 2 0 1
1 2 0
2 0
1 3 2
1 0 0
1 0 2
2 0 3 3 2 0 1
1 0
1 2 0
1 2 1
1 2 1
1(1 0 3 1) 2 (2 0 3 1) 0 (2 1 1 1)
3 6 03
1 (0 0 3 2) 0 (2 0 3 1) 0(2 2 0 1)
6
1 ( 0 1 1 2) 0(2 1 1 1) 2 (2 2 0 1)
2– 0 8 6
0 1 2 3
1 0 2 0 3 2( 6 ) 3(6)
Now
2 0 1 3 3 – 12 18 33
1 2 1 0
Ans.
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Determinants and Matrices
229
EXERCISE 4.2
Write the minors and co factors of each element of the following determinants and also evalutate
the determinant in each case :
1.
2.
2
M11 =
3
A11 = – 9, A12 = – 4, A21 = – 3, A22 = – 2
4 9
cos sin
sin
cos
28 7 4
14 3 2
4.
1 a
1 b
bc
ca
1 c
ab
|A|=6
Ans.
M11 = cos , M12 = sin M21 = – sin , M22 = cos
A11 = cos , A12 = – sin , A21 = sin , A22 = cos | A | = 1
M11
M23
A11
A23
42 1 6
3.
– 9, M12 = 4, M21 = 3, M22 = – 2
Ans.
= 2, M12 = 0, M13 = – 14, M21 = – 16, M22 = 0
= 112, M31 = – 38, M32 = 0, M33 = 266
= 2, A12 = 0, A13 = – 14, A21 = 16, A22 = 0
= – 112, A31 = – 38, A32 = 0, A33 = 266, | A | = 0 Ans.
M11 = (ab2 – ac2), M12 = (ab – ac), M13 = (c – b), M21 = a2b – bc2
M22 = (ab – bc), M23 = (c – a), M31 = (ca2 – cb2), M32 = ca – bc, M33 = (b – a),
A11 = (ab2 – ac2), A12 = (ac – ab), A13 = (c – b), A21 = bc2 – a2b
A22 = (ab – bc), A23 = (a – c), A31 = (ca2 – cb2), A32 = (bc – ca), A33 = (b – a)
| A | = (a – b) (b – c) (c – a).
Ans.
Expand the following determinants :
2 3
4
5
1 6
5.
7
8
Ans. | A | = 5
5
6.
9
0
7
8 6 4
2
3
9
Ans. | A | = 42
a h g
7. h b f
g f c
Ans. | A | = abc + 2fgh – af 2 – bg2 – ch2
Expand the following determinants by two methods :
(i) along the-third row.
(ii) along the-third column.
8.
1 3 2
4 1 2
3
5 2
Ans. | A | = 40
11.
12.
log 3 512 log 4 3
log 3 8
log 4 9
9.
3 2
4
1
2
1
0
1 1
2 3 2
10.
1 2
2
Ans. | A | = – 7
Ans. | A | =
3
1 3
Ans. | A | = – 37
15
2
If a, b, c are all positive and are the pth, qth, rth
terms of a G.P. respectively; then prove that
log a p 1
log b q 1 0
13.
log c r 1
3 2 5
1 4 3
6
2
4
1
7
0
2 1
0
3
Ans. 96
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230
4.6
Determinants and Matrices
RULESOF SARRUS (For third order determinants only).
After writing the determinant, repeat the first two columns as below
= (a1b2c3 + b1c2a3 + c1a2b3) + (– c1b2a3 – a1c2b3 – b1a2c3)
Example 7. Expand the determinant
2 3 4
1 5 3 by Rule of Sarrus.
3 0 5
Solution.
=(2) × (5) × (5) + (3) × (3) × (3) + (4) × (1) × (0) – (4) × (5) × (3) – (2) × (3) × (0) – (3) × (1) × (5)
= 50 + 27 + 0 – 60 – 0 – 15 = 2
Ans.
EXERCISE 4.3
Expand the following determinants by Rule of Sarrus.
3 2 4
1.
5
1
1
2 6
7
Ans. – 155
1 4 2
2.
2 5 3
3 6 4
Ans. 0
6
3.
3
7
32 13 37
10 4 11
Ans. 10
4.
9 25 6
7 13 5
9 23 6
Ans. 6
ax c
b
bx a
0
5. If a + b + c = 0, solve the equation c
b
a
cx
Ans. x
2
2
2
(a b c ab bc ca) , x = 0
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Determinants and Matrices
231
4.7 PROPERTIES OF DETERMINANTS
Property (i) The value of a determinant remains unaltered, if the rows are interchanged into columns
(or the columns into rows).
Consider the determinant.
a1 b1 c1
a2 b2 c2
a3 b3 c3
= a1 (b2c3 – b3c2) – b1 (a2c3 – a3c2) + c1 (a2b3 – a3b2)
= a1b2c3 – a1b3c2 – a2b1c3 + a3b1c2 + a2b3c1 – a3b2c1
= (a1b2c3 – a1b3c2) – (a2b1c3 – a2b3c1) + (a3b1c2 – a3b2c1)
= a1 (b2c3 – b3c2) – a2 (b1c3 – b3c1) + a3 (b1c2 – b2c1)
a1 a2 a3
b1
b2
b3
Proved.
c1 c2 c3
Property (ii) If two rows (or two columns) of a determinant are interchanged, the sign of the value
of the determinant changes.
Interchanging the first two rows of , we get
a2 b2
' a1
c2
b1
c1
a3 b3
c3
= a2 (b1c3 – b3c1) – b2 (a1c3 – a3c1) + c2 (a1b3 – a3b1)
= a2b1c3 – a2b3c1 – a1b2c3 + a3b2c1 + a1b3c2 – a3b1c2
= – [(a1b2c3 – a1b3c2) – (a2b1c3 – a3b1c2) + (a2b3c1 – a3b2c1)]
= – [(a1 (b2c3 – b3c2) – b1 (a2c3 – a3c2) + c1 (a2b3 – a3b2)]
a1 b1 c1
= a2 b2 c2
a3 b3 c3
Proved.
Property (iii) If two rows (or columns) of a determinant are identical, the value of the determinant
is zero.
a1 b1
Let
a1 b1
c1
c1 , so that the first two rows are identical.
a3 b3 c3
By interchanging the first two rows, we get the same determinant .
By property (ii), on interchanging the rows, the sign of the determinant changes.
or
=–
or
2=0
or = 0
Proved.
Property (iv) If the elements of any row (or column) of a determinant be each multiplied by the
same number, the determinant is multiplied by that number.
ka1 kb1 kc1
a2
b2
c2
a3
b3
c3
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Determinants and Matrices
= ka1 (b2c3 – b3c2) – kb1 (a2c3 – a3c2) + kc1 (a2b3 – a3b2)
= k [a1 (b2c3 – b3c2) – b1 (a2c3 – a3c2) + c1 (a2b3 – a3b2)]
a1
b1
c1
k a2 b2 c2 k .
a3 b3
c3
Example 8. Prove that
a2
b
a
2
b
c2
a2
1
1
2
2
ca a
b
a3
c ab
b3
1
c
2
c3
bc
2
b ca
c2
c ab
b
Solution.
a
bc
By multiplying R1, R2, R3 by a, b and c respectively we get
a3 a2 abc
a3
3
2
1 b b abc
abc
c3 c2 abc
3
abc b
abc
c3
a3
a2 1
b
2
1
c
2
1
a2
1
1 a2
a3
2
1
1 b2
b3
c3
c2
1
1 c2
c3
1
1
1
2
2
c2
b3
c3
b
3
a
b
a3
b
By changing rows into columns
Proved
Example 9. Without expanding and or evaluating, show that
a
2
b
2
c
2
d
2
a 1 bcd
b 1 cda
c 1 dab
d 1 abc
a
3
a
2
a 1
b
3
b
2
b 1
c
3
c
2
c 1
d
3
d
2
d 1
Solution
a
2
a 1 bcd
a3
a2
a
abcd
b
2
b 1 cda
3
2
b
abcd
c
2
c
1 dab
c3
c2
c
abcd
d2
d
1 abc
d3
d2
d
abcd
1
abcd
b
b
R1 aR1
R2 bR2
R3 cR3
R4 dR4
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Determinants and Matrices
abcd
abcd
a3
a2
3
2
b
b
c3
c2
3
2
d
d
233
a 1
a3
a2
a 1
b 1
3
2
b 1
c3
c2
c 1
3
2
d 1
1
C4
c 1 C4
abcd
d 1
1 a a2
Example 10. Prove that
1 b b
2
1 c
2
c
1 a bc
1 b ca
1 c
b
d
b
d
Proved
(Try yourself)
ab
Property (v) The value of the determinant remains unaltered if to the elements of one row (or
column) be added any constant multiple of the corresponding elements of any other
row (or column) respectively.
Let
a1 b1 c1
a2 b2 c2
a3 b3
c3
On multiplying the second column by l and the third column by m and adding to the
first column we get
a1 lb1 mc1
b1
c1
' a2 lb2 mc2 b2
c2
a3 lb3 mc3 b3
c3
a1
b1
b1
c1
b1
c1
c1
b1
c1
a2 b2 c2 l b2 b2 c2 m c2 b2 c2
a3 b3
c3
b3
b3
c3
c3 b3
c3
= + 0 + 0
=
(Since columns are identical)
Proved
9 9 12
Example 11. Evaluate, using the properties of determinant 1 3 – 4
1 9 12
9 9 12
Solution. Let
1 3 4
1 9 12
Applying : R1 R1 3R 2 and R 3 R 3 3R 2 , we get
12 18
1
2 3
0
3 4 6 2 1 3 4
4 18
Expand by C3
0
= 6×2×4
0
2 9
0
2 3
2 9
= 48 (2 × 9 – 2 × 3) = 48 × 12 = 576.
Ans.
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234
Determinants and Matrices
265 240 219
Example 12. Without expanding evaluate the determinant 240 225 198
219 198 181
Solution. Applying C1 C1 C3 and C 2 C 2 C3 , we get
46
21 219
42 27 198
38 17
181
Applying C1 C1 2C2 and C3 C3 10 C2 , we get
4
21
9
12 27 72
4 17
11
Applying R1 R1 R3 and R2 R2 3R3
0
4
2
0
0 78 39 2(39) 0
4 17
11
2 1
2 1 [Taking 2 common from R1 and 39 common from R2]
4 17
11
= 78 × 0 = 0
(Since R1 and R2 are identical) Ans.
bc ca ab
c
a a b bc =0
Example 13. Show that
ab bc ca
bc ca ab
Solution. Let
ca ab
ab
bc
bc ca
Applying C1 C1 C2 C3 , we get
0 ca ab
0 ab bc 0
0 bc ca
[C1 consists of all zeros.]
sin
cos
sin( + )
Example 14. Without expanding, evaluate the determinant sin cos sin( + ) .
sin cos
sin( + )
sin cos sin ( )
sin cos sin ( )
Solution. Let
sin cos sin ( )
sin cos sin cos cos sin
sin
cos
sin cos cos sin
sin
cos
sin cos cos sin
[ sin (A + B) = sin A cos B + cos A sin B]
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Determinants and Matrices
235
sin cos 0
sin
cos 0
sin
cos 0
[Applying C3 C3 cos . C1 sin . C2 ]
=0
[ C3 consists of all zeros] Ans.
2x – 1 x + 7
x
6
2
x–1
x+1
3
Example 15. Solve the determinantal equation
2x 1 x 7
0
x4
x
6
2
x 1
x 1
3
Solution. Given equation
x+4
By applying R1 R1 – (R2 + R3), we get
0
0
0
x 1
x
6
2
x 1 x 1
0
3
On expanding by first row, we get
(x – 1) (x2 + x – 6x + 6) = 0 (x – 1) (x – 2) (x – 3) = 0 x = 1, 2, 3
Example 16. Using the properties of determinants, show that
x+ y
x x
3
5x + 4y 4x 2x = x .
10x + 8y 8x 3x
x y
5x 4 y
Solution. Let
Ans.
x x
4x 2x
10 x 8 y 8x 3x
Operate : R2 R2 2 R1 ; R3 R3 3R1
x y
x
x
3x 2 y
2x 0
a 2b
bc 2
Expand by C3
x
3x 2 y
2x
7 x 5 y 5x
7 x 5 y 5x 0
= x [5x (3x + 2y) – 2x (7x + 5y)]
= x [15x2 + 10 xy – (14x2 + 10 xy)] = x3.
Proved.
Example 17. Using the properties of determinants, evaluate the following :
2
2
0 ab ac
0
2
a c cb
0
Solution. Let a 2b
ab
2
0
a 2c cb2
ac
2
bc
2
2
0
0
Take a2, b2 and c2 common from C1, C2 and C3 respectively,
0 a a
2
2
a b c
2
b 0 b
c
c 0
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236
Determinants and Matrices
0
Operate : C2 C2 C3 ,
2
2
0
a
2
a b c b b b
c
2
2
2
a b c .a
c
0
b b
3 2 2
3 3 3
a b c (bc bc) 2a b c . Ans.
c c
Example 18. Using properties of determinants, prove that
Expand by R1,
Solution. Let
x
y
x
2
y
2
z
x
3
y
3
z
2
z
3
= x y z (x – y)(y – z) (z – x).
x
y
z
1
1
1
x
2
y
2
z
2
xyz x
y
z
x
3
y
3
z
3
2
2
x
y
z
2
0
xyz
Operate : C1 C1 C2 ; C2 C2 C3 ,
x y
2
x y
On expanding by R1, xyz
x y
2
x y
yz
2
2
y z
0
yz
2
2
y z
xyz ( x y) ( y z)
2
= xyz (x – y) (y – z) (z – x).
Example 19. Using the properties of determinants, show that
Solution. Let
a+x
y
z
x
a+ y
z
x
y
a+z
a
Operate : R1 R1 R2 ,
a
Operate : C2 C2 C1 ,
On expanding by R1
a
2
1
1
x y
yz
Proved.
2
az
a
0
y
z
az
0
0
x a y x
x
z
z
z
y
x a y
x
z
2
= a (a + x + y + z).
ax
y
x
a y
x
1
y x
a yx
z
a z
z
= a [(a + y + x) (a + z) – (y + x) z]
yx
az
= a [a2 + az + (y + x) a + (y + x) z – (y + x) z]
= a2 (a + x + y + z).
Proved.
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Determinants and Matrices
237
Example 20. If is the one of the imaginary cube roots of unity, find the value of the determinant
2
1
Solution. The given determinant
1
2
1
2
By R1 R1 R2 R3, we get
1 2 1 2 1 2
2
2
1
0
0
0
1
2
1
2
1
=0
[1 + + 2 = 0]
(Since each entry in R1 is zero) Ans.
Example 21. Without expanding the determinant, show that (a + b + c) is a factor of
b c a .
c a b
a b c
b c a
Solution. Let
a b c
c a b
abc b
1 b
c
c
c a (a b c) 1 c a
1 a b
abc a b
abc
Operate : C1 C1 C2 C3 ,
(a + b + c) is a factor of .
Example 22. Using properties of determinants, prove that :
Solution. Let
x+4
x
x
x
x+4
x
x
x
x+4
x4
x
x
x
x4
x
x
x
x4
3x 4
Operate : C1 C1 C2 C3 ,
1
x
(3x 4) 1 x 4
1
16(3x 4)
x
= 16 (3x + 4)
x
3x 4 x 4
3x 4
x
x
x
x4
x
1 x
x
(3x 4) 0 4 0 R2 R1
x4
Proved.
x
0 0 4 R3 R1
(3x 4)
4 0
0 4
Proved.
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238
Determinants and Matrices
1 a b+c
Example 23. Without expanding the determinant, prove that
1 b c + a = 0.
1 c
a +b
1 a bc
Solution. Let
1 b ca
1 c ab
1 a abc
Operate : C3 C3 C2 ,
1 a 1
1 b a b c (a b c) 1 b 1
1 c abc
1 c 1
=0
( C1 and C3 are identical). Proved.
Example 24. Without expanding the determinant, prove that
1 a2
a
2
Solution. Let
1 b
b
1 c2
c
Multiply R1 by a, R2 by b and R3 by c.
bc
1
a
1
b
1
c
a2
b
2
c2
bc
ca = 0
ab
ca
ab
1 a3 1
1 a 3 abc
3
1 1 b abc
abc
1 c 3 abc
3
1 . abc 1 b 1 1 0 0.
abc
1 c3 1
(Since C1 and C3 are identical) Proved.
1 a a
2
Example 25. Evaluate 1 b b2
1 c c
2
Solution. Let be the given determinant. Applying R2 R2 R1 and R3 R3 R1, we
get,
1
a2
a
2
0 ba b a
1 a
2
0 c a c2 a 2
1 a
a
2
(b a) (c a) 0 1 b a
0 1 ca
a
[Taking out (b – a) common
from R2 and (c – a) from R3]
2
= (b – a) (c – a) 0 1 b a
0 0 cb
[Applying R3 R3 R2 ]
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Determinants and Matrices
= (b – a) (c – a)
239
1 ba
[Expanding along C1]
0 cb
= (b – a) (c – a) (c – b).
Example 26. Using properties of determinants, prove that :
Ans.
1 a a3
1 b b 3 = (a – b)(b – c)(c – a)(a + b + c)
1 c
c3
1 a a3
Solution. Let
= 1 b b
1 c
3
c3
0 a b a3 b3
3
Operate : R1 R1 R2 ; R2 R2 R3 , 0 b c b c
1
(a b) (b c)
3
1.
c3
c
2
2
2
2
1 a ab b
1 b bc c
2
3
3
3
3
ab a b
bc
b c
(Expanding by C1)
2
0 (a c ) (ab bc)
Operate : R1 R1 R2 , (a b) (b c)
2
2
1
b bc c
= (a – b) . (b – c) . (– 1) [(a2 – c2) + b (a – c)]
= (a – b) . (b – c) (c – a) (a + b + c).
Example 27. Evaluate
a–b–c
2a
2a
2b
b–c–a
2b
2c
2c
c–a–b
Proved.
abc abc abc
Solution. By R1 R1 + R2 + R3, we get
2b
bca
2b
2c
2c
cab
1
1
1
(a b c) 2b b c a
2c
2b
2c
cab
0
0
1
(a b c) 2b (a b c)
2c
0
On expanding by first row = (a + b + c) (a + b +
Example 28. Show, without expanding
x
c)2
0
C2 C1
(a b c) C3 C1
= (a + b + c)3.
1
1
1
x
y
z = (x – y)(y – z)(z – x)
2
y
2
z
Ans.
2
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240
Determinants and Matrices
Solution. By C1 – C2, C2 – C3, we get
0
0
1
x y
yz
z
2
x y
( x y) ( y z )
Proved.
1
1
x y
yz
Solution. Let
2
2
2
x y
2
x y
yz
2
2
y z
2
y z z
On expanding by first row, we get
= (x – y) (y – z) (y + z – x – y) =
Example 29. Prove that
2
(x – y) (y – z) (z – x).
2
2
2
.
2
2
2
Applying R3 R1 R3
= ( + + )
2
2
2 [Taking out (+ + ) common from R3]
1
1
1
2
( )
1
2
0
2
2
0
2
( ) ( ) ( )
1
( ) ( ) ( ).1
2
Applying C2 C2 C1
C3 C3 C1
1
1
0
0
1
1
[Expanding along R3]
= (+ + ) (– ) (– ) (+ – – )
= (+ + ) (– ) (– ) (– )
–a
Example 30. Prove that
2
ba – b
ac
a2
Solution. Let
ab
ac
2
2 2 2
bc = 4 a b c
bc – c 2
ab
ac
2
bc
ba b
ac
Proved.
bc c 2
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Determinants and Matrices
241
a
Taking a, b, c common from R1, R2 and R3 respectively, we get
1
2 2 2
abc
1
1
1 1
1
2 2 2
a
a
b b
b
c
1 0 2
[Applying C2 C2 C1, C3 C3 C1 ]
1 2 0
0 2
= a2b2c2 (– 1) 2 0
[Expanding along R1]
= a2b2c2 (– 1) (0 – 4) = 4a2 b2 c2
Example 31. Show that
Solution. Let
c c
[Taking a, b, c common from C1, C2 and C3 respectively]
1 1 1
1 0 0
abc
abc
Proved.
3a
–a + b
–b + a
3b
–c + a
–c + b
–a + c
– b + c = 3(a + b + c) (ab + bc + ca)
3c
3a
a b a c
b a
3b
b c
c a
c b
3c
a b c a b a c
abc
Applying C1 C1 C2 C3, we get
abc
3b
b c
c b
3c
1 a b a c
(a b c) 1
3b
b c
1 c b
[Taking (a + b + c) common from C1]
3c
1 a b a c
= (a b c) 0 2b a b a [Applying R2 R2 R1 , R3 R3 R1 ]
0 c a 2c a
(a b c)
2b a
b a
[Expanding along C1]
c a 2 c a
= (a + b + c) [(2b + a) (2c + a) – (– b + a) (– c + a)]
= (a + b + c) {(4bc + 2ab + 2ca + a2 – (bc – ab – ac + a2)}
= (a + b + c) (3bc + 3ab + 3ca)
= 3 (a + b + c) (ab + bc + ca)
Proved.
Property (vi) If each element of a row (or column) of a determinant consists of the algebraic sum of
n terms, the determinant can be expressed as the sum of n determinants,
Let
a1 p1 q1
b1
c1
a 2 p2 q 2
b2
c2 .
a3 p3 q3
b3
c3
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242
Determinants and Matrices
= (a1 + p1 + q1) (b2c3 – b3c2) – (a2 + p2 + q2) (b1c3 – b3c1) + (a3 + p3 + q3) (b1c2 – b2c1)
= a1 (b2c3 – b3c2) – a2 (b1c3 – b3c1) + a3 (b1c2 – b2c1)
+ p1 (b2c3 – b3c2) – p2 (b1c3 – b3c1) + p3 (b1c2 – b2c1)
+ q1 (b2c3 – b3c2) – q2 (b1c3 – b3c1) + q3 (b1c2 – b2c1)
a1
b1
c1
b1
c1
a2 b2 c2 p2 b2 c2 q2 b2
c2
a3 b3
a a
Example 32. If
a a2
b b
2
c
2
c
c3
c1
p3 b3
q1
c3
q3 b3
Proved.
c3
3
b3 – 1 = 0, prove that abc = 1.
2
3
c –1
a3 1
3
b 1 0
3
c 1
1 a a2
b1
a –1
b b2
c c
Solution.
2
p1
abc 1 b b
2
1 c c
2
a a2
a3
a a2
b b
2
b
3
b b
2
1 0
c
2
c
3
c
2
1
c
c
1
a a2 1
b b
2
1 0
c c
2
1
(Taking out common a, b, c from R1, R2 and R3 from 1st determinant)
1 a a2
abc 1 b b
1 c
c
2
a 1 a2
b 1 b
2
c 1 c
2
2
1 a a2
abc 1 b b
1 c
c
( a b c 1)
2
(Interchanging C2 and C3)
1 a a2
1 b b
2
1 c
c
2
1
a
a
2
1
b
b
2
c
2
2
1
0
c
0
(Interchanging C1 and C2 )
0
abc 1 0
abc 1
Example 33. Show that
Proved.
b+c
c+a
a+b
a
b
c
q+r
r+ p
p+q =2 p
q
r
y+z
z+x
x+ y
y
z
x
Solution. The above determinant can be expressed as the sum of 8 determinants as given below:
bc ca ab
b c a
b a a
b c b
b a b
qr r p pq q r p q p p q r q q p q
y z
z x
x y
y
z
x
y
x
x
y
z
y
y
x
y
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Determinants and Matrices
243
c c
b c
a
c
a
a
c c b
r
r
p r
p
p r
z
z
x
x
x
z
a
r
q
z z y
c a b
q
r
p 000000 r
p
q
y
z
x
x
y
= (1)
2
z
a
b c
p
q
r (1)
x
y
z
2
a
b c
p
q
a
+
c
a
b
r
p
q
z
x
y
b c
r 2 p
q
r
x y z
x y z
0
Example 34. Prove that
Solution. Given determinant
The above determinant can be expressed as the sum of 8 determinants.
Proved.
Each of the 8 determinants has either two identical columns or identical rows.
Each of the resulting determinant is zero. Hence the result.
Example 35. Prove that
Solution.
x
l m 1
x n 1
x 1
1
Proved.
(x ) (x ) (x )
x l m 1
x l m 1
x n 1
0
x n 1
(C1 C1 – C4)
x 1
0
x 1
[On expanding by first column we get]
1
0
1
x n 1
x n 1
(x ) x 1 (x )
0
x 1
1
0
1
( x ) ( x ) ( x )
(C1 C1 – C3)
[On expanding by first column]
Proved.
Example 36. Show that x = – (a + b + c) is one root of the equation:
x+a
b
c
b
x+c
a
c
a
x+b
= 0 and solve the equation completely..
xabc
Solution. By C1 C1 + C2 + C3, we get
b
xabc xc
xabc
a
c
a
0
xb
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244
Determinants and Matrices
1
c
( x a b c) 1 x c
1
1
b
a
b
0
xb
c
( x a b c) 0 x b c
0
a
ab
ac
0, R2 R2 R1; R3 R3 R1
xbc
On expanding by first column, we get
(x + a + b + c) [(x – b + c) (x + b – c) – (a – b) (a – c)] = 0
(x + a + b + c) [x2 – (b – c)2 – (a2 – ac – ab + bc)] = 0
(x + a + b + c) (x2 – b2 – c2 + 2bc – a2 + ac + ab – bc] = 0
(x + a + b + c) (x2 – a2 – b2 – c2 + ab + bc + ca) = 0
Either x + a + b + c = 0
or
x2
x = – (a + b + c)
– a2 – b2 – c2 + ab + bc + ca = 0
x=
2
2
2
a b c ab bc ca
Hence, x = – (a + b + c) is one root of the given equation.
(b + c)2
Example 37. Find the value of
b
2
a2
Proved.
a2
2
b
c2
(a + b)2
c2
(b c)2 a 2
2
b b
Solution. By C1 – C3, C2 – C3, we get
2
(c + a)
2
a2 a2
2
(c a) b
c 2 (a b) 2
a2
2
b
c 2 (a b) 2
2
(a b)2
(a b c) (b c a)
0
a2
0
(a b c) (c a b)
b
2
(a b c) (c a b) (a b c) (c a b) (a b)2
On taking out (a + b + c) as common from 1st and 2nd column, we get
(a b c )
bca
0
a2
0
cab
b
2
cab
a b c
(a b c)
2
0
2b
2
c a b (a b ) 2
0
a2
2
a b c b R3 R3 (R1 R2 )
2a
2ab
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Determinants and Matrices
245
a b c
2 (a b c)
2
a2
0
2
0
b
abc b
a
ab
On expanding by first row, we get
= – 2 (a + b + c)2 [(– a + b + c) {– ab (a – b + c) – ab2} + a2 {0 – b (a – b + c)}]
= – 2 (a + b + c)2 [(– a + b + c) (– a2b – abc) – a2b (a – b + c)]
= – 2ab (a + b + c)2 [(– a + b + c) (– a – c) – a (a – b + c)]
= – 2ab (a + b + c)2 (a2 + ac – ab – bc – ac – c2 – a2 + ab – ac]
= – 2ab (a + b + c)2 (– bc – ac – c2)
= 2abc (a + b + c)2 (b + a + c)
= 2abc (a + b + c)3.
Ans.
a+x a – x a – x
Example 38. Using properties of determinants, solve for x :
a – x a+x a – x =0
a – x a– x a+x
ax ax ax
Solution. Given that
ax ax ax 0
ax ax ax
3a x a x a x
3a x a x a x 0
Applying C1 C1 C2 C3
3a x a x a x
1 ax ax
(3a x) 1 a x a x 0
1 ax ax
Now, R2 R2 R1 and R3 R3 R1,
1 ax ax
(3a x) 0
2x
0
0
0
2x
0
2
Expanding by C1, we get (3a x) (4x 0) 0
2
2
4x (3a x) 0
If 4 x 0, then x 0
If 3a x 0, then x 3a
Hence,
x=0
or 3a
Ans.
Example 39. Using properties of determinants, prove the following
Solution. Let
1+ a
1
1
1
1+ b
1
1
1
1+c
1 a
1
1
1 b
1
1
= abc 1 + 1 + 1 + 1
a b c
1
1
1c
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Determinants and Matrices
1 a
a
abc 1
b
1
c
1
a
1
b
1c
c
1 1 1
1
a b c
1
Operate : R1 R1 R2 R3 , abc
b
1
c
1
1
1
Taking 1
common from R1, we get
a
b
c
1
a
1b
b
1
c
1
1
1 1
a
a
a
1
1
1
1
abc
b
b
b
1
1
1
1
c
c
c
1 1 1
a b c
1
1
b
1
c
1
abc 1 1 1 1
a b c
Operate : C2 C2 C1 ; C3 C3 C1 ,
abc 1 1 1 1 (On expanding by R )
1
a b c
Example 40. Prove that :
Solution. Let
1
b
1
1
c
1 0 0
1 1 0
b
1
0 1
c
Proved.
a a
2
bc
b b
2
ac = (a – b)(b – c)(c – a)(ab + bc + ac).
c
2
ab
c
a a2
bc
2
ac
b b
c
c2
ab
a2
a3
2
1 b
abc
c2
b
3
c
3
a 2 b2
Operate : R1 R1 R2 ; R2 R2 R3,
2
b c
c
a2
abc
2
abc 1 . abc b
abc
abc
c2
2
3
b c
2
c
3
2
0
2
2
0
(a b) (b c). 1
c
3
1
c
3
1
1
2
2
2
2
a b a ab b
bc
3
1
2
c
b
0
3
a b a ab b
2
a3 1
a 3 b3 0
(a b) (b c) b c b bc c
Expand by C3
1 1 1
a b c
1
1
1
1 1 1
1
b
b
b
1
1
1
1
c
c
c
abc 1 1 1 1
a b c
1
b bc c
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Determinants and Matrices
247
2
ab
Operate : R2 R2 R1 (a b) (b c)
a ab b
2
2
2
c a b (c a) (c a )
(a b) (b c) (c a)
a b a 2 ab b2
1
b c a
= (a – b) (b – c) (c – a) [(a + b) (a + b + c) – 1 . (a2 + ab + b2)]
= (a – b) (b – c) (c – a) (ab + bc + ac).
Proved.
EXERCISE 4.4
Expand the following determinants, using properties of the determinants :
1 3 7
x a a
2
4 9 1
1.
Ans. 51.
2. Prove that a x a ( x 2a) ( x a) .
2 7 6
a a x
x3 a3 x2 x
a3
3
3
2
, x b, x c
3. Solve the equation b a b b 0, b c, bc 0
Ans. x
bc
3
3
2
c a c c
0
xa xb
xa
0
xc 0
4. Show that zero is one of the roots of the equation
x b x c
0
1
a
1
b
1
c
x
5. Without expanding the determinant, prove that
6. Without expanding the determinant, prove that :
8.
c
9.
a
1 x y
x2 y2
1 yz
y z
1 zx
2
2
2
2
z x
b
b
b
ca 0
c ab
y
yz
zx
z
x
y
1
1
1
x4
2x
2x
2x
x4
2x
2x
2x
x4
7. Using properties of determinant prove that :
a b 2c
a
c
b c 2a
a bc
0.
(5x 4) (4 x)
2
2(a b c)3
c a 2b
( x y) ( y z) ( z x).
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Determinants and Matrices
10.
2 2
bc
bc
c2a 2
ca
ca 0
2 2
ab
a
12.
14.
2
11.
ab a b
ab
abc
2a 3a 2b
3
4a 3b 2c a .
3a 6a 3b 10a 6b 3c
1 a a
2
1 b b
2
1 c
16.
1 a a 2 bc
b c
c2
1 a bc
1 b ca
1 c ab
13.
15.
abc
c
b
c
abc
a
b
a
abc
1 b b ca 0.
1 c c 2 ab
1
1
1
( ) ( ) ( ).
a2
bc
ac c 2
a 2 ab
b2
ac
ab
b 2 bc
c2
4a 2 b 2 c 2
2 (a b) (b c) (c a).
4.8
FACTOR THEOREM
If the elements of a determinant are polynomials in a variable x and if the substitution x = a
makes two rows (or columns) identical, then (x – a) is a factor of the determinant.
When two rows are identical, the value of the determinant is zero. The expansion of a determinant
being polynomial in x vanishes on putting x = a, then x – a is its factor by the Remainder theorem.
Example 41. Show that
1
1
1
x
y
z = (x – y)(y – z)(z – x)
2
2
2
x
y
z
Solution. If we put x = y, y = z, z = x then in each case two columns become identical and the
determinant vanishes.
(x – y), (y – z), (z – x) are the factors.
Since the determinant is of third degree, the other factor can be numerical only k (say).
1
x
1
y
2
2
1
z k ( x y ) ( y z ) ( z x)
... (1)
2
x
y
z
This leading term (product of the elements of the diagonal elements) in the given determinant
is yz2 and in the expansion
k (x – y) (y – z) (z – x) we get kyz2
Equating the coefficient of yz2 on both sides of (1), we have
k=1
Hence the expansion = (x – y) (y – z) (z – x).
Proved.
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Determinants and Matrices
249
1
1
2
2
Example 42. Factorize = a
b
3
1
c
3
2
3
a b c
Solution. Putting a = b, C1 = C2 and hence = 0.
a – b is a factor of .
Similarly b – c, c – a are also factors of .
(a – b) (b – c) (c – a) is a third degree factor of which itself is of the fifth degree as is
judged from the leading term b2c3.
The remaining factor must be of the second degree. As is symmetrical in a, b, c the
remaining factor must, therefore, be of the form k (a2 + b2 + c2) + l (ab + bc + ca)
= (a – b) (b – c) (c – a) {k (a2 + b2 + c2) + l (ab + bc + ca)}
If k 0, we shall get terms like a4b, b4c etc. which do not occur in . Hence, k must be zero.
= (a – b) (b – c) (c – a) {0 + l (ab + bc + ca)}
or
= l (a – b) (b – c) (c – a) (ab + bc + ca)
The leading term in = b2c3. The corresponding term on R.H.S = l b2c3
l =1
Hence,
= (a – b) (b – c) (c – a) (ab + bc + ca).
Example 43. Show that
Solution.
x
x2
x3
y
y2
y 3 xyz ( x y ) ( y z ) ( z x ).
z
z2
z3
x
x2
x3
y
y2
y 3 xyz 1 y
z
z2
z3
Example 44. Show that
1 x
1 z
x2
y 2 = xyz (x – y ) (y – z) (z – x) (see example 42).
z2
Proved.
x3
x2
x 1
3
2
1
2
1
3
Ans.
( x ) ( x ) ( x ) ( ) ( ) ( )
3 2 1
Solution. If we put x = ; x = ; x = ; = , = ; then two rows become identical and
the determinant vanishes.
(x – ) ; (x – ; (x – ; – ; (– ; – are the factors.
Since the determinant is of six degree the other factor can be numerical only say k.
x3
x2
x 1
3
2
1
3
2
1
3
2
1
k ( x ) ( x )( x ) ( ) ( ) ( )
The leading term is x3 . And in the expansion it is kx3 (– 2 ).
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250
Determinants and Matrices
k = –1 Hence the expansion = – (x – ) (x – (x – – (– –
Proved.
EXERCISE 4.5
2
3
a a 1 a
2
3
1. Evaluate, without expanding b b 1 b
2
3
c c 1c
2. Without expanding, show that
(a x)2 (a y)2
(b x)
2
(c x)2
(b y)
2
(c y) 2
Ans.
(a – b) (b – c) (c – a) (1 + abc)
(a z ) 2
(b z)
2
(c z)2
= 2 (a – b) (b – c) (c – a) (x – y) (y – z) (z – x).
3. Show (without expanding) that
bc a 2
b
2
c2
a2
bc ab ca
ab ca bc
ca bc ab
ab
ca b
c2
2
1
2
2
2
(ab bc ca)[(ab bc) (bc ca) (ca ab) ]
2
4.9
PIVOTAL CONDENSATION METHOD
The condensation process of reducing nth order determinant to (n – 1)th order determinant is
shown below :
a1 b1 c1 d1
a2 b2 c2 d 2
a3 b3 c3 d3
th
Consider n order determinant D a4 b4 c4 d 4
an bn cn dn
Add such a multiple of first column in the other columns so that at the places of b1, c1, d1 ......., we
get zero. Hence subtracting
columns respectively, we get
a1
0
b
a2
b2 1 . a2
a1
D
b1 c1 d1
,
, , ..., times the first column from the 2nd, 3rd, 4th...
a1 a1 a1
0
0
c2
c1
a2
a1
d2
d1
. a2
a1
a3
b3
b1
. a3
a1
c3
c1
a3
a1
d3
d1
. a3
a1
a4
b4
b1
. a4
a1
c4
c1
a4
a1
d4
d1
. a4
a1
an
b
bn 1 . an
a1
c
cn 1 . an
a1
d
d n 1 . an
a1
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Determinants and Matrices
251
b2
b1
. a2
a1
c2
c1
a2
a1
d2
d1
. a2
a1
b3
b1
. a3
a1
c3
c1
a3
a1
d3
d1
. a3
a1
D a1 b4
b1
. a4
a1
c4
c1
a4
a1
d4
d1
. a4
a1
b1
. an
a1
cn
bn
c1
. an
a1
dn
d1
. an
a1
Which is a determinant of (n – 1)th order. Now,
a1b2 b1a2
a1c2 c1a2
a1d2 d1a2
a1
a1
a1
a1b3 b1a3
a1
D a1
a1c3 c1a3
a1
a1b4 b1a4
a1
a1c4 c1a4
a1
1
(a1 )n 1
a1d 4 d1a4
a1
a1bn b1an
a1
D a1 .
a1d3 d1a3
a1
On expanding along
the first row
a1dn d1an
a1
a1cn c1an
a1
a1b2 b1a2
a1b3 b1a3
a1b4 b1a4
a1c2 c1a2
a1c3 c1a3
a1c4 c1a4
a1bn b1an
a1cn c1an
a1d2 d1a2
a1d3 d1a3
a1d 4 d1a4
a1dn d1an
1
as the determinant is of (n – 1)th order and a is common in every row (or column)
1
a1 b1 a1 c1 a1 d1
a2 b2 a2 c2 a2 d 2
1
(a1 )
n2
a1
b1
a1
c1
a1
d1
a3
b3
a3
c3
a3
d3
a1
b1
a1
c1
a1
d1
a4
b4
a4
c4
a4
d4
a1
b1
a1
c1
a1
d1
an
bn
an
cn
an
dn
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Determinants and Matrices
Thus, the nth order determinant is condensed to (n – 1)th order determinant. Repeated application
of this method ultimately results in a determinant of 2nd order which can be evaluated.
It is obvious that the leading element a1 behaves like a pivot in the condensation process
(i.e., reduction from n to (n – 1) and hence the method is pivotal condensation.
If the leading element is zero, it can be made non-zero by interchanging the columns.
Example 45. Condense the following determinants to second order and hence evaluate them:
2
1 3
5
10 2 3
4 2 7
6
D
5 12 15
(i)
(ii)
8
3 1
0
7 6 4
5
7 2 6
Solution. (i) Using the leading element as pivot, we get
D
32
(10)
1
10
D=
(ii)
1
1
(2)4 2
120 10 150 15
60 14 40 21
110 165
74
61
order = 3
55 2 3
11
11
344 11172 1892. Ans.
= 122 222 =
10 74 61
2
2
4 4 14 12
12 20
68
2 24
0 40
14 5
4 15
12 25
as the order is 4.
8 2
8
4 1
4
52 7 80 28
1
2 2
1
= 4 14 26 40 4 7 13 20
3 2 44 9
148 36
(4)
9 11 37
9 11 37
=
1 59 52
4 35 184
=
4 59 52
59 46 13 35 2259.
4 35 46
Ans.
0 4 1 2
5 3 7 8
Example 46. Condense and hence evaluate the determinant ,
4 1 2 3
1 2 5 5
Solution. As the leading element is zero, hence interchanging the 1st and second columns, we get
0 4 1 2
4 0 1 2
20 0 28 3 32 6
20 25 26
5 3 7 8
3 5 7 8 1 16 0 8 1 12 2 1 16 7 10
4 1 2 3 =
16
42
1 4 2 3
4 0 20 2 20 4
4 18 16
1 2 5 5
2 1 5 5
5 25 13
4 2
4 7 5 1 . 1 35 100 25 52 1 65 27 1 65 27 0.
16
2 5 90 25 40 13
10 65 27
10 65 27
1 18 8
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Determinants and Matrices
253
x 1
1
7 9 3
0 2 5
Example 47. By condensing the given determinant evaluate x, 2 x 2 6 8 3
2
1 1 0
Solution. D
x 1
1
7 9 3
0 2 5
2x 2 6 8 3
2
1 1 0
7
14
7
0
x 1
1
0.
9 3
2 5
6 2x 2 8 3
1
2
1 0
35
1
2
5
1
1
2 14 x 14 6 x 6 56 54 21 18
2 7 8 x 20 2
3
7
7
14 x 1
7 9
03
x 13 2 3
1
1
2
8 x 20 1
7
x 13 1
5
3
3
2 8 x 19 40 x 97
5 x 62
7 x 12
2 1 8 x 20 3 40 x 100
3 5 x 65
7 1 x 13
2
8 x 19 (5 x 62) (40 x 97) ( x 12)
7
2
40 x 2 95 x 496 x 1178 40 x 2 97 x 480 x 1164
7
Thus
4x + 4 = 0
x+1=0
2
[14 x 14] 4 x 4
7
x = –1
Ans.
EXERCISE 4.6
Using the leading element as pivots, condense the following determinants to second order and hence
evaluate them.
1 2 1 3
1 3 7
2 0 2
5 2 7
3 4 2 5
4 9 1
3 7 4
9 1 10
1.
2.
3.
4.
6 1 7 1
2 7 6
2 5 1
2 3 4
4 3 9 2
Ans. 51
Ans. 52
Ans. – 39
Ans. 75
1 2 3 4 5
4 2 3 0
3 7 2 1 1
1
0 2 7
9 4 1 2 3
5.
5 1 6 1 Ans. –1334
6.
8 1 3 7 2 Ans. –2276
2 3 5 4
4 2 0 3 1
7. Condense the following determinant and hence evaluate x,
3 2
1
5
4 7
6
2
0.
2 1 x 1 4
Ans. x = 4
5 3
4
1
4.10 CONJUGATE ELEMENTS
Two equidistant elements lying on a line perpendicular to the leading diagonal are said to be
conjugate.
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254
Determinants and Matrices
a1
In the determinant
b1
c1
a2 b2
c2 ,
a3
c3
b3
a2 , b1 ; a3 , c1 ;
b3 , c2 ; are pairs of conjugate elements.
4.11 SPECIAL TYPES OF DETERMINANTS
(i) Orthosymmetric Determinant. If every element of the leading diagonal is the same and
the conjugate elements are equal, then the determinant is said to be orthosymmetric determinant.
a
h
g
h
a
f
g
f
a
(ii) Skew-Symmetric Determinant. If the elements of the leading diagonal are all zero and
every other element is equal to its conjugate with sign changed, the determinant is said to be Skewsymmetric.
0 a b
a 0 c
b c
0
Property 1. A Skew-symmetric determinant of odd order vanishes.
0
–a –b
Example 48. Prove that Δ = a 0 – c = 0
b c
0
Solution. Taking out (– 1) common from each of the three columns
0
a b
3
(1) a 0 c
b c 0
0 a b
Changing rows into columns (1)3 a 0 c (1)3
b c
0
or
2 = 0 or = 0
Property 2. A skew-symmetric determinant of even order is a perfect square.
0
x
Example 49. Prove that
x
0
y
c
z
b
y c 0 a
z b a 0
Proved.
(ax by cz )2
0
ax
y z
0
c b
1 x
Solution. Multiplying column 2 by a the given determinant is
y
ac
0
a
a
On expanding by column 2, we get
z ab a 0
x c b
(ax by cz )
y 0 a
a
z a 0
(ax by cz )
a
x
c b
y
0 a
z
a 0
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Determinants and Matrices
ax ac ab
(ax by cz )
aa
255
(ax by cz )
y
0
a
z
a
0
a2
ax by cz
0
0
y
0
a
a2
z
ax by cz ac ac ab ab
(ax by cz )
y
0
a
z
a
0
R1 R1 bR2 cR3
( ax by cz )
a2
a 0
( ax by cz ) ( a 2 )
= (ax – by + cz)2
Proved.
4.12 LAPLACE METHOD FOR THE EXPANSION OF A DETERMINANT IN TERMS
OF FIRST TWO ROWS
(i) Make all possible determinants from first two rows by taking any two columns.
(ii) Multiply each of them by corresponding determinant which is left by suppressing the rows and
columns intersecting at them.
(iii) Add them with proper signs.
Here we count the number of movements of columns of the determinant by shifting to the
place of the first determinant. If the number of movement is odd then negative sign, if even then
positive sign.
a1 b1 c1 d1
a2 b2 c2 d 2
Example 50. Expand the determinant a b c d by Laplace method.
3
3
3
3
a4 b4 c4 d 4
Solution .
a1
a2
b1
c3
b2
b1
d1
a3
c3
b2
d2
a4
c4
d3
c4
d4
a1
a2
c1
b3
c2 b4
c1
d1
a3
b3
c2
d 2 a4
b4
d3
d4
a1
d1
b3
c3
a2
d2
b4
c4
a1
Explanation : a
2
b1
c1
a3
d3
b2
c 2 a4
d4
c1
c2
Now the c column being 3rd can be made 2nd by one movement of column; “a” column is in the
position of first column so that the total number of movements is one i.e. odd; hence the sign will be
–ve.
Ans.
a1 b1 0 0
a2 b2 0 0
Example 51. Expand the following determinant by Laplace method :
a3 b3 c3 d3
a4 b4 c4 d 4
a1 b1 c3 d3
a1 0 b3 d 3
Solution . a b c d a 0 b d
2
2
4
4
2
4
4
a1
0
b3
c3
a2
0
b4
c4
a1
b1
c3
d3
a2
b2
c4
d4
b1
0 a3
d3
b2
0 a4
d4
b1
0
a3
c3
b2
0
a4
c4
( a1b2 2 b1 ) (c3 d 4 c4 d 3 )
0 0 a3
b3
0 0 a4
b4
Ans.
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256
Determinants and Matrices
4.13 APPLICATION OF DETERMINANTS
Area of triangle. We know that the area of a triangle, whose vertices are (x1, y1), (x2, y2) and
(x3, y3) is given by
1
x ( y y3 ) x2 ( y1 y3 ) x3 ( y1 y2 )
2 1 2
x1
y2 1
y1 1
y1 1
1
1
x1
x2
x3
2 x2
2
y3 1
y3 1
y2 1
x3
y1 1
y2 1
y3 1
Note. Since area is always a positive quantity, therefore we always take the absolute value of
the determinant for the area.
Condition of collinearity of three points. Let A (x1, y1), B (x2, y2) and C (x3, y3) be three
points. Then,
A, B, C are collinear
area of triangle ABC 0
x1 y1 1
x1 y1 1
1 x2 y2 1 0
x2 y2 1 0
Proved.
2
x3 y3 1
x3 y3 1
Example 52. Using determinants, find the area of the triangle with vertices (– 3, 5), (3, – 6)
and (7, 2).
1
Solution. The area of the given triangle
2
3
5 1
3 6 1
7
2 1
6 11 0
1 4 8 0
Operate : R1 R1 R2 ; R2 R2 R3
2
7
2 1
6
11
1
1
Expand by C3 2 .1. 4 8 2 (48 44) 46 sq. units
Ans.
Example 53. Using determinants, show that the points (11, 7), (5, 5) and (– 1, 3) are collinear.
Solution. The area of the triangle formed by the given points 1
2
11 7 1
5 5 1
1 3 1
Operate : R1 R1 R2 ; R2 R2 R3
1
2
6 2 0
6 2 0 1 .0 0.
2
1 3 1
( R1 and R2 are identical)
The three given points are collinear.
Proved.
Example 54. Using determinants, find the area of the triangle whose vertices are (1, 4) (2, 3)
and (– 5, – 3). Are the given points collinear?
Solution. Area of the required triangle
1
2
1
4 1
2
3 1
5 3 1
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Determinants and Matrices
257
1
1
13
[1(3 3) 4 (2 5) 1( 6 15)] (6 28 9)
0
2
2
2
Hence, the given points are not collinear.
Ans.
EXERCISE 4.7
Using determinants, find the area of the triangle with vertices:
1. (2,– 7), (1, 3), (10, 8). Ans. Area
95
2
2. (– 2, 4), (2, – 6) and (5, 4). Ans. Area
= 35
3. (– 1, – 3), (2, 4) and (3, – 1). Ans. Area = 11 4. (1, – 1), (2, 4) and (– 3, 5). Ans. Area =
13
5. Using determinants, show that the points (3, 8), (– 4, 2) and (10, 14) are collinear.
6. Find the value of , so that the points (1, – 5), (– 4, 5) and (, 7) are collinear.
Ans. = –5
7. Find the value of x, if the area of is 35 square cms with vertices (x, 4), (2, – 6), (5, 4).
Ans. x = – 2, 12
8. Using determinants find the value of k, so that the points (k, 2 – 2k), (– k + 1, 2k) and
(– 4 – k, 6 – 2 k) may be collinear.
1
2
Ans.x = 3
Ans. x = 1
Ans. k = –1,
9. If the points (x, – 2), (5, 2) and (8, 8) are collinear, find x using determinants.
10. If the points (3, – 2), (x, 2) and (8, 8) are collinear, find x using determinants.
4.14. SOLUTION OF SIMULTANEOUS LINEAR EQUATIONS BY DETERMINANTS
(CRAMER’S RULE)
Let us solve the following equations.
a1 x + b1 y + c1 z = d1
a2 x + b2 y + c2 z = d2
Let
a3 x + b3 y + c3 z = d3
a1 b1 c1
D a2 b2 c2
b1
c1
or x D a2 x b2
a1 x
c2
a3 b3 c3
a3 x b3 c3
Multiplying the 2nd column by y and 3rd column by z and adding to the 1st column, we get
b1
c1
x D a2 x b2 y c2 z b2
a1x b1 y c1 z
c2
a3 x b3 y c3 z b3
c3
d1
d1 b1 c1
d 2 b2 c2
x
d3
a1
b1
b1
c3
D
1
D
c1
a2 b2 c2
a3 b3 c3
b1
c1
x D d2 b2 c2
d3 b3 c3
Similarly,
y
D2
D
a1
d1
c1
a2
a3
d 2 c2
d3 c3
a1 b1 c1
a2 b2 c2
a3
b3
c3
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258
Determinants and Matrices
a1 b1 d1
a2 b2 d2
a b d3
D3
3 3
D
a1 b1 c1
z
a2 b2 c2
a3 b3 c3
D1
D
D
,
y 2,
z 3
D
D
D
Example 55. Solve the following system of equations using Cramer’s rule :
5x – 7y + z = 11
6x – 8y – z = 15
x
Ans.
3x + 2y – 6z = 7
Solution. The given equations are
5x 7 y z 11
6x 8 y z 15
3x 2 y 6 z 7
5 7
Here,
D 6
8
1
1
= 5 (48 + 2) + 7 (– 36 + 3) + 1 (12 + 24) = 55 ( 0)
3
2 6
11 7
1
D1 15 8
1
7
2 6
5 11
1
D2 6 15
1
= 5 (– 90 + 7) – 11 (– 36 + 3) + 1 (42 – 45) = – 55
3 7 6
5 7 11
D3 6
8 15
3
2
= 11 (48 + 2) + 7 (– 90 + 7) + 1 (30 + 56) = 55
= 5 (– 56 – 30) + 7 (42 – 45) + 11 (12 + 24) = – 55
7
D1 55
D
55
1,
y 2
1 ,
D 55
D
55
y = – 1,
z=–1
By Cramer’s Rule x
Hence, x = 1,
z
D3 55
1
D
55
Ans.
Example 56. Solve, by determinants, the following set of simultaneous equations :
5x – 6y + 4z = 15
7x + 4y – 3z = 19
2x + y + 6z = 46
5 6
Solution.
D 7
2
4
4 3 419
1
6
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Determinants and Matrices
15 6
D1 19
46
259
5 15
4
Hence, x = 3,
2 46
6
By Cramer’s Rule: x
D1 1257
3,
D
419
y = 4,
5 6 15
D2 7 19 3 1676
4 3 1257 ,
1
4
y
6
,
D3 7
4 19 2514
2
D2 1676
4,
D
419
z
1 46
D3 2514
6.
D
419
z=6
Ans.
Example 57. Solve the following system of equations using Cramer’s Rule :
2x – 3y + 4z = – 9
– 3x + 4y + 2z = – 12
4x – 2y – 3z = – 3
Solution. The given equations are
2x 3y 4z 9
3x 4 y 2z 12
4 x 2 y 3z 3
Here
2 3
D 3
4
4
2
=2 (– 12 + 4) + 3 (9 – 8) + 4 (6 – 16) = – 53
4 2 3
9 3
4
D1 12
4
2
3 2 3
2
9
4
D2 3 12
2
4
2
D3 3
3 3
3
=2 (36 + 6) + 9 (9 – 8) + 4 (9 + 48) = – 321
9
4 12
4 2
=– 9 (– 12 + 4) + 3 (36 + 6) + 4 (24 + 12) = – 342
3
By Cramer’s Rule,
D
342 342
x 1
,
D
53
53
=2 (– 12 – 24) + 3 (9 + 48) – 9 (6 – 16) = – 189
y
D2
321 321
,
D
53
53
z
D3 189 189
D
53
53
342
y 321 ,
z 189
Hence, x 53 ,
53
53
Example 58. Solve the following system of equations by using determinants :
Ans.
x+ y+z=1
ax + by + cz = k
2
2
2
a x+b y+c z=k
Solution. We have D
1
1
1
a
b
c
2
2
a
b
c
2
2
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260
Determinants and Matrices
1
a
a
2
0
ba
2
b a
2
0
c a [Applying C2 C2 C1 and C3 C3 C1]
2
c a
(b a) (c a)
2
1
0
0
a
1
1
a
2
(b a) (c a).1.
ba ca
1
1
ba ca
= (b – a) (c – a) (c + a – b – a)
= (b – c) (c – a) (a – b)
D1
k
D2
and
1
1
k
b
c (b c) (c k ) (k b)
2
2
2
b
c
1
1
1
a
k
c (k c) (c a) (a k )
2
2
2
a
D3
1
k
c
1
1
1
a
b
k (a b) (b k ) (k a)
2
2
2
a
b
k
[Expanding along R1]
...(1)
[Replacing a by k in (1)]
[Replacing b by k in (1)]
[Replacing c by k in (1)]
D1 (b c) (c k ) (k b)
D
(k c) (c a) (a k )
, y 2
D (b c) (c a) (a b)
D
(b c) (c a) (a b)
D
(a b) (b k ) (k a)
z 3
and
D
(a b) (b c) (c a)
(c k ) (k b)
(k c) (a k )
(b k ) (k a)
x
, y
and z
Hence,
(c a) (a b)
(b c) (a b)
(b c) (c a) Ans.
Example 59. The sum of three numbers is 6. If we multiply the third number by 2 and add the
first number to the result, we get 7. By adding second and third numbers to three times the first
number we get 12. Use determinants to find the numbers.
Solution. Let the three numbers be x, y and z. Then, from the given conditions, we have
x y z 6
x yz6
x 2z 7 or
x 0. y 2 z 7
3x y z 12
3x y z 12
x
Here,
1 1 1
D 1 0 2 1(0 2) 1(1 6) 1(1 0) 2 5 1 4
3 1 1
6 1 1
D1
7 0 2 6 (0 2) 1(7 24) (7 0) = – 12 + 17 + 7 = 12
1 1
12
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Determinants and Matrices
1
6
261
1
D2 1
7 2 1(7 24) 6 (1 6) 1(12 21) = – 17 + 30 – 9 = 4
3 12 1
1 1
6
7 1(0 7) 1(12 21) 6(1 0) = – 7 + 9 + 6 = 8
3 1 12
D
D
D
x 1 12 3, y 2 4 1, and z 3 8 2
D
4
D
4
D 4
Thus, the three numbers are 3, 1 and 2.
and
D3 1 0
Ans.
EXERCISE 4.8
Using Cramer’s Rule, solve the following system of equations :
1. 2x – 3y = 7
7x – 3y = 10
4.
7.
10.
13.
2. 2x + y = 1
x – 2y = 8
3
29
Ans. x 5 , y 15
5x + 2y = 3
5.
3x + 2y = 5.
Ans. x = – 1, y = 4
x – 4y – z = 11
8.
2x – 5y + 2z = 39
– 3x + 2y + z = 1.
Ans. x = – 1, y = – 5, z = 8
x+y+z=1
11.
3x + 5y + 6z = 4
9x + 2y – 36x = 17
1
1
Ans. x 3 , y 1, z 3
x+y+z=1
x + 2y + 3z = k
3. 2x + 3y = 10
x + 6y = 4.
16
2
Ans. x 3 , y 9
7x – 2y = – 7
6. x – 2y = 4
2x – y = 1.
– 3x + 5y = – 7
Ans. x = – 3, y = – 7
Ans. x = – 6, y = – 5
x + 3y – 2z = 5
9. x + 2y + 5z = 23
2x + y + 4z = 8
3x + y + 4z = 26
6x + y – 3z = 5.
6x + y + 7z = 47
Ans. x = 1, y = 2, z = 1 Ans. x = 4, y = 2, z = 3
2y – z = 0
12. x + y + z = – 1
x + 3y = – 4
x + 2y + 3z = – 4
3x + 4y = 3
x + 3y + 4z = – 6
Ans. x 2, y 3
Ans. x = 5, y = – 3, z = – 6 Ans. x = 1, y = – 1, z = – 1
(2 k ) (3 k )
(1 k ) (3 k )
(1 k ) (2 k )
, y
, z
2
1
2
14. Show that there are three real values of for which the equations:
(a – ) x + by + cz = 0
bx + (c – ) y + az = 0
cx + ay + (b – ) z = 0
a b c
12x + 22y + 32z = k2 Ans. x
are simultaneously true, and that the product of these values of is
b c a
c a b
15. Solve the following system of equations by using the Cramer’s Rule
x1 + x2 = 1; x2 + x3 = 0; x3 + x4 = 0; x4 + x5 = 0; x5 + x1 = 0 (A.M.I.E.T.E., Summer 2005)
1
1
1
1
1
Ans. x1 , x2 , x3 , x4 , x5
2
2
2
2
2
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Determinants and Matrices
4.15 RULE FOR MULTIPLICATION OF TWO DETERMINANTS
Multiply the elements of the first row of 1 with the corresponding elements of the first, the
second and the third row of 2 respectively.
Their respective sums form the elements of the first row of 12. Similarly multiply the
elements of the second row of 1 with the corresponding elements of first, second and third row of 2
to form the second row of 12 and so on.
a
Example 60. Find the product
c
c
b
a b
a
c
b
Solution. Product of the given determinants
a11 b11 c11
a1 2 b12 c1 2
a1 3 b13 c1 3
a21 b21 c2 1 a2 2 b22 c2 2
a2 3 b23 c2 3
a31 b31 c31
a3 2 b32 c3 2
a b c
Example 61. Show that
–a
Ans.
a33 b33 c3 3
c b
b c a × –b a c
c a b
–c b a
2bc a
c
2
2
c2
b2
2ca b
b2
2
a2
a
2
3
3
3
(a b c 3abc)
2
2ab c 2
Solution. Product of the given determinants
a b c
a c b
a 2 bc bc ab ab c 2
2
ac b2 ac
2
b c a b a c ab c ab b ac ac
c a b
c b a
ca ca b2
c
2
b
2
Now,
b a c
(1)
2
a
a b c
2
c b a
c a b
b2
2
a
2
2ab c
... (1)
2
b c a
c
= a (bc –
a b c
b c a
c2
2ca b
a c b
2
bc a 2 bc c 2 ab ab
2bc a 2
bc bc a
a2)
a b
– b (b2 – ac) + c (ab – c2) = – (a3 + b3 + c3 – 3abc)
a c b
b a c = (a3 + b3 + c3 – 3abc)2
c b a
From (1) and (2), we get the required result.
... (2)
Proved.
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Determinants and Matrices
263
Example 62. Prove that the determinant
2b1 + c1
c1 + 3a1
2a1 + 3b1
2b2 + c2
c2 + 3a2
2a2 + 3b2
2b3 + c3
is a multiple of the determinant
c3 + 3a3
2a3 + 3b3
a1 b1
c1
a2 b2 c2
a3 b3
2b1 c1
Solution.
c1 3a1
2a1 3b1
2b2 c2 c2 3a2
2a2 3b2
2b3 c3
2a3 3b3
a1
c3 3a3
b1
c1
a2 b2
0 2 1
c2 3 0 1
a3 b3
Example 63. Prove that
and find the other factor..
c3
c3
cos
cos
cos
cos
cos
cos
cos sin 0
Solution.
cos sin 0
sin 0 cos
sin
0 0
cos
sin
sin
0
0
cos
cos cos sin sin cos cos sin sin
2
2
cos cos sin sin
cos sin
cos cos sin sin
cos cos sin sin
1
or
cos
cos 2 sin 2
or
Ans.
2 3 0
cos cos sin sin 0
cos2 sin 2
cos ( ) cos ( )
cos ( )
1
cos ( ) 0
Proved.
cos ( ) cos ( )
1
Example 64. If A1, A2, A3, B1, B2, B3, C1, C2, C3 are cofactors of the elements a1, a2, a3, b1, b2,
a1 b1 c1
b3, c1, c2, c3 respectively of the determinant
a2 b2
c2
a3 b3
c3
, show that
A1
A2
B1 C1
a1 b1
B2 C2 = a2 b2
c1
c2
A3
B3 C3
c3
a3 b3
2
(Try Yourself)
4.16 CONDITION FOR CONSISTENCY OF A SYSTEM OF SIMULTANEOUS
HOMOGENEOUS EQUATIONS
Case I For a system of homogeneous equations.
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Determinants and Matrices
a11 x a12 y a13 z 0
a11
a21 x a22 y a23 z 0
a31 x a32 y a33 z 0 ,
a12
a13
D a21 a22
a23
a31 a32 a33
1. If D 0, then the system of equations are consistent with infinite solutions.
2. If D 0, then the system of equations is consistent with trivial solution.
Example 65. Find values of , for which the following system of equations is consistent and
has nontrivial solutions:
(– 1) x + (3 + 1) y +
2z = 0
(– 1) x + (4 – 2) y + (+ 3) z = 0
2x + (3 + 1) y + 3 (– 1) z = 0
Solution.
(– 1) x + (3+ 1) y +
(– 1) x + (4 – 2) y +
2x + (3 + 1) y +
This is a system of homogeneous equations.
2z=0
(+ 3) z = 0
3 (– 1) z = 0
For infinite solutions,
1 3 1
D 1 4 2
2
0
0
3
3
3 0
3 1 3 3
0
1 5 1
2
3 0
3 1 3 3
1 4 2
2
2
[ R1 R1 R2 ]
3
3 0
6 2 3 3
[C2 C2 + C3)
(– 3) [(– 1) (6 – 2) – 2 (5 + 1)] = 0 [6 2 – 8 + 2 – 10 – 2] = 0
6 2 – 18 = 0 6 (– 3) = 0 = 3, 0
Ans.
4.17 FOR A SYSTEM OF THREE SIMULTANEOUS LINEAR EQUATIONS WITH
THREE UNKNOWNS
(i) If D 0, then the given system of equations is consistent and has a unique solution given
D
D
D
by x 1 , y 2 , and z 3 .
D
D
D
(ii) If D = 0 and D1 = D2 = D3 = 0, then the given system of equations is consistent, and it has
infinitely many solutions.
(iii) If D = 0 and at least one of the determinants D1, D2, D3 is non zero, then the given system
of equations is inconsistent.
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Determinants and Matrices
265
Equations with three unknowns
D0
Consistent with unique
solution
D=0
D1 = D2 = D3 = 0
Consistent with infinitely
many solutions
D1 or D2 or D3 0
Inconsistent
Example 66. Test the consistency of the following equations and solve them if possible:
3x + 3y + 2z = 1, x + 2y = 4, 10y + 3z = – 2
Solution. The system of equations is
3x + 3y + 2z = 1
x + 2y + 0z = 4
0x + 10y + 3z = – 2
Therefore
3
3 2
D 1
2 0
0 10 3
= 3 (6 – 0) – 3 (3 – 0) + 2 (10 – 0)
= 18 – 9 + 20 = 29 0
Since D 0, so the system of simultaneous equations is consistent with unique solution. Now
let us solve the system of equation.
D1
1
3 2
4
2 0 1(6 0) 3(12 0) 2(40 4)
2 10 3 6 36 88 58
3
D2 1
1 2
4 0 3(12 0) 1(3 0) 2 ( 2 0)
0 2 3 36 3 4 29
3
D3 1
3
2
1
4 3( 4 40) 3( 2 0) 1 (10 0)
0 10 2 132 6 10 116
By Cramer’s Rule
D1 58
D
D
116
2, y 2 29 1, z 3
4
D 29
D 29
D
29
Hence x = 2, y = 1, z = – 4.
x
Ans.
Example 67. Show that the system of equations
2x + 6y = – 11, 6x + 20y – 6z = – 3, 6y – 18z = –1 is not consistent.
Solution.
2x + 6y + 0z = – 11
6x + 20y – 6z = – 3
0x + 6y – 18z = – 1
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Determinants and Matrices
2
6
0
D 6 20
6
0
11
D1
=2 (– 360 + 36) – 6 (– 108) = – 648 + 648 = 0
6 18
6
0
3 20
6
1
6 18
= – 11 (– 360 + 36) – 6 (54 – 6) = 3564 – 288 = 3276
Here D = 0 and D1 0
Hence the system of equations is not consistent.
Proved
EXERCISE 4.9
Find, whether the following system of equations is consistent or inconsistent. If consistent solve
them.
1. Find the value of k, for which the following system of equations
3x1 – 2x2 + 2x3 = 3, x1 + kx2 – 3x3 = 0, 4x1 + x2 + 2x3 = 7 is consistent.
1
Ans. k ,
4
2. Find the value of , for which the system of equations
7
x + y + 4z = 1, x + 2y – 2z = 1, x + y + z = 1 will have a unique solution. Ans. 10
3. For what values of and , the following system of equations
2x + 3y + 5z = 9, 7x + 3y – 2z = 8, 2x + 3y + z = will have
(i) unique solution;
(ii) no solution.
Ans. (i) 5 (ii) 5, 9
3 2 1 x b
5 8 9 y 3
4. Determine the values of a and b for which the system
2
1 a z 1
(i) has a unique solution, (ii) has no solution and (iii) has infinitely many solutions.
1
1
Ans. (i) a 3 (ii) a 3, b 3 (iii) a 3, b 3
5. Find the condition on for which the system of equations
3x – y + 4z = 3, x + 2y – 3z = – 2, 6x + 5y + z = – 3
has a unique solution. Find the solution for = – 5.
5k 4
13k 9
Ans. 5, x 7 7 , y 7 7 , z k
EXERCISE OF OBJECTIVE QUESTIONS
Choose the Correct Answers :
1.
(a) 52
52
53
54
The value of 53
54
55
54
56
57
(b) 0
is
(c) 513
(d) 59
Ans. (b)
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Determinants and Matrices
a1
b1
c1
2. If a2 b2
a3 b3
c2
b2 c3 b3c2
= 5 then the value of b3c1 b1c3
b1c2 b2 c1
c3
(a) 5
1 ax
267
(b) 25
1 bx
a3c2 a2 c3
a1c3 a3c1
a2 c1 a1c2
(c) 125
a2 b3 a3b2
a1b1 a1b3 is
a1b2 a2 b1
(d) 0
Ans. (b)
1 cx
3. If 1 a1x 1 b1x 1 c1x = A0 A1x A2 x2 A3 x3 , then A1 is equal to
1 a2 x 1 b2 x 1 c2 x
(a) abc
(b) 0
(c) 1
(d) none of these
1
n
2n
4. If 1, , 2 are the cube roots of unity, then n
2 n
1
2n
(a) 0
(c)
(b) 1
xp y
5. The determinant yp z
0
(a) x, y, z are in A.P.
(c) x, y, z are in H.P.
6. If the determinant
1
x
y
y
z
Ans. (b)
is equal to
n
(d) 2
Ans. (a)
= 0 if
xp y yp z
(b) x, y, z are in G.P.
(d) xy, yz, zx are in A.P.
a
b
2a 3b
b
c
2b 3c 0, then
2a 3b 2b 3c
Ans. (b)
0
(b) is root of 4ax 2 12bx 9c 0 or a, b, c are in G.P.
(d) a, b, c are in A.P.
Ans. (a)
(a) a, b, c are in H.P.
(c) a, b, c are in G.P. only
log l
p 1
7. If l, m, n are the p , q and r term of a G.P. all positive, then log m q 1 equals
th
th
th
log n
(a) 3
(b) 2
8. If the system of linear equations
x 2ay az 0
r
1
(c) 1
(d) zero
Ans. (d)
(c) are in H.P.
(d) satisfy a 2b 3c 0
Ans. (c)
x 3by bz 0
x 4cy cz 0
has a non-zero solution, then a, b, c
(a) are in A.P.
(b) are in G.P.
9. If , and are the roots of the equation x 3 px q 0 , then the value of the
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Determinants and Matrices
determinant is
(a) q
(b) 0
(d) p2 2q .
(c) p
10. The number of values of k for which the system of equations
(k 1) x 8y 4k ,
kx (k 3) y 3k 1
has infinitely many solutions is
(a) 0
(b) 1
(c) 2
Ans. (a)
(d) infinite
Ans. (b) [Hint : Here 0 for k = 3, 1, x 0 for k = 2, 1, y 0 for k = 1.
Hence k = 1. Alternatively, for infinitely many solutions the two equations become
identical
k 1
8
4k
k
k 3 3k 1
k = 1]
11. The system x y 0 , y z 0 , z x 0 has infinitely many solutions when
(a) 1
(b) 1
(c) 0
(d) no real value of
1
Ans. (b) [Hint :
0
0
1 0 . Solve for .]
0
1
12. If the system of equations x ky z 0 , kx y z 0 , x y z 0 has a non-zero
solution, then the possible values of k are
(a) – 1, 2
(b) 1, 2
(c) 0, 1
(d) – 1, 1
Ans. (d)
13. The value of for which the system of equations 2 x y 2z 2 , x 2 y z 4 ,
x y z 4 has no solution is
(a) 3
(b) – 3
(c) 2
(d) – 2
2 1 2
1 2 1
Ans. (b) [Hint : The Coefficient determinant =
3 9 .
1 1
For no solution the necessary condition is –3 – 9 = 0
= –3
For = – 3, there is no solution for the given system of equations].
14. If the system of equations x 2 y 3z 1 , ( 3) z 3 , (2 1) x z 0 is inconsistent,
then the value of is equal to
1
(a)
(b) – 3
(c) 2
(d) 0
Ans. (b)
2
a 0 1
a 1 1
15. A 1 c b , B 0
1 d b
f
f
a2
c d , U g , V 0 . If there is a vector matrix X such that
h
g h
0
AX = U has infinitely many solutions, then prove that BX = V cannot have a unique solution. If
a f d 0 , prove that BX = V has no solution.
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269
4.18
MATRICES
Let us consider a set of simultaneous equations,
x+2y+3z+5t=0
4x+ 2y+ 5z+ 7 t= 0
3 x + 4 y + 2 z + 6 t = 0.
Now we write down the coefficients of x, y, z, t of the above equations and enclose them within
brackets and then we get
1 2 3 5
A = 4 2 5 7
3 4 2 6
The above system of numbers, arranged in a rectangular array in rows and columns and bounded
by the brackets, is called a matrix.
It has got 3 rows and 4 columns and in all 3 × 4 = 12 elements. It is termed as 3 × 4 matrix,
to be read as [3 by 4 matrix]. In the double subscripts of an element, the first subscript determines
the row and the second subscript determines the column in which the element lies, aij lies in the ith
row and jth column.
4.19
VARIOUS TYPES OF MATRICES
(a) Row Matrix. If a matrix has only one row and any number of columns, it is called a Row
matrix, e.g.,
[2 7 3 9]
(b) Column Matrix. A matrix, having one column and any number of rows, is called a Column
1
matrix, e.g., 2
3
(c) Null Matrix or Zero Matrix. Any matrix, in which all the elements are zeros, is called a
Zero matrix or Null matrix e.g.,
0 0 0 0
0 0 0 0
(d) Square Matrix. A matrix, in which the number of rows is equal to the number of columns,
is called a square matrix e.g.,
2 5
1 4
(e) Diagonal Matrix. A square matrix is called a diagonal matrix, if all its non-diagonal elements
are zero e.g.,
1 0 0
0 3 0
0 0 4
(f ) Scalar matrix. A diagonal matrix in which all the diagonal elements are equal to a scalar,
say (k) is called a scalar matrix.
For example;
0
0
0
6
2 0 0
0
0
0 2 0 , 0 6
0
0 6
0
0 0 2
0
0 6
0
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Determinants and Matrices
0, when i j
i.e., A = [aij]n × n is a scalar matrix if aij =
k , when i j
(g) Unit or Identity Matrix. A square matrix is called a unit matrix if all the diagonal elements
are unity and non-diagonal elements are zero e.g.,
1 0 0
0 1 0 , 1 0
0 1
0 0 1
(h) Symmetric Matrix. A square matrix will be called symmetric, if for all values of i and j,
aij = aji i.e., A = A
a h g
h b f
e.g.,
g f c
(i) Skew Symmetric Matrix. A square matrix is called skew symmetric matrix, if
(1) aij = – aji for all values of i and j, or A = –A
(2) All diagonal elements are zero, e.g.,
0 h g
h 0 f
g f
0
(j) Triangular Matrix. (Echelon form) A square matrix, all of whose elements below the
leading diagonal are zero, is called an upper triangular matrix. A square matrix, all of
whose elements above the leading diagonal are zero, is called a lower triangular matrix
e.g.,
1 3 2
2 0 0
0 4 1
4 1 0
0 0 6
5 6 7
Upper triangular matrix
Lower triangular matrix
(k) Transpose of a Matrix. If in a given matrix A, we interchange the rows and the
corresponding columns, the new matrix obtained is called the transpose of the matrix A and
is denoted by A or AT e.g.,
2 3 4
2 1 6
A = 1 0 5 , A 3 0 7
6 7 8
4 5 8
(l) Orthogonal Matrix. A square matrix A is called an orthogonal matrix if the product of the
matrix A and the transpose matrix A’ is an identity matrix e.g.,
A. A = I
if | A | = 1, matrix A is proper.
(m) Conjugate of a Matrix
Let
4
1 i 2 3i
A=
i
3 2 i
7 2 i
Conjugate of matrix A is A
4
1 i 2 3i
A =
i
3 2 i
7 2 i
(n) Matrix A. Transpose of the conjugate of a matrix A is denoted by A.
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Determinants and Matrices
271
4
1 i 2 3i
A=
i
3 2 i
7 2 i
4
1 i 2 3i
A =
7
2
i
i
3
2 i
1 i 7 2 i
2 3 i
i
( A ) =
4
3 2 i
1 i 7 2 i
2 3 i
i
A =
4
3 2 i
(o) Unitary Matrix. A square matrix A is said to be unitary if
A A = I
Let
1 i 1 i
1 i 1 i
2
2
2
2
A
e.g.
A=
,
, A A I
1
i
1
i
1
i
1
i
2
2
2
2
(p) Hermitian Matrix. A square matrix A = (aij) is called Hermitian matrix, if every i-jth
element of A is equal to conjugate complex j-ith element of A.
In other words,
aij = a ji
2 3i 3 i
1
2 3 i 2
1 2 i
3 i
1 2 i 5
e.g.
Necessary and sufficient condition for a matrix A to be Hermitian is that A = A i.e. conjugate
transpose of A
A = ( A) .
(q) Skew Hermitian Matrix. A square matrix A = (aij) will be called a Skew Hermitian matrix
if every i-jth element of A is equal to negative conjugate complex of j-ith element of A.
In other words,
aij = a j i
All the elements in the principal diagonal will be of the form
aii = aii
or
aii aii 0
If
aii = a + ib
then aii a ib
(a + ib) + (a – ib) = 0
2a=0a=0
So, aii is pure imaginary aii = 0.
Hence, all the diagonal elements of a Skew Hermitian Matrix are either zeros or pure imaginary.
i
2 3 i 4 5 i
(2 3 i )
0
2i
e.g.
2i
3 i
(4 5 i )
The necessary and sufficient condition for a matrix A to be Skew Hermitian is that
A = – A
( A ) = – A
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Determinants and Matrices
A2
(r) Idempotent Matrix. A matrix, such that = A is called Idempotent Matrix.
2 2 4
2 2 4 2 2 4 2 2 4
1 3 4 , A2 1 3 4 1 3 4 1 3 4 A
e.g. A =
1 2 3
1 2 3 1 2 3 1 2 3
(s) Periodic Matrix. A matrix A will be called a Periodic Matrix, if
Ak+1 = A
where k is a +ve integer. If k is the least + ve integer, for which Ak+1 = A, then k is said to
be the period of A. If we choose k = 1, we get A2 = A and we call it to be idempotent
matrix.
(t) Nilpotent Matrix. A matrix will be called a Nilpotent matrix, if Ak = 0 (null matrix) where
k is a +ve integer ; if however k is the least +ve integer for which Ak = 0, then k is the index
of the nilpotent matrix.
ab
b 2 2 ab
b 2 ab
b 2 0 0
,
A
e.g., A =
0
2
2
2
a ab
a ab a ab 0 0
A is nilpotent matrix whose index is 2.
(u) Involuntary Matrix. A matrix A will be called an Involuntary matrix, if A2 = I (unit
matrix). Since I2 = I always Unit matrix is involuntary.
(v) Equal Matrices. Two matrices are said to be equal if
(i) They are of the same order.
(ii) The elements in the corresponding positions are equal.
2 3
2 3
A = 1 4 , B 1 4
Here
A=B
(w) Singular Matrix. If the determinant of the matrix is zero, then the matrix is known as
Thus if
1 2
singular matrix e.g. A =
is singular matrix, because |A| = 6 – 6 = 0.
3 6
Example 1. Find the values of x, y, z and ‘a’ which satisfy the matrix equation.
x 3 2 y x 0 7
z 1 4 a 6 3 2a
Solution. As the given matrices are equal, so their corresponding elements are equal.
x+3=0
x=–3
2y + x = – 7
z– 1=3
z=4
4 a – 6= 2 a
a=3
Putting the value of x = – 3 from (1) into (2), we have
2y – 3 = – 7
y=–2
Hence,
x = – 3, y = – 2, z = 4, a = 3
...(1)
...(2)
...(3)
...(4)
Ans.
4.20 ADDITION OF MATRICES
If A and B be two matrices of the same order, then their sum, A + B is defined as the matrix,
each element of which is the sum of the corresponding elements of A and B.
5
4 2
1 0 2
Thus if
A = 1 3 6 , B 3 1 4
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Determinants and Matrices
273
5 2 5 2
7
4 1 2 0
A + B = 1 3 3 1 6 4 4 4 2
If
A = [aij], B = [bij] then A + B = [aij + bij]
Example 2. Show that any square matrix can be expressed as the sum of two matrices, one
symmetric and the other anti-symmetric.
Solution. Let A be a given square matrix.
1
1
Then
A = ( A A ) ( A A )
2
2
Now,
(A + A) = A + A = A + A.
A + A is a symmetric matrix.
Also,
(A – A) = A – A = –(A – A)
1
A – A or
(A – A ) is an anti-symmetric matrix.
2
1
1
A = ( A + A) + ( A – A)
2
2
Square matrix = Symmetric matrix + Anti-symmetric matrix
Proved.
Example 3. Write matrix A given below as the sum of a symmetric and a skew symmetric
matrix.
1 2 4
A = 2 5 3
1 6 3
then
1 2 4
Solution. A = 2 5 3 On transposing, we get A =
1 6 3
On adding A and A, we have
1 2 4 1 2
5
A + A = 2 5 3 2
1 6 3 4
3
1 2 1
2
5 6
4
3 3
1 2 0 3
6 0 10 9
3 3 9 6
...(1)
On subtracting A from A, we get
5
1 2 4 1 2 1 0 4
2 5 3 2
4 0 3
5
6
A – A =
1 6 3 4
3 3 5 3
0
On adding (1) and (2), we have
...(2)
5
2 0 3 0 4
0 10 9 4 0 3
2A=
0
3 9 6 5 3
3
5
1 0 2 0 2
2
9
3
A= 0 5
2 0
2
2
3 9 3 5 3
0
2 2
2 2
A = [Symmetric matrix] + [Skew symmetric matrix.]
Ans.
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Determinants and Matrices
4.21
PROPERTIES OF MATRIX ADDITION
Only matrices of the same order can be added or subtracted.
(i) Commutative Law. A + B = B + A.
(ii) Associative law. A + (B + C) = (A + B) + C.
4.22
SUBTRACTION OF MATRICES
The difference of two matrices is a matrix, each element of which is obtained by subtracting
the elements of the second matrix from the corresponding element of the first.
A – B = [aij – bij]
Thus
4.23
1
3
8 6 4 3 5 1
8 3 6 5 4 1 5
1 2 0 7 6 2 = 1 7 2 6 0 2 6 4 2
Ans.
SCALAR MULTIPLE OF A MATRIX
If a matrix is multiplied by a scalar quantity k, then each element is multiplied by k, i.e.
2 3 4
A = 4 5 6
6 7 9
2 3 4 3 2 3 3 3 4 6 9 12
3 A = 3 4 5 6 3 4 3 5 3 6 12 15 18
6 7 9 3 6 3 7 3 9 18 21 27
EXERCISE 4.10
1. (i)
1 7 1
If A = 2 3 4 , represent it as A = B + C where B is a symmetric
5 0 5 and C is a skew-symmetric matrix.
1 2 0
(b) Express 3 7 1 as a sum of symmetric and skew-symmetric matrix.
5 9 3
9
5
1 2 3 0 2 2
1
9
5
5
3 2
0 2 ( b) A
Ans. (i) A
2
2
2
3 2 5 2 2 0
5
2
2. Matrices A and B are such that
5
2
7
5
5
1
5
0
2
2
2
1
5
0 4
2
5
3
4
0
2
1
2
1 2
3A–2B=
and – 4 A + B = 4 3
2
1
0 1
1 2
Ans. A
, B 4 1
2
1
Find A and B.
x y x
3. Given 3
z w 1
Find x, y, z and w.
0 2 0
4. If A 1 0 3 , B
1 1 2
6
4 x y
2w z w
3
1
2
0
Find (i) 2 A + 3 B (ii) 3 A
Ans. x = 2, y = 4, z = 1, w = 3
2 1
1 0
0 3
3 10 3
4 2 4
Ans. (i) 8 3 6 , (ii ) 5 4
9
2 2 13
3
3 6
– 4 B.
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4.24
275
MULTIPLICATION
The product of two matrices A and B is only possible if the number of columns in A is equal
to the number of rows in B.
Let A = [aij] be an m × n matrix and B = [bij] be an n × p matrix. Then the product AB of these
matrices is an m × p matrix C = [cij] where
cij = ai1 b1j + ai2 b2j + ai3 b3j + .... + ain bnj
4.25 (AB) = BA
If A and B are two matrices conformal for product AB, then show that (AB) = BA, where
dash represents transpose of a matrix.
Solution. Let A = (aij) be an m × n matrix and B = (bij) be n × p matrix.
Since AB is m × p matrix, (AB) is a p × m matrix.
Further B is p × n matrix and A an n × m matrix and therefore B A is a p × m matrix.
Then (AB) and B A are matrices of the same order.
n
Now the (j, i)th element of (AB) = (i, j)th element of (AB) =
aik bkj
...(1)
k 1
Also the jth row of B is b1j, b2j .... bnj, and ith column of A is ai1, ai2, ai3.... ain.
n
(j, i)th element of BA =
bkj aik
...(2)
k 1
From (1) and (2), we have (j, i)th element of (AB) = (j, i) th element of BA.
As the matrices (AB) and BA are of the same order and their corresponding elements are
equal, we have (AB) = BA.
Proved.
4.26 PROPERTIES OF MATRIX MULTIPLICATION
1.
Multiplication of matrices is not commutative. AB BA
2.
Matrix multiplication is associative, if conformability is assured. A (BC) = (AB) C
3.
Matrix multiplication is distributive with respect to addition. A (B + C) = AB + AC
4.
Multiplication of matrix A by unit matrix. AI = IA = A
5.
Multiplicative inverse of a matrix exists if |A| 0. A . A–1 = A–1 . A = I
6.
If A is a square then A × A = A2, A × A × A = A3.
7.
A0 = I
8.
In = I, where n is positive integer.
0 1 2
1 –2
1 2 3 and B –1 0
Example 4. If A =
2 3 4
2 1
obtain the product AB and explain why BA is not defined.
Solution. The number of columns in A is 3 and the number of rows in B is also 3, therefore
the product AB is defined.
C1 C2
0 1 2 R1
1 2 R1 C1
1 2 3 R 1 0 R C
2
2 1
AB =
2 3 4 R3
2 1 R3 C1
R1 C2
R2 C2
R3 C2
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Determinants and Matrices
R1, R2, R3 are rows of A and C1, C2 are columns of B.
1
2
0
0 1 2 1 0 1 2
2
1
1
2
0
= 1 2 3 1 1 2 3
2
1
1
2
2 3 4 1 2 3 4
0
2
1
For convenience of multiplication, we write the columns in horizontal rectangles.
=
0
1 2
1 1 2
1
1 3
1 1 2
2
0
1
2 0
1
2
2 0
3 4
2
3
1 1 2
2
0
2
1
0 1 1 (1) 2 2 0 (2) 1 0 2 (1)
3
= 1 1 2 (1) 3 2 1 (2) 2 0 3 (1)
1
2 1 3 (1) 4 2 2 (2) 3 0 4 (1)
4
1
0 1 4 0 0 2 3 2
1 2 6 2 0 3 5 5
=
Ans.
2 3 8 4 0 4 7 8
Since, the number of columns of B is (2) the number of rows of A is 3, BA is not defined.
1 2 3
1 0 2
2 3 1 and B = 0 1 2
Example 5. If A =
3
1 2 0
1 2
from the products AB and BA, and show that AB BA.
Solution. Here,
1 0 3 0 2 6
1 2 3 1 0 2
2 0 1 0 3 2
2 3 1 0 1 2
AB =
=
3 0 2 0 1 4
3
1 2 1 2 0
1 0 2 1 2 3
1 0 6 2 0 2
0 1 2 2
3
1
BA =
= 0 2 6 0 3 2
1 2 0 3
1 4 0 2 6 0
1 2
AB BA
2 4 0 4 4 2
4 6 0 1 1 10
6 2 0 1 5 4
3 0 4 5 0 7
0 1 4 4 5 3
3 2 0 5 4 1
Proved.
1 2
2 1
–3 1
Example 6. If A =
, B
and C
–2 3
2 3
2 0
Verify that (AB) C = A (BC) and A (B + C) = AB + AC.
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Solution. We have,
(1) (1) (2) (3) 6 7
1
(1) (2) (2) (2)
= (2) (2) (3) (2) (2) (1) (3) (3) 2 7
3
1 6 2 2 0 4 2
0 6 6 2 0 0 2
1 0 1
1
1 2 3 1 3 4
AC = 2 3 2 0 6 6 2 0 12 2
2 ( 3) 1 1 1 2
B + C = 2 2
3 0 4 3
6 7 3 1 18 14 6 0 4 6
(i) (AB) C =
2 7 2 0 6 14 2 0 8 2
2 4 4 6
1 2 4 2 4 0
and
A (BC) = 2 3 0 2 8 0 4 6 8 2
Thus from (1) and (2), we get
(AB) C = A (BC)
1 2 2
AB =
2 3 2
2 1 3
BC = 2 3 2
(ii)
...(1)
...(2)
1 2 1 2
A (B + C) = 2 3 4 3
2 6 7 8
1 8
= 2 12 4 9 14 5
6 1 7 1 7 8
AB + AC = 2 12 7 2 14 5
Thus from (3) and (4), we get
A (B + C) = AB + AC
...(3)
...(4)
Verified.
1 2 2
Example 7. If A = 2 1 2 show that A2 – 4 A – 5 I = 0 where I, 0 are the unit matrix and
2 2 1
the null matrix of order 3 respectively. Use this result to find A–1. (A.M.I.E., Summer 2004)
1 2 2
2 1 2
Solution. Here, we have
A=
2 2 1
1 2 2 1 2 2 9 8 8
2 1 2 2 1 2 8 9 8
A2 =
2 2 1 2 2 1 8 8 9
9 8 8
1 2 2
1 0 0
8 9 8 4 2 1 2 5 0 1 0
A2 – 4 A – 5 I =
8 8 9
2 2 1
0 0 1
9 4 5 8 8 0 8 8 0 0 0 0
8 8 0 9 4 5 8 8 0 0 0 0
A2 – 4 A – 5 I =
8 8 0 8 8 0 9 4 5 0 0 0
A2 – 4 A – 5 I = 0
5 I = A2 – 4 A
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Determinants and Matrices
Multiplying by
A–1,
we get
5 A–1 = A – 4 I
1 2 2
1 0 0 3 2 2
2 1 2 4 0 1 0 2 3 2
=
2 2 1
0 0 1 2 2 3
A–1 =
2
2
3
1
2 3
2
5
2
2 3
Ans.
Example 8. Show by means of an example that in matrices AB = 0 does not necessarily mean
that either A = 0 or B = 0, where 0 stands for the null matrix.
1 1 1
1 2 3
Solution. Let
A = 3 2 1 , B 2 4 6
2
1 2 3
1 0
242
3 6 3
1 2 1
3 4 1 6 8 2 9 12 3
AB =
2 2 0 4 4 0 6 6 0
0 0 0
0 0 0
0 0 0
AB = 0.
But here neither A = 0 nor B = 0.
Proved.
Example 9. If AB = AC, it is not necessarily true that B = C i.e. like ordinary algebra, the
equal matrices in the identity cannot be cancelled.
2 1
4 1 0 3 3 0
1
1 3
2
1 3 2
1 1 1 1 15 0 5
Solution. Let AB =
4 3 1 1 2 1 2 3 15 0 5
2 2
1 1 2 3 3 0
1
1 3
2
1 3 3 2 1 1 1 15 0 5
AC =
Proved.
4 3 1 2 5 1 0 3 15 0 5
Here, AB = AC. But B C.
Example 10. Represent each of the transformations
x1 = 3y1 + 2y2, y1= z1 + 2z2 and x2 = – y1 + 4y2 , y2 = 3z1
by the use of matrices and find the composite transformation which expresses x1, x2 in
terms of z1, z2.
Solution. The equations in the matrix form are
x1 3 2 y1
x = 1 4 y
2
2
y1
1 2 z1
y = 3 0 z
2
2
Substituting the values of y1, y2 in (1), we get
...(1)
...(2)
6 z1 9 z1 6 z2
x1
3 2 1 2 z1 9
x = 1 4 3 0 z 11 2 z 11z 2 z
2
2
2 1
2
x1 = 9z1 + 6z2, x2 = 11z1 – 2z2
Ans.
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Determinants and Matrices
279
Example 11. Prove that the product of two matrices
cos 2
cos sin
cos 2
cos sin
cos sin sin 2
is zero when and differ by an odd multiple of .
2
2
2
cos
cos sin cos
cos sin
×
Solution. =
2
2
cos sin sin
cos sin sin
cos2 cos 2 cos sin cos sin cos 2 cos sin cos sin sin 2
=
2
2
2
2
cos sin cos sin cos sin cos sin cos sin sin sin
cos sin
and
sin2
cos cos (cos cos sin sin ) cos sin (cos cos sin sin )
= sin cos (cos cos sin sin ) sin sin (cos cos sin sin )
cos cos cos ( ) cos sin cos ( )
= sin cos cos ( ) sin sin cos ( )
Given
– = (2 n + 1)
2
cos ( – ) = cos (2n + 1)
0
The product = 0
Example 12. Verify that
A =
=0
2
0
0.
0
Proved.
2
1 2
1
2 1 – 2 is orthogonal .
3
– 2 2 – 1
2
2 2
1 2
1
1
1
1
2
Solution.
A = 2 1 2 A 2
3
3
2 2 1
2 2 1
2 1
2 2
9 0 0 1 0 0
1 2
1
1
AA = 2 1 2 2
1
2 = 0 9 0 0 1 0 I
9
9
0 0 9 0 0 1
2 2 1 2 2 1
Hence, A is an orthogonal matrix.
Verified.
Example 13. Determine the values of , , when
0 2
is orthogonal.
0 2
Solution.
Let A =
On transposing A, we have
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Determinants and Matrices
0
A = 2
If A is orthogonal, then AA = I
0
1 0 0
0 2
2
0 1 0
0 0 1
4 2 2
2 2 2
2 2 2 1 0 0
2 2 2 2 2 2 2 2 2 0 1 0
2
2
2 2 2 2 2 2 0 0 1
2
Equating the corresponding elements, we have
1
1
4 2 2 1
,
6
3
2 2 2 0
1
1
1
,
,
But
2 + 2 + 2 = 1 as
Ans.
6
3
2
Example 14. Prove that
(AB)n = An . Bn, if A . B = B . A
Solution.
(AB)1 = AB = (A) . (B)
(AB)2 = (AB) . (AB) = (ABA) . B = { A (AB) } . B
= (A2B) . B = A2 (B . B) = A2.B2
Suppose that
(AB)n = An . Bn
(AB)n+1 = (AB)n . (AB) = (An . Bn) . (AB) = An . (BnA) . B
= An . (Bn–1 . BA) . B = An . (Bn–1 . AB) . B
= An . (Bn–2 . B . AB) . B = An . (Bn–2 . AB . B) . B
= An . (Bn–2 . AB2) . B, continuing the process n times.
= An . (A . Bn) . B = An . (A . Bn+1) = An+1 . Bn+1
Hence, taking the above to be true for n = n, we have shown that it is true for n = n + 1 and
also it was true for n = 1, 2, .... so it is universally true.
Proved.
EXERCISE 4.11
1. Compute AB, if
2 5 3
3 6 4
20 38 26
Ans.
47 92 62
4 7 5
0
2 3 4
1 2 3
1
. From the product AB and BA. Show that AB BA.
,B=
1 1 2
2
0
0 0 0
1 0 0
1
,B=
0
0 1 0
1 2 3
A=
and B =
4 5 6
1 3
1 2
2. If A =
0 0
0 1
0 0
3. If A =
0 0
(i) Calculate AB and BA. Hence evaluate A2 B + B2 A
(ii) Show that for any number k, (A + kB2)3 = KI, where I is the unit matrix.
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281
0 1
2
4. If A =
choose and so that ( I + A) = A
1 0
1
Ans. = =
2
3 0
2
.
B
1
1
0
5. If A =
and C 2 1
0
0 1
T
T T T
verify that ABC C B A , where T denotes the transpose.
6. Write the following transformation in matrix form :
3
1
1
3
y1 y2 ; x2 = y1
y2
2
2
2
2
Hence, find the transformation in matrix form which expresses y1, y2 in terms of x1, x2.
x1 =
Ans. y1 =
7.
y1 0 0 x1
The linear transformation y 0 1 x ,represents
2
2
3
1
1
3
x1 x2 , y2 = x1
x2
2
2
2
2
(a) reflection about x1 -axis (b) reflection about x2 -axis (c)clockwise rotation through angle
(d) orthogonal projection on to x2 axis.
9.
10.
11.
12.
(A.M.IE.T.E., Summer 2005) Ans. (d)
cos sin
2
and I is a unit matrix, show that I + A = ( I A)
sin cos
0
1
3
1
3 3
If f (x) = x3 – 20 x + 8, find f (A) where A = 1
2 4 4
1
1
tan
1
tan
2
2
cos sin
Show that
=
tan
sin cos
1 tan
1
2
2
3
3
4
If A = 2 3 4 then show that A3 = A–1.
0 1 1
2 1
2
1
2
1 2 is orthogonal.
Verify whether the matrix A =
3
2 2
1
0
8. If A =
tan
2
2
tan
Ans. 0
1 2 2
1
2 1 2
13. Verify that 3
is an orthogonal matrix.
2 2 1
cos
sin sin
14. Show that
cos sin
cos
0
15. Show that A =
sin
0
sin
cos sin cos
is an orthogonal matrix.
sin cos cos
0 sin
1
0
is an orthogonal matrix.
0 cos
(A.M.I.E., Summer 2004)
16. If A and B are square matrices of the same order, explain in general
(i)
(A + B)2 A2 + 2 AB + B2
(ii) (A – B)2 A2 – 2 AB + B2 (iii) (A + B) (A – B) A2 – B2
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Determinants and Matrices
17. Let A and B be any two matrices such that AB = 0 and A is non- singular.
then (a) B = 0; (b) B is also non-singular; (c) B = A; (d) B is singular.
Ans. (d)
18. If A2 = A then matrix A is called
(a) Idempotent Matrix (b) Null Matrix (c) Transpose Matrix (d) Identity Matrix
(A.M,I.E.T.E.,Dec.,2006) Ans. (a)
4.27 MATHEMATICAL INDUCTION
By mathematical induction we can prove results for all positive integers. If the result to be
proved for the positive integer n then we apply the following method.
Working Rule:
Step 1. Verify the result for n = 1
Step 2. Assume the result to be true for n = k and then prove that it is true for n = k + 1.
Explanation. By step 1, the result is true for n = k = 1
By step 2, the result is true for n = k + 1 = 1 + 1 = 2
(k = 1)
Again, the result is also true for n = k + 1 = 2 + 1 = 3
(k = 2)
Similarly, the result is also true for n = k + 1 = 3 + 1 = 4
(k = 3)
Hence, in this way the result is true for all positive integer n.
Example 15. By mathematical induction,
cos n sin n
cos sin
, show that An =
if A =
– sin n cos n
– sin cos
Where n is a positive integer.
Solution. We prove the result by mathematical induction :
cos n sin n
An =
– sin n cos n
Let us verify the result for n = 1.
cos1 sin1 cos sin
A1 =
A
– sin1 cos1 – sin cos
The result is true when n = 1.
Let us assume that the result is true for any positive integer k.
[Given]
cos k sin k
Ak = – sin k cos k
Now,
cos k sin k cos sin
Ak+ 1 = Ak. A =
– sin k cos k – sin cos
cos k sin sin k cos
cos k cos – sin k sin
=
–
sin
k
cos
–
cos
k
sin
–
sin
k sin cos k cos
cos(k 1) sin(k 1)
cos(k ) sin( k )
=
= – sin(k 1) cos(k 1)
– sin(k ) cos( k )
The result is true for n = k + 1.
Hence, by mathematical induction the result is true for all positive integer n.
Proved.
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Determinants and Matrices
283
4.28 ADJOINT OF A SQUARE MATRIX
Let the determinant of the square matrix A be | A |.
a1 a2 a3
a1 a2 a3
b b b ,
b b b .
2
3
2
3
If A = 1
Than | A | = 1
c1 c2 c3
c1 c2 c3
The matrix formed by the co-factors of the elements in
A1 A2 A3
B B B .
2
3
| A | is 1
C1 C2 C3
where
A1
A3
b2
c2
b3
b2 c3 b3c2 ,
c3
b1 b2
A2
b1 b3
b1c3 b3c1
c1 c3
a2
c2
a3
a2 c3 a3 c2
c3
a1
a2
c1
c2
a1
b1
a3
a1b3 a3b1
b3
c1
c2
b1c2 b2 c1 ,
B1
B2
a1
c1
a3
a1c3 a3 c1 ,
c3
B3
C1
a2
b2
a3
a2 b3 a3b2 ,
b3
C2
a1c2 a2 c1
a2
a1b2 a2 b1
b1 b2
Then the transpose of the matrix of co-factors
A1 B1 C1
A B C
2
2
2
A3 B3 C3
is called the adjoint of the matrix A and is written as adj A.
C3 =
a1
4.29 PROPERTY OF ADJOINT MATRIX
The product of a matrix A and its adjoint is equal to unit matrix multiplied by the determinant A.
Proof. If A be a square matrix, then (Adjoint A) . A = A . (Adjoint A) = |A| . I
A1 B1 C1
a1 a2 a3
A B C
b b b
Let
A= 1
2
2
2
3 and adj . A = 2
A
B
C
3
3
3
c1 c2 c3
a1 a2 a3 A1 B1 C1
A . (adj. A) = b1 b2 b3 A2 B2 C2
c1 c2 c3 A3 B3 C3
a1 A1 a2 A2 a3 A3 a1 B1 a2 B2 a3 B3 a1 C1 a2 C2 a3 C3
= b1 A1 b2 A2 b3 A3 b1 B1 b2 B2 b3 B3 b1 C1 b2 C2 b3 C3
c A c A c A
c1 B1 c2 B2 c3 B3 c1 C1 c2 C2 c3 C3
2 2
3 3
1 1
0
| A | 0
1 0 0
0 | A| 0
=
(A.M.I.E., Summer 2004)
= | A | 0 1 0 = | A | I
0
0 | A |
0 0 1
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Determinants and Matrices
4.30 INVERSE OF A MATRIX
If A and B are two square matrices of the same order, such that
AB = BA = I
(I = unit matrix)
then B is called the inverse of A i.e. B = A–1 and A is the inverse of B.
Condition for a square matrix A to possess an inverse is that matrix A is non-singular,
i.e., | A | 0
If A is a square matrix and B be its inverse, then AB = I
Taking determinant of both sides, we get | AB | = | I | or | A | | B | = I
From this relation it is clear that | A | 0
i.e. the matrix A is non-singular.
To find the inverse matrix with the help of adjoint matrix
We know that
A . (Adj. A) = | A | I
1
A
( A dj. A) = I
[Provided | A | 0]
...(1)
| A|
and
A . A–1 = I
...(2)
From (1) and (2), we have
1
A –1 =
( Adj. A)
| A|
3
2
Example 16. If A =
0
3 – 3
2 – 3
Solution. A =
0 – 1
– 3 4
– 3 4 , find A1 .
(A.M.I.E. Summer 2004)
– 1 1
4
4
1
| A | = 3 (– 3 + 4) + 3 (2 – 0) + 4 (– 2 – 0) = 3 + 6 – 8 = 1
The co-factors of elements of various rows of | A | are
( 2 0) ( 2 0)
( 3 4)
(3 4)
(3 0)
(3 0)
(12 12) ( 12 8) ( 9 6)
Therefore, the matrix formed by the co-factors of | A | is
0
1 1
1 2 2
2 3 4
1
3
3 , Adj. A =
2 3 3
0 4 3
0 1 1
0
1 1
1
2 3 4
1
1
2
3
4
A
Adj. A = 1
Ans.
| A|
2 3 3 2 3 3
1 4
– 8
1
4 7 , prove that A–1 = A, A being the transpose of A.
Example 17. If A = 4
9
1 – 8 4
(A.M.I.E., Winter 2000)
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285
1 4
8
4
4
7 ,
A=
9
1 8 4
1 4
– 8
1
1
4
4 7
AA = 9
9
1 – 8 4
1
8 4
1
1
4
8
Solution. We have,
9
4 7
4
1
8 4
1 4 8
4 7
4
64 1 16 32 4 28 8 8 16
1
32 4 28 16 16 49 4 32 28
= 81
8 8 16
4 32 28 1 64 16
81 0 0 1 0 0
1
0 81 0 0 1 0 or AA I
= 81
0 0 81 0 0 1
A = A–1
Proved.
2
Example 18. If a matrix A satisfies a relation A A I 0 proved that A1 exists and that
A –1= I + A, I being an identity matrix.
(A M I E Winter 2003)
A A I I
Solution. Here A2 A I 0
or
A2 AI I or
A A I I
1
Again
2
A AI 0
A
A 0 and so A–1exists.
A2 A I
or
...(1)
Multiplying (1) by A ,we get
A1 A2 A A1 I
or A I A1
A–1 = I + A
Proved.
Example 19. If A and B are non-singular matrices of the same order then,
(AB)–1 = B–1 . A–1
Hence prove that (A–1)m = (Am)–1 for any positive integer m.
Solution. We know that,
(AB) . (B–1 A–1) = [(AB) B–1] . A–1 = [A (BB–1] . A–1
= [AI] A–1 = A . A–1 = I
1
Also,
B–1 A–1 . (AB) = B–1[A–1 . (AB)] = B–1 [(A–1 A) . B]
= B–1 [I . B] = B–1 . B = I
By definition of the inverse of a matrix, B–1 A–1 is inverse of AB.
B–1 A–1 = (AB)–1
(Am)–1 = [A . Am–1]–1 = (Am–1)–1 A–1
Proved.
= (A . Am–2)–1 . A–1 = [(Am–2)–1 . A–1] . A–1 = (Am–2)–1 (A–1)2
= (A . Am–3)–1 . (A–1)2 = [(Am–3)–1 . A–1] (A–1)2 = (Am–3)–1 (A–1)3
= A–1 (A–1)m–1 = (A–1)m
Proved.
Example 20. Find A satisfying the Matrix equation.
2
4
2 1 – 3
– 2
3 2 A 5 – 3 = 3 – 1
2
2 1 3
2 4
Solution. 3 2 A 5 3 = 3 1
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Determinants and Matrices
2 1
2 1
i.e.,
Both sides of the equation are pre-multiplied by the inverse of
3 2
3 2
2 2 1 2
4
2 1 2 1 3
3 2 3 2 A 5 3 = 3 2 3 1
2 7
9
1 0 3
0 1 A 5 3 = 12 14
2
9
3
7
A
=
5 3
12 14
2
3
Again both sides are post-multiplied by the inverse of
i.e.
5 3
2 3 2
9 3 2
3
7
A
=
5 3 5 3
12 14 5 3
3 2
5 3
13
13
1 0 24
24
A
=
A
=
0 1 34 18
34 18
EXERCISE 4.12
Ans.
Find the adjoint and inverse of the following matrices: (1 - 3)
1.
3.
4.
2 5 3
3 1 2
1 2 1
0
1
3
4
0 6
3
If A
1
1
5
7
1
7
3
1
5
Ans. 1 1
4
1 13
5
6
4
2
1
21
7
8
Ans.
20
18
6
4
2.
1 1 2
1 9 3
1 4 2
6 15
6
1
0
1
Ans. 1
3
8
5 3
4
1 2n 4n
, then show that An
1
1 2n
n
1 0 0
1 1 2
1 1 3
1 2
1 , P 0 3 2 , show that P–1 AP = 0 2 0
5. If A =
0 0 1
0 1 1
1 1 1
1 1 1
2 5 3
6. If A = 1 2 3 , B 3 1 2 , show that (AB)–1 = B–1 A–1.
1 4 9
1 2 1
3 2 2
3 4 2
7. Given the matrix A = 1 3 1 compute det (A), A–1 and the matrix B such that AB = 1 6 1
5 3 4
5 6 4
Also compute BA. Is AB = BA ?
9 2 4
1 0 0
1
2 1 . B 0 2 0 , AB BA
Ans. 5, 1
5
12
0 0 1
1
7
8. Find the condition of k such that the matrix
29 17 14
1 3 4
3 1 1
–1
5
6
A = 3 k 6 has an inverse. Obtain A for k = 1. Ans. k , A 9
5
8
16 8 8
1 5 1
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9. Prove that (A–1)T = (AT)–1.
0 1 2 1
a b
10. If A
1 0 where A
, then A is
2
1
c d
2 1
(a)
0 0
0 1
(b)
2 1
2 1
(c)
1 0
1
2
(AMIETE, June 2010) Ans. (d)
(d)
1 1
2
2
2
3 x 2
2
4 x
1
11. For what values of x, the matrix
is singular ? (A.M.I.E.T .E . Summer 2004) Ans. 0,3
2
4 1 x
12. Prove that (A–1)T = (AT)–1
13. Let I be the unit matrix of order n and adj. (2I) = 2k I. Then k equals
(a) 1
(b) 2
(c) n – 1
(d) n.
Ans (c)
14. Let T be a linear transformation defined by
1
1
1
1
1 1 0 0 0 1 0 0
4 5
2
,
T
2
,
T
2
,
T
2 ,
T 1 1 1 1 1 1 0 1 Find T 3 8 .
3
3
3
3
(AMIETE Dec. 2005)
4.31 ELEMENTARY TRANSFORMATIONS
Any one of the following operations on a matrix is called an elementary transformation.
1. Interchanging any two rows (or columns). This transformation is indicated by Rij, if the ith
and jth rows are interchanged.
2. Multiplication of the elements of any row Ri (or column) by a non-zero scalar quantity k is
denoted by (k.Ri).
3. Addition of constant multiplication of the elements of any row Rj to the corresponding
elements of any other row Rj is denoted by (Ri + kRj).
If a matrix B is obtained from a matrix A by one or more E-operations, then B is said to be
equivalent to A. The symbol ~ is used for equivalence.
i.e., A ~ B.
Example 21. Reduce the following matrix to upper triangular form (Echelon form) :
1 2 3
2 5 7
3 1 2
Solution. Upper triangular matrix. If in a square matrix, all the elements below the principal
diagonal are zero, the matrix is called an upper triangular matrix.
2
3
3
1 2 3 1
1 2
2 5 7 ~ 0
R2 R2 2 R1 ~ 0 1
1
1
1
R R3 5 R2
3 1 2 0 5 7 R3 R3 3 R1 0 0 2 3
1 3 3
Example 22. Transform 2 4 10 into a unit matrix.
3 8 4
Ans.
(Q. Bank U.P., 2001)
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Determinants and Matrices
Solution.
3
3
1 3 3 1
2 4 10 ~ 0 2
4 R2 R2 2 R1
3 8 4 0 1 5 R3 R3 3 R1
3
9 R1 R1 3 R2
1 3
1 0
1
~ 0
1 2 R2 R2 ~ 0 1 2
2
0 1 5
0 0 7 R3 R3 R2
9
1 0
1 0 0 R1 R1 9 R3
~ 0 1 2
~ 0 1 0 R2 R2 2 R3
1
0 0
1 R3 R3 0 0 1
7
4.32 ELEMENTARY MATRICES
A matrix obtained from a unit matrix by a single elementary transformation is called elementary
matrix.
1 0 0
I = 0 1 0
0 0 1
Consider the matrix obtained by R2 + 3 R1
1 0 0
3 1 0 is called the elementary matrix.
0 0 1
4.33 THEOREM
Every elementary row transformation of a matrix can be affected by pre-multiplication with
the corresponding elementary matrix.
Consider the matrix
2 3 4
A = 5 6 7
3 5 9
Let us apply row transformation R3 + 4 R1 and we get a matrix B.
2 3 4
5 6 7
B=
11 17 25
Now we shall show that pre-multiplication of A by corresponding elementary matrix R3 + 4 R1
will give us B.
1 0 0
1 0 0
0 1 0
then, Elementary matrix = 0 1 0
Now, if I =
0 0 1
4 0 1
(R 4 R )
3
1
1 0 0 2 3 4 2 3 4
Elementary matrix × A = 0 1 0 5 6 7 = 5 6 7 = B
4 0 1 3 5 9 11 17 25
Similarly, we can show that every elementary column transformation of a matrix can be affected
by post-multiplication with the corresponding elementary matrix.
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4.34 TO COMPUTE THE INVERSE OF A MATRIX FROM ELEMENTARY
MATRICES (Gauss-jordan Method)
If A is reduced to I by elementary transformation then
PA = I
where
P = PnPn–1 ... P2 P1
–1
P=A
= Elementary matrix.
Working rule. Write A = IA. Perform elementary row transformation on A of the left side and
on I of the right hand side so that A is reduced to I and I of right hand side is reduced to P getting
I = PA.
Then P is the inverse of A.
4.35 THE INVERSE OF A SYMMETRIC MATRIX
The elementary transformations are to be transformed so that the property of being symmetric
is preserved. This requires that the transformations occur in pairs, a row transformation must be
followed immediately by the same column transformation.
Example 24. Find the inverse of the following matrix employing elementary transformations:
3 3 4
2 3 4
(U.P., I Semester, Compartment 2002)
0 1 1
3 3 4
Solution. The given matrix is A = 2 3 4
0 1 1
R1
1
4
3 0 0 R1 3
1 1 3
A
2 3 4 = 0 1 0
0 1 1
0 0 1
1
4
4 1
0 0
1
1
3 0 0
3
1 1 3
3
2
4
2 1 0
4
0
=
0 1
1 = 3 1 0 A
A
3
3
3
R2 R2 2R1
0 0 1 R2 R2
0
1
1
0 0 1
0 1 1
4
1
4
1
0
0
0 0
1 1
1 1
3
3
3
3
4
2
2
4
0
A
1
1
0
1 0 A
1
=
0
=
3
3
3
3
1
0 0 1
2 3 3 R3 3 R3
2 1 1 R3 R3 R2
0 0
3
3
3 3 4
1 0 0
2 3 4
= 0 1 0 A
0 0 1
0 1 1
4
4
3 4
R1 R1 R3
1 1 0
3
0
3 4 A
4
1 0 = 2
R2 R2 R3
2
3
3
3
0 1
0
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Determinants and Matrices
0 R1 R1 R2
1 1
0
1 1
1 0 0
0 1 0 = 2
–1
3
4
2
3
4
Hence, A =
Ans.
A
2
3 3
2
3 3
0 0 1
Example 23. Find the inverse of the matrix M by applying elementary transformations
0
1
1
–1
2
1
2
1
Solution. Here, we
Let
1
3
–1 –2
.
0
1
2 6
0
1
have A =
1
–1
1
[U.P.T.U.(C.O.) 2003]
2
1
3
1 –1 –2
2 0
1
1 2 6
0
1
1
–1
2
3 1
1 –1 –2 0
~
2 0
1 0
1 2 6 0
0 0 0
1 0 0
A
0 1 0
0 0 1
1
0
1
–1
1 –1 –2
2 1 3
~
2 0
1
1 2 6
0
1
0
0
1 0 0 R1 R2
0 0 0
0 1 0 A
0 0 1
1
0
0
0
1 –1 –2
2 1 3
~
1 1 3
2 1 4
0 1 0
1 0 0
0 –1 1
0 1 0
1
0
0
0
1 –1 –2
0 1 0 0
0 –1 1 0 A
1 1 3
=
1 0 0 0 R3 R2
2 1 3
2 1 4
0 1 0 1
1
0
0
0
1 –1 –2 0 1 0 0
1 1 3 0 –1 1 0 A
=
0 –1 –3 1 2 –2 0 R R –2R
3
3
2
0 –1 –2 0 3 –2 1 R R – 2R
4
2
4
1
0
0
0
1 –1 –2 0 1 0 0
1 1 3 0 –1 1 0 A
=
0 –1 –3 1 2 –2 0
0 0
1 –1 1 0 1 R4 R4 – R3
0
0 A
0 R3 R3 – R1
1 R4 R4 + R1
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Determinants and Matrices
1
0
0
0
291
1 –1 –2 0
1 0 0
1 1 3
= 0 –1 1 0 A
0 1 3 –1 –2 2 0 R3 – R3
0 0
1 –1 1 0 1
1 –1 0
–2 3 0 2 R1 R1 2 R4
3 –4 1 –3 R R – 3 R
1 1 0
2
4
2
=
2 –5 2 –3 R3 R3 – 3R4
0 1 0
1 A
0 0 1
–1 1 0
1
0
0
0
1
0
0
0
1 0 0
1 0 0
=
0 1 0
0 0 1
1
0
0
0
0 0 0
1 0 0
=
0 1 0
0 0 1
0 –2 2 –1 R1 R1 R3
1 1 –1 0 R R R
2
3
2
2 –5 2 –3 A
1
–1 1 0
–1
1
2
–1
I = A–1 A
–3
3 –1 R1 R1 – R2
1 –1 0
–5 2 –3 A
1 0 1
–1 –3 3 –1
1 1 –1 0
A–1 =
2 –5 2 –3
–1 1 0 1
Hence,
Ans.
EXERCISE 4.13
Reduce the matrices to triangular form:
1 2 3
1. A = 2 5 7
3 1 2
1 2 3
Ans. 0 1 1
0 0 –2
3 1 4
2. 1 2 5
0 1 5
0 1 4
Ans. 0 5 –19
0 0 22
Find the inverse of the following matrices:
3.
5.
1 3 3
1 4 3
1 3 4
1 – 1 1
1 0
4. 4
8
1 1
1 1 3
Use elementary row operations to find inverse of A 1 3 3
2 4 4
7 – 3 – 3
Ans. – 1
1
0
– 1
0
1
2
1
Ans. – 4 – 7
– 4 – 9
12 4
1
Ans. 5 1
4
1 1
– 1
4
5
6
3
1
(AMIETE, June 2010)
6.
1 –1
2
2
1
3
2
–
3
– 1
2
1 – 1
4
2 – 3 – 1
5 –7
1
2
5 –1
5 2
1
Ans.
5 11 10
18 – 7
1 – 2 10 5
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292
Determinants and Matrices
7.
1
1
2
1
8.
1 –1 2
2
1
2 5 –7
1 3 2 –3
5 –1 5 –2
Ans. 1
–1 2 1 –1
18 –7 5 11 10
2 –3 –1 4
1 –2 10 5
10.
3
3
1
1
4
3
1
3
4
1
1
1
1 – 2 – 1
11.
2
3
4
1
3
3
3
1
1
2
(Q. Bank U.P. II Semester 2001)
3
1
1 0
1 –2
1 –2 2 –3
Ans. 0
1 –1 1
–2 3 –2 3
2 –6 –2 –3
–2
5 –13 –4 –7
Ans. 1
9.
–1
–4
4
1 2
1 0
1
0
–1
1 0 1
0 2 –1
1 –3 1
0 –2 2
30 – 20 – 15 25 – 5
30 – 11 – 18
7 – 8
1
– 30
Ans.
12
21 – 9
6
15
–
15
12
6
–
9
6
15 – 7 – 6 – 1 – 1
2
1
3 – 1
1
1
1 – 1
2
2
X
If X, Y are non-singular matrices and B =
O
matrix.
X –1 O
O
where O is a null
–1 =
,
show
that
B
Y
O Y –1
4.36 RANK OF A MATRIX
The rank of a matrix is said to be r if
(a) It has at least one non-zero minor of order r.
(b) Every minor of A of order higher than r is zero.
Note: (i) Non-zero row is that row in which all the elements are not zero.
(ii) The rank of the product matrix AB of two matrices A and B is less than the rank of either
of the matrices A and B.
(iii) Corresponding to every matrix A of rank r, there exist non-singular matrices P and Q such
I r 0
that
PAQ =
0 0
4.37
NORMAL FORM (CANONICAL FORM)
By performing elementary transformation, any non-zero matrix A can be reduced to one of the
following four forms, called the Normal form of A :
(i) Ir
(ii) [Ir 0]
Ir
(iii)
0
I
(iv) r
0
0
0
I r 0
The number r so obtained is called the rank of A and we write (A) = r. The form
is
0 0
called first canonical form of A. Since both row and column transformations may be used here, the
element 1 of the first row obtained can be moved in the first column. Then both the first row and
first column can be cleared of other non-zero elements. Similarly, the element 1 of the second row
can be brought into the second column, and so on.
Example 24. Reduce to normal form the following matrix
1 2 3 4
A 2 1 4 3
3 0 5 10
R 3 – 2R 2, R 3 – 3R 1
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Determinants and Matrices
Solution.
293
4
1 2 3 4 1 2 3
A 2 1 4 3 0 3 2 5
3 0 5 10 0 6 4 22
1
1
C2 2C1 , C3 3C1 , C4 4C1 , R2 , R3
R 3 R 2
3
6
1 0 0
0 1 0 0
0
0
1 0 0
2
5
2
5
0 3 2 5 0 1
0 1
3
3
3
3
0 6 4 22
0 0 0
2
11
2
0 1
3
3
2
5
1 C
C3 C2 , C4 C2 C3 C4
2 3
3
3
1 0 0 0 1 0 0 0 1 0 0 0
0 1 0 0 0 1 0 0 0 1 0 0
0 0 0 2 0 0 2 0 0 0 1 0
= [I3 0] is the normal form of A. Ans.
Example 25. Find the rank of the following matrix by reducing it to normal form –
2 – 1 3
1
4
1
2 1
A=
(U.P. I Sem., Com. 2002, Winter 2001)
3 – 1
1 2
2
0 1
1
2 –1
3
1
2 – 1 3
1
4
0 –7
6 – 11 R2 R2 – 4 R1
1 2 1
Solution.
~
0 – 7
4 – 7 R3 R3 – 3 R1
3 – 1
1 2
0
1 – 2 R4 R4 – R1
2
0 1
0
1
0
0
0
0 0
0 1
1
0 – 7 6 – 11 0 – 7
6 – 11
~
0 –2
4 R3 R3 – R2
0 – 7 4 – 7 0
0
0
1 – 2
0 1 – 2
0
C2 C2 – 2 C1, C3 C3 + C1, C4 C4 – 3C1
0
0
0
0 1
1
0 – 7
0 – 7
0
0
~
0
0
0 –2
4 0
0
0
1 – 2 0
0
6
C3 C3 +
C , C C4 –
7 2 4
0
0 0
1
0 – 7
0 0
C4 C4 + 2C3
0
0 – 2 0
0
0 0
0
Rank of A = 3
0 0
0 0
– 2 4
1
0 0 R4 R4 R3
2
11
C,
7 2
1 0 0 0
0 1 0 0 R – 1/ 7 R
2
2
~
0 0 1 0 R3 – 1/ 2 R3
0 0 0 0
Ans.
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294
Determinants and Matrices
Example 26. Reduce the matrix A to its normal form, when
Hence, find the rank of A.
2 –1
4
1
2
4
3
4
A=
1
2
3
4
–
1
–
2
6
–
7
(U.P., I Semester, Dec. 2004, Winter 2001)
2 –1
4
1
2
4
3
4
Solution. The given matrix is A =
1
2
3
4
6 – 7
– 1 – 2
1
0
0
0
2 –1
4
1
0
R
R
–
2
R
0
5 – 4 2
2
1
0
0
4
0 R3 R3 – R1
R R R
0
5 – 3 4
0
4
1
1
0
0
0
1
0 0
0
0
5 0 – 4
C3 C2
4 0
0
0
5 0 – 3
0
1
0
0
0
1
0
0
0
0 0
0
C2 C2 – 2 C1
0 5 – 4
C3 C3 C1
0 4
0
C4 C4 – 4 C1
0 5 – 3
0 0
0
5 0 – 4
16
4
0 0
R3 R3 – R2
5
5
0 0
1 R4 R4 – R2
0
0
0 0
1 0
0 5
0
5 – 4 0
16
C4 C3
16
0
0
0 0
5
5
0
0
0
0
1 0
0 0 0
1 0 0 R2 1/ 5 R2
I3
0 1 0 R3 5 /16 R3
0
0 0 0
0
5
0 R2 R2 R3
4
0
5
R3
0 R4 R4 –
16
0
0
Which is the required normal form.
And since, the non-zero rows are 3 hence, the rank of the given matrix is 3.
Example 27. Find non-singular matrices P, Q so that PAQ is a normal form where
Ans.
1 – 3 – 6
2
1
2
A = 3 – 3
(R.G.P.V., Bhopal, April, 2010, U.P., I Sem. Winter 2002)
1
1
1
2
and hence find its rank.
Solution. Order of A is 3 × 4
Total number of rows in A = 3; Consider unit matrix I3.
Total number of columns in A = 4
Hence, consider unit matrix I4,
A 3 × 4 = I3 A I4
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Determinants and Matrices
295
1
1 – 3 – 6
2
1 0 0
0
3 – 3
1
2 = 0 1 0 A
0
1
0 0 1
1
1
2
0
1
1
1
2
1
0 0 1
0
3 – 3
1
2 = 0 1 0 A
0
1 0 0
2
1 – 3 – 6
0
0 0 0
1 0 0
0 1 0
0 0 1
0 0 0
1 0 0
R1 R3
0 1 0
0 0 1
1
1
1
2 0 0
1
1
0 – 6 – 2 – 4
0
= 0 1 – 3 A 0
0 – 1 – 5 – 10 1 0 – 2
0
0 0 0
1 0 0 R2 R2 – 3 R1
0 1 0 R3 R3 – 2 R1
0 0 1
C2 C2 – C1, C3 C3 – C1, C4 C4 – 2 C1
1 1 1 2
1
0
0
0 0 0
1
0
1 0
0
0 – 6 – 2 – 4 = 0 1 – 3 A
0
0
1
0
0 – 1 – 5 – 10 1 0 – 2
0
0
1
0
1 – 1 – 1 – 2
0 0 1
0
1 0 0
1 0
0 R2 (1) R2
A 0
0
1
3
0 6 2
0
4 =
0
1
0 R3 (1) R3
1 0 2
0 1 5 10
0
0
1
0
1 – 1 – 1 – 2
1 0 0 0
0 1
0
0 1 5 10
0
1 0
0 R2 R3
= – 1 0 2 A
0
0
1
0
0 6 2 4
0 – 1 3
0
0
1
0
1 – 1 – 1 – 2
0
0
0
1
1 0
0
1 0
0
0 1
– 1 0
A 0
5
10
2
R3 R3 – 6 R2
=
0
0
1
0
6 – 1 – 9
0 0 – 28 – 56
0
0
1
0
C3 C3 – 5 C2,
C4 C4 – 10 C2
4
8
1 – 1
0
1
0
0
0
1 0
1 – 5 – 10
– 1 0
A 0
0 1
2
=
0
0
0
0
1
0
6 – 1 – 9
0 0 – 28 – 56
0
0
1
0
1 0 0 0
0 1 0 0
=
0 0 1 2
–
0
0
–1
0
6
28
1
28
1 – 1
4
8
1
0
1 – 5 – 10
1
2 A
R3 –
R3
0
0
1
0
28
9
0
0
0
1
28
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296
Determinants and Matrices
0
0
1 0 0 0
0 1 0 0 – 1 0
=
0 0 1 0 6
1
–
28 28
N = PAQ
1 – 1
4
0
1
0
1 –5
0
2 A
C4 C4 – 2 C3
0
0
1 – 2
9
0
0
0
1
28
4
0
1 – 1
0
0
1
0
1 –5
0
–
1
0
2
,
Q
P=
Ans.
0
0
1 – 2
3
1
9
–
0
0
1
0
14 28 28
Note. P and Q are not unique.
1 0 0 0
Normal form of the given matrix is 0 1 0 0
0 0 1 0
The number of non zero rows in the normal matrix = 3
Hence Rank = 3
Ans.
3 – 3 4
Example 28. If A = 2 – 3 4 , Find two non singular matrices P and Q such that
0 – 1 1
PAQ = I. Hence find A–1.
Solution.
A 3 × 3 = I3 A I3
3 – 3 4 1 0 0 1 0 0
2 – 3 4 0 1 0 A 0 1 0
=
0 – 1 1 0 0 1 0 0 1
0 0 1 – 1 0 1 0 0
1
2 – 3 4
1 0 A 0 1 0 R1 R1 – R2
= 0
0 – 1 1 0
0 1 0 0 1
0 0
1
1 – 1 0 1 0 0
0 – 3 4
3 0 A 0 1 0 R3 R2 – 2R1
= – 2
0
0 1 0 0 1
0 – 1 1
1 0 0 1 – 1 0 1 0 0
0 3 4 – 2
3 0 A 0 – 1 0 C2 – C2
=
0 1 1 0
0 1 0
0 1
1 0 0 1 – 1 0 1 0 0
0 1 1 0
0 1 A 0 – 1 0 R2 R3
=
0 3 4 – 2
3 0 0
0 1
0 1 0 0
1 0 0 1 – 1
0 1 1 0
0
1 A 0 – 1 0 R3 R3 – 3 R2
=
3 3 0
0 1
0 0 1 – 2
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Determinants and Matrices
297
0 1 0 0
1 0 0 1 – 1
0 1 0 = 0
0
1 A 0 – 1 1 C3 C3 – C2
3 – 3 0
0 1
0 0 1 – 2
I3 = PAQ
A–1
= QP,
A–1 =
0
1 0 0 1 – 1
0 – 1 1 0
0
1
0
0 1 – 2
3 – 3
A–1 =
0
1 –1
– 2
3
–
4
– 2
3 – 3
I P A Q
–1
P A Q
–1 –1
P Q A
( P –1 Q –1 ) –1 A –1
QP A–1
Ans.
Exercise 4.14
Find non singular matrices P and Q such that PAQ is normal form
1.
1 2 3
3 1 2
2.
2
1 1
1 2
3
0 1 1
3.
1 2 3 2
2 2 1
3
3 0 4
1
4.38
2 1
1 5 5
1 0
1 7
p
,
Q
0
Ans.
3 1
5 5
0 0 1
1 0 0
1 1 1
Ans. p 1 1 0 , Q 0 1 1
1 1 1
0 0 1
1 4 1
1 3 15 21
0 1 1 1
1 0 0
6 6 6
Ans. P 2 1 0 , Q
0 0 1 0
1 1 1
5
1
0 0 0
7
RANK OF MATRIX BY TRIANGULAR FORM
Rank = Number of non-zero row in upper triangular matrix.
Note. Non-zero row is that row which does not contain all the elements as zero.
Example 29. Find the rank of the matrix
1 2 3 2
2 3 5 1
1 3 4 5
(U.P., I Semester, Winter 2003, 2000)
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298
Determinants and Matrices
3
2
1 2 3 2 1 2
2 3 5 1 0 – 1 1 – 3
R2 R2 – 2 R1
1 3 4 5 0
1
1
3 R3 R3 – R1
Solution.
3
2
1 2
0 – 1 – 1 – 3
0
0
0
0 R3 R3 R2
Rank = Number of non zero rows = 2.
Ans.
2
3 – 2
– 1
2 –5
1
2
Example 30. Find the rank of the matrix
3 –8
5
2
6
5 – 12 – 1
2 3 – 2
2
3 – 2 – 1
– 1
0 – 1 7 – 2 R R 2 R
2 –5
2
1
1
2
2
Solution.
~
R
R
3
R
3 –8
3
1
5
2 0 – 2 14 – 4 3
0 – 2 14 – 4 R R 5 R
4
1
6
5 – 12 – 1
4
– 1 2 3 – 2
0 – 1 7 – 2 R R – 2 R
3
2
3
~
0
0 0
0 R4 R4 2 R2
0 0
0
0
Here the 4th order and 3rd order minors are zero. But a minor of second order
3 –2
7
–2
– 6 14 = 8 0
Rank = Number of non-zero rows = 2.
Ans.
Example 31. Find the rank of matrix
3 – 2 4
2
3 –2
1 2
3
2
3 4
4
0 5
– 2
Solution. Multiplying R1 by
(U.P., I Semester, Dec., 2006)
1
, we get 1 as pivotal element
2
3
– 1 2
1
2
1 2
3 –2
3
2
3 4
4
0 5
– 2
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Determinants and Matrices
299
3
0
0
0
2
–1
2
1
1
2
R2 – 13 R2
8
8
0
1 –
0 – 13
4 – 4 R2 R2 – 3R1
13 13 C C – 3 C
2
2
1
2
2
5
5
0
–
6
–
2
0 –
6 – 2 R3 R3 – 3R1
2
C3 C3 C1
2
0
7
–2
9 C4 C4 – 2C1
0
7 –2
9 R4 R4 2 R1
1
0
0
0
0
0
0
1
8
8
0
1 –
13
13
5
58
6
0
0
– R3 R3 R2
2
13
13
30
61
0
0
R R –7R
13
13 4
4
2
1
0
0
0
0
1
0
0
0
0 0
1 0
0
0
1
0
1 0
0
3 R 13 R
0
1 – 3
3
0
58
29
30
61
0
0
13
13
0
1
0
0
58
13
30
0
13
0
0
0
6
8
– C3 C3 C2
13
13
61
8
C C4 – C2
13 4
13
0 0
0
1 0
0
3
0 1 –
29
30
143
R4 R4 –
R3
0 0
13
29
0
1
0
0
0 1
0
0
143
0
0 0
3
C3
29 C4 C 4
29
0 0 0
1 0 0
0 1 0
29
R
R4
0 0 1 4
143
I4
Hence, the rank of the given matrix = 4
Ans.
Example 32. Use elementary transformation to reduce the following matrix A to triangular
from and hence find the rank of A.
3 –1
2
1 – 1 – 2
A=
3
1
3
6
3
0
1
– 4
– 2
– 7
(R.G.P.V., Bhopal, June 2007, Winter 2003, U.P., I Semester, Dec. 2005)
Solution. We have,
3 –1
2
1 – 1 – 2
A=
3
1
3
3
0
6
1 1 – 1 – 2 – 4
– 4 2
3 – 1 – 1
R1 R2
– 2 3
1
3 – 2
– 7 6
3
0 – 7
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Determinants and Matrices
–2
– 4
1 – 1 – 2 – 4
1 – 1
0
0
5
3
7
5
3
7
R2 R2 – 2 R1
0
4
9 10 R3 R3 3R1 0
0 33 / 5 22 / 5 R3 R3 – 4 / 5 R2
9 12 17 R4 R4 – 6 R1 0
0 33 / 5 22 / 5 R4 R4 – 9 / 5 R2
0
–2
– 4
1 – 1
0
5
3
7
0
0 33 / 5 22 / 5
0
0
0 R4 R4 – R3
0
R(A) = Number of non-zero rows.
R(A) = 3
Ans.
EXERCISE 4.15
Find the rank of the following matrices:
1.
1 2 3
2 4 7
3 6 10
3.
0 1 2 – 2
4 0 2
6
2 1 3
1
Ans. 2
1
1 2
2. – 1 0
2
2 1 – 3
Ans. 3
Ans. 2
4
3 – 2
2
– 3 – 2 – 1
4
4.
6 – 1 7
2
Ans. 3
4
3 – 2 1
1
4 1
1
3
– 2 – 3 – 1
2
4 3
4
3
6
5.
Ans. 4
6.
–1
– 1 – 2 6
6
7
2 9
4
3
6
6 12
1 – 1 2 – 3
– 3
Reduce the following matrices to Echelon form and find out the rank:
1 0 0
Ans. 0 1 0 , Rank = 3
0 0 1
7.
1 1 2
1 2 2
2 2 3
9.
3 2 5 7 12
I2
1 1 2 3 5 Ans.
0
3 3 6 9 15
0
, Rank = 2
0
1
2
8.
3
6
2 3 0
4 3 2
2 1 3
8 7 5
Ans. 2
I
Ans. 3
0
0
, Rank = 3
0
3
1 0
2 – 4
1 – 2
I 0
1 – 4 2
10.
Ans. 3
, Rank = 3
0
1 –1
3 1
0 0
4 – 4 5
4 – 7
Using elementary transformations, reduce the following matrices to the canonical form (or row-reduced
Echelon form):
0 0 0 0 0
1 0 0 0
0 1 2 3 4
Ans. 0 1 0 0
11. A
0 2 3 4 1
0 0 1 0
0 3 4 1 2
4 – 12 8
0
0
2 –6 2
12. A
0
1 –3 6
24 3
0 – 8
9
1 0 0 0
5
Ans. 0 1 0 0
4
0 0 1 0
1
Using elementary transformations, reduce the following matrices to the normal form:
1 0 0 0
1 2 0 – 1
13. A
2 Ans. 0 1 0 0
3 4 1
0 0 1 0
– 2 3 2
5
1 2 3 4
14. A 3 4 1 2
4 3 1 2
1 0 0 0
Ans. 0 1 0 0
0 0 1 0
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Determinants and Matrices
301
Obtain a matrix N in the normal form equivalent to
0 0
0 0 0
0 4 5
0 0
15.
A
0 9 1 – 1 2
1 11
0 10 0
Hence find non-singular matrices P and Q such that PAQ = N.
1 – 3
0
1
3
4
Find the rank
16.
1
2
17. A
1
0
1
2
2
3
1 – 2
of the following matrix by reducing it into normal form:
1 2 3 1
3
2 5
1
1 3 3 2
2 –1 6
3
Ans. 3
18. A
2 4 3 3
1 2 3 – 1
2
5 2 – 3
1 1 1 1
1 2 3
19. Rank of matrix A 1 4 2 is
2 6 5
(a) 0
(b) 1
(d) 2
1 5 4
20. For which value of ‘b’ the rank of the matrix A 0 3 2 is
b 13 10
(a) 1
(b) 2
(c) 3
(c) 3
(d) 0
Ans. 4
(AMIETE, June 2009) Ans. (d)
(AMIETE, Dec. 2009) Ans. (b)
4.39 SOLUTION OF SIMULTANEOUS EQUATIONS
The matrix of the coefficients of x, y, z is reduced into Echelon form by elementary row
transformations. At the end of the row transformation the value of z is calculated from the last
equation and value of y and the value of x are calculated by the backward substitution.
Example 33. Solve the following equations
x – y + 2z = 3, x + 2y + 3z = 5, 3x – 4y – 5z = – 13
Solution. In the matrix form, the equations are written in the following form.
2 x
1 1
3
2 x
1 1
3
1
2
3 y = 5 or 0
3
1 y 2 R2 R2 R1
3 4 5 z
R R3 3 R1
13
0 1 11 z
22 3
1 1
2 x 3
1
1 y = 2 R3 R3 R2
0 3
3
32 z 64
0 0
3
3
x– y+ 2 z=3
3 y + z= 2
...(1)
...(2)
32
64
z =
z=2
3
3
Putting the value of z in (2), we get 3y + 2 = 2 y = 0
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Determinants and Matrices
Putting the value of y, z in (1), we get x – 0 + 4 = 3 x = – 1
x = – 1, y = 0, z = 2
Ans.
Example 34. Find all the solutions of the system of equations
x1 + 2x2 – x3 = 1, 3x1 – 2x2 + 2x3 = 2, 7x1 – 2x2 + 3x3 = 5
Solution.
1
1 2 1 x1
2
3 2 2 x
2 =
5
7 2 3 x3
R2 R2 – 3R1, R3 R3 – 7R1
2 1 x1
1
1
1 1 2 1 x1
1 R R 2 R
0 8 5 x
1 , 0 8 5 x
3
2
2 =
2 = 3
0
2 0 0 0 x3
0 16 10 x3
x1 + 2 x2 – x3 = 1
– 8x2 + 5x3 = – 1
Let
...(1)
...(2)
x3 = k
Putting x3 = k in (2), we get – 8x2 + 5k = – 1 x2 =
1
(5k 1)
8
1
(5k 1) k = 1
4
5k 1
k 3
=
x1 = 1 k
4 4
4 4
k 3
5k 1
, x3 k
x1 = , x2 =
4 4
8 8
The equations have infinite solution.
Substituting the values of x3, x1 in (1), we get x1
Ans.
4.40 Gauss - Jordan Method
(R.G.P.V., Bhopal, III Semester, Dec. 2007)
This is modification of the Gaussian elimination method.
By this method we eliminate unknowns not only from the equations below but also from the
equations above. In this way the system is reduced to a diagonal matrix.
Finally each equation consists of only one unknown and thus, we get the solution. Here, the
labour of backward substitution for finding the unknowns is saved
Gauss-Jordan method is modification of Gaussian elimination method.
Example 35. Express the following system of equations in matrix form and solve them by the
elimination method ( Gauss Jordan Method)
2x1 + x2 + 2x3 + x4 = 6
6x1 – 6x2 + 6x3 + 12x4 = 36
4x1 + 3x2 + 3x3 – 3x4 = – 1
2 x1 + 2 x2 – x3 + x4 = 10
Solution. The equations are expressed in matrix form as
6
1 2
1 x1
2
36
6 6 6 12 x
2 =
1
4
3 3 3 x3
10
2 1
1 x4
2
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Determinants and Matrices
303
1
2
1 x1
2
0 9
0
9 x2
=
0
1 1 5 x3
1 3
0 x4
0
2
0
0
0
1
2
1
1
0 1
1 1 5
1 3
0
x1
x
2
x3
x4
6
18 R2 R2 3 R1
R R 2 R
3
1
13 3
R4 R4 R1
4
6
2
R R2
2
13
=
9
4
6
2
1 x1
2 1
2 R R R
0 1
0 1 x2
3
2
3
= 11
0 0 1 4 x3
1 x4
6 R4 R4 R2
0 0 3
1 x1 6
2 1 2
0 1 0 1 x 2
2 =
0 0 1 4 x3 11 R4 R4 3 R3
0 0 0 13 x4 39
2 x1 + x2 + 2 x3 + x4 = 6
x2 – x4 = – 2
– x3 – 4 x4 = – 11
13 x4 = 39 x4 = 3
Putting the value of x4 in (3), we get
– x3 – 12 = – 11 x3 = – 1
Putting the value of x4 in (2), we get
x2 – 3 = – 2 x2 = 1
Substituting the values of x4, x3 and x2 in (1), we get
2 x1 + 1 – 2 + 3 = 6 or 2 x1 = 4 x1 = 2
x1 = 2, x2 = 1, x3 = – 1, x4 = 3
Example 36. Find the general solution of the system of equations:
3x1 + 2x3 + 2x4 = 0
– x1 + 7x2 + 4x3 + 9x4 = 0
7 x1 – 7x2 – 5x4 = 0
Solution. The system of equations in the matrix form is expressed as
...(1)
...(2)
...(3)
Ans.
x1
0 2
2
0
3
x
2
1
7 4
9 = 0
x3
0
7 7 0 5
x4
x1
7 4
9
0
1
x2
3
0 2
2
= 0 R1 R2
x3
0
7 7 0 5
x4
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Determinants and Matrices
x1
1 7 4 9
0
0 21 14 29 x2
R2 R2 3 R1
x = 0 R R 7 R
3
3
1
0 3
0 42 28 58
x4
x1
1 7 4 9 0
0 21 14 29 x2 0 R R 2 R
3
2
x = 3
0 0 0 0 3 0
x4
– x1 + 7 x2 + 4 x3 + 9 x4 = 0
21 x2 + 14 x3 + 29 x4 = 0
Let
x4 = a, x3 = b
2 b 29 a
From (2), 21 x2 + 14 b + 29 a = 0 or x2 =
3
21
2 b 29 a
From (1), x1 7
4b 9a = 0
3
21
...(1)
...(2)
2a 2b
3
3
2
1
(29 a 14 b)
x 1 = ( a b) , x 2 =
3
21
x3 = b, x4 = a
Ans.
4.41 TYPES OF LINEAR EQUATIONS
(1) Consistent. A system of equations is said to be consistent, if they have one or more
solution i.e.
x + 2y = 4
x + 2y = 4
3x + 2y = 2
3x + 6y = 12
Unique solution
Infinite solution
(2) Inconsistent. If a system of equation has no solution, it is said to be inconsistent i.e.
x +2 y = 4
3x + 6y = 5
4.42 CONSISTENCY OF A SYSTEM OF LINEAR EQUATIONS
a11 x1 + a12 x2 + . . . a1n xn = b1
a21 x1 + a22 x2 + . . . a2n xn = b2
x1 =
..........................................................................
am1 x1 + am2 x2 + ... amn xn= bm
a11 a12 .......... a1n
a
21 a22 .......... a2 n
..............................
am1 am 2 .......... amn
x1 b1
x
2 = b2
... ....
xm bm
a11 a12 .......... a1n b1
a
21 a22 .......... a2 n b2
and C= [A, B] = .....................................
am1 am2 .......... amn bm
AX = B
is called the augmented matrix.
[ A : B] C
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Determinants and Matrices
305
(a) Consistent equations. If Rank A = Rank C
(i) Unique solution: Rank A = Rank C = n
(ii) Infinite solution: Rank A = Rank C = r, r < n
(b) Inconsistent equations. If Rank A Rank C.
In Brief :
where n = number of unknown.
A system of non-homogeneous linear equations
AX = B
Find R (A) and R (C)
R (A) = R (C)
R (A) R (C)
Solution exists, system
is consistent
R (A) = R (C)
= n (no. of unknowns)
Unique
solution
No solution, system
is inconsistent
R (A) = R (C) < n (no. of unknowns)
Infinite no. of
solutions
Example 37. Show that the equations
2x + 6y = – 11, 6x + 20y – 6z = – 3, 6y – 18z = – 1
are not consistent.
Solution. Augmented matrix C = [A, B]
0 : 11 2 6
0 : 11
2 6
6 20 6 : 3 ~ 0 2 6 : 30 R R 3 R
2
1
2
=
0 6 18 : 1 0 6 18 : 1
0 : 11
2 6
0 2 6 : 30 R R – 3 R
3
3
2
0 : 91
0 0
The rank of C is 3 and the rank of A is 2.
Rank of A Rank of C.
The equations are not consistent.
Example 38. Test the consistency and hence solve the following set of equation.
x1 + 2x2 + x3 = 2
Ans.
3x1 + x2 – 2x3 = 1
4x1 – 3x2 – x3 = 3
2x1 + 4x2 + 2x3 = 4
(U.P., I Semester, Compartment 2002)
Solution. The given set of equations is written in the matrix form:
2
1
2
1
x1
1
3
1 2
=
x
2
3
4 3 1
x3
4
2
4
2
AX = B
2
1 2
1
3
1
2
1
[
A
,
B
]
~
Here, we have augmented matrix C =
4 3 1 3
4
2 4
2
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Determinants and Matrices
2
1
2
1
0 5 5 5
~
0 11 5 5
0
0
0
0
2
1
2
1
1
R2 R2 3 R1
0
1
1
1 R2 R2
~
5
R3 R3 4 R1
0 11 5 5
R4 R4 2 R1
0
0
0
0
1
0
~
0
0
2 1 2
1 2 1 2
0 1 1 1
1 1 1
~
1
0 6 6 R3 R3 11 R2
0 0 1 1 R3 R3
6
0 0 0
0 0 0 0
Number of non-zero rows = Rank of matrix.
R(C) = R(A) = 3
Hence, the given system is consistent and possesses a unique solution. In matrix form the
system reduces to
2 1
2
x1
1 1 1
x2 =
0 1 1
x3
0 0 0
x1 + 2x2 + x3 = 2
...(1)
x2 + x3 = 1
...(2)
x3 = 1
From (2),
x2 + 1 = 1 x2 = 0
From (1),
x1 + 0 + 1 = 2 x1 = 1
Hence,
x1 = 1, x2 = 0 and x3 = 1
Ans.
Example 39. Test for consistency and solve :
5x + 3y + 7z = 4, 3x + 26y + 2z = 9, 7x + 2y + 10 z = 5
Solution. The augmented matrix C = [A, B]
(R.G. P.V. Bhopal I. Sem. April 2009-08-03)
1
0
0
0
3 7
4
1
:
5 3 7 : 4
5 5
5
3 26 2 : 9 3 26 2 : 9 R 1 R
1
1
5
7 2 10 : 5 7 2 10 : 5
3
7
4
3
7
4
:
1
:
1
5
5
5
5
5
5
121
11
33
121
11
33
R2 R2 3 R1 0
0
:
:
5
5
5
5
5
5
1
R2
1
3
0
0
0 : 0 R3 R3
0 11
: R3 R3 7 R1
11
5
5
5
Rank of A = 2 = Rank of C
Hence, the equations are consistent. But the rank is less than 3 i.e. number of unknows. So its
solutions are infinite.
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Determinants and Matrices
307
3
7
4
1
5
5
5 x
0 121 11 y
33
=
5
5
5
z
0
0
0
0
3
7
4
x y z =
5
5
5
33
121
11z
y
=
or 111y – z = 3
5
5
5
3
k
Let z = k then
11y – k = 3 or y =
11 11
33 k 7
4
16
7
x k =
k
or x =
Ans.
5
5 11 11 5
11
11
Example 40. Discuss the consistency of the following system of equations
2x + 3y + 4z = 11, x + 5y + 7z = 15, 3x + 11y + 13z = 25.
If found consistent, solve it.
(A.M.I.E.T.E., Winter 2001)
Solution. The augmented matrix C = [A, B]
2 3 4 11 1 5 7 15
1 5 7 15 ~ 2 3 4 11 R R
2
1
3 11 13 25 3 11 13 25
5
7
15
1
0 7 10 19
0 4 8 20
1
1
R 2 , R 3 R 3 , R3 R3 – R2
7
4
1 5 7 15
1 5 7 15
0 1 10 19 ~ 0 1 10 19
7 7
7 7
0 1 2 5
0 0 4 16
7 7
R2
R2 R2 – 2R1, R3 R3 – 3R1,
~
Rank of C = 3 = Rank of A
Hence, the system of equations is consistent with unique solution.
15
1 5 7
x
10
19
0
1
y
Now,
=
7
7
16
z
0 0 4
7
7
x + 5y + 7z = 15
y
10 z
19
=
7
7
...(1)
...(2)
4z
16
=
z=4
7
7
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Determinants and Matrices
19
10
y=–3
4 =
7
7
From (1), x + 5 (– 3) + 7 (4) = 15 x = 2
x = 2, y = – 3, z = 4
Ans.
Example 41. Find for what values of and the system of linear equations:
x+y+z=6
x + 2y + 5z = 10
2x + 3y + z =
has (i) a unique solution (ii) no solution
(iii) infinite solutions. Also find the solution for = 2 and = 8.
(Uttarakhand, 1st semester, Dec. 2006)
1
1
1
x
6
Solution. 1 2 5 y = 10
2 3 z
AX = B
1 1 1 : 6
1
:
6
1 1
1 2 5 : 10
0 1
4
:
4 R2 R2 R1
C = (A, B) =
~
2 3 :
0 1 2 : 12 R3 R3 2 R1
From (2),
y
1
:
6
1 1
0 1
4
:
4
~
0 0 6 : 16 R3 R3 R2
...(1)
(i)
A unique solution
If R (A) = R (C) = 3
then – 6 0 6 and – 16 0 16
(ii) No solutions
If R (A) R (C), then R (A) = 2 and R (C) = 3
– 6 = 0 = 6 and – 16 0 16
(iii) Infinite solutions
If R (A) = R (C) = 2
then – 6 = 0 and – 16 = 0
= 6 and = 16
(iv) Putting = 2 and = 8 in (1), we get
1 :
6
1 x
1 1
1 1
6
0 1
4 :
4
0 1
4
y 4
0 0 4 : 8
0 0 4 z
8
x+ y+ z=6
y + 4z = 4
– 4z = – 8
z=2
Putting z = 2 in (3), we get
y+8=4
y=–4
Putting y = – 4, z = 2 in (2), we get
x–4+2=6
x=8
Hence,
x = 8, y = – 4, z = 2
Ans.
Example 42. Find for what values of k the set of equations
2x – 3y + 6z – 5t = 3, y – 4z + t = 1, 4x – 5y + 8z – 9t = k
has (i) no solution (ii) infinite number of solutions.
(A.M.I.E.T.E., Summer 2004)
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Determinants and Matrices
309
Solution. The augmented matrix C = [A, B]
R3 R3 – 2 R1
.
6 5
3
6 5 . 3
2 3
2 3
0
. 1 ~ 0
. 1
1
4
1
1
4
1
4 5
0
8 9 . k
1 4
1 . k 6
6 5 . 3
2 3
~ 0
1 4
1 . 1
0
0
0
0 . k 7 R3 R3 R2
(i) There is no solution if R (A) R (C)
k – 7 0 or k 7, R (A) = 2 and R (C) = 3.
(ii) There are infinite solutions if R (A) = R (C) = 2
k – 7= 0 k = 7
x
3
6 5 y
2 3
= 1
0
1 4
1 z
t
2x – 3y + 6z – 5t = 3
y – 4z + t = 1
Let t = k1 and z = k2.
From (2), y – 4k2 + k1 = 1 or y = 1 + 4k2 – k1
From (l), 2x – 3 – 12k2 + 3k1 + 6k2 – 5k1 = 3
2x = 6 + 6k2 + 2k1 x = 3 + 3k2 + k1
y = 1 + 4k2 – k1 z = k2, t = k1
4.43. HOMOGENEOUS EQUATIONS
Ans.
...(1)
...(2)
Ans.
For a system of homogeneous linear equations AX = O
(i) X = O is always a solution. This solution in which each unknown has the value zero is
called the Null Solution or the Trivial solution. Thus a homogeneous system is always
consistent.
A system of homogeneous linear equations has either the trivial solution or an infinite
number of solutions.
(ii) If R (A) = number of unknowns, the system has only the trivial solution.
(iii) If R (A) < number of unknowns, the system has an infinite number of non-trivial solutions.
A system of homogeneous linear equations
AX = O
Always has a
solution
Find R (A)
R (A) = n (no. of unknowns)
Unique or trivial
solution
(each unknown equal to zero)
R (A) < n (no. of unknowns)
Infinite no. of non-trivial
solutions
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Determinants and Matrices
Example 43. Determine ‘b’ such that the system of homogeneous equations
2x + y +2z = 0 ;
x + y +3z = 0 ;
4x +3y + bz = 0
has (i) Trivial solution
(ii) Non-Trivial solution . Find the Non-Trivial solution using matrix method.
(U.P., I Sem Dec 2008)
Solution. Here, we have
2x + y + 2z = 0
x + y + 3z = 0
4x + 3y + bz = 0
(i) For trivial solution: We know that x = 0, y = 0 and z = 0. So, b can have any value.
(ii) For non-trivial solution: The given equations are written in the matrix form as :
2 1 2 x
1 1 3 y =
4 3 b z
AX=B
R1 R2,
2 1 2 :
C = 1 1 3 :
4 3 b :
0 1 1 3 :
0 ~ 2 1 2 :
0 4 3 b :
0
0
0
R2 R2 – 2R1, R3 R3 – 4R1,
R3 R3 – R2
0 1 1
3 : 0 1 1
3 : 0
0 ~ 0 1
4 : 0 ~ 0 1
4 : 0
0 0 1 b 12 : 0 0 0 b 8 : 0
For non trivial solution or infinite solutions R (C) = R (A) = 2 < Number of unknowns
b – 8 = 0, b = 8
Ans.
Example 44. Find the values of k such that the system of equations
x + ky + 3z = 0,
4x + 3y + kz = 0,
2x + y + 2z = 0
has non-trivial solution.
Solution. The set of equations is written in the form of matrices
0
1 k 3 x
4 3 k y = 0 , AX = B, C = [A : B] =
0
2 1 2 z
On interchanging first and third rows, we have
1 k 3 : 0
4 3 k : 0
2 1 2 : 0
2 1 2 : 0
4 3 k : 0
1 k 3 : 0
R2 R2 – 2 R1, R3 R3
2
1
~ 0
1
1
0 k
2
2
k 4
:
:
2
:
1
R1
2
1
R3 R3 k R2
2
0 2 1
2
0 ~ 0 1
k 4
1
0 0 0 2 k (k 4)
2
: 0
: 0
: 0
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Determinants and Matrices
311
For a non-trivial solution or for infinite solution, R (A) = R (C) = 2
1
k
2 k (k 4) = 0 2 k 2 4k 2 = 0
2
2
so
9
9
9
k = 0 k k = 0 k = , k = 0
Ans.
2
2
2
4.44 CRAMER’S RULE
Example 45. Find values of for which the following system of equations is consistent and
has non-trivial solutions. Solve equations for all such values of .
k2
( – 1) x + (3 + 1) y + 2z = 0
( – 1) x + (4 – 2) y + ( + 3) z = 0
2x + (3 + 1) y + 3 ( – 1) z = 0
2 x
( 1) (3 1)
0
( 1) (4 2) ( 3) y =
0
(3 1) (3 3) z
0
2
AX = 0
Solution.
For infinite solutions,
1 3 1
0
2
3 = 0,
3 1 3 3
0
0
3
1 4 2
2
3 1
3
3 = 0,
3 3
3
1 5 1
2
...(1)
|A|=0
1 4 2
2
(A.M.I.E.T.E., Summer 2010, 2001)
3 = 0,
6 2 3 3
( – 3) [( – 1) (6 – 2) – 2 (5 + 1)] = 0
[62 – 8 + 2 – 10 – 2] = 0 or 62 – 18 = 0 or 6 ( – 3) = 0, = 3
On putting = 3 in (1), we get
0 2 10 6 x 0
2 10 6 x
0 0 0 0 y 0
2 10 6 y
=
0 0 0 0 z 0
2 10 6 z
2x + 10y + 6z = 0 x + 5y + 3z = 0
Let
x = k1, y = k2, 3z = – k1 – 5 k2 z =
k1 5k 2
3
3
Ans.
EXERCISE 4.16
Test the consistency of the following equations and solve them if possible.
1.
3x + 3y + 2z = 1, x + 2y = 4, 10y + 3z = – 2, 2x – 3y – z = 5
Ans. Consistent, x = 2, y = 1, z = – 4
2.
(R.G.P.V. Bhopal 1st Sem 2001)
x1 – x2 + x3 – x4 + x5 = 1,
2x1 – x2 + 3x3 + 4x5 = 2,
3x1 – 2x2 + 2x3 + x4 + x5 = 1,
x1 + x3 + 2x4 + x5 = 0
(A.M.I.E.T.E., Winter 2003)
Ans. x1 = – 3k1 + k2 – 1, x2 = –3k1 – 1, x3 = k1 – 2k2 + 1, x4 = k1, x4 = k1, x5 = k21
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312
Determinants and Matrices
3. Find the value of k for which the following system of equations is consistent.
3x1 – 2x2 + 2x3 = 3, x1 + kx2 – 3x3 = 0, 4x1 + x2 + 2x3 = 7
Ans. k =
1
4
4. Find the value of for which the system of equations
x + y + 4z = 1, x + 2y – 2z = 1, x + y + z = 1
will have a unique solution.
(A.M.I.E., Winter 2000) Ans.
7
10
3 2 1 x b
5. Determine the values of a and b for which the system 5 8 9 y 3
2
1 a z 1
(i) has a unique solution, (ii) has no solution and, (iii) has infinitely many solutions.
1
1
, (iii) a = –3, b =
3
3
6. Choose that makes the following system of linear equations consistent and find the general solution of
the system for that .
x + y – z + t = 2, 2y + 4z + 2t = 3, x + 2y + z + 2t =
Ans. (i) a –3, (ii) a = –3, b
Ans. =
7
1
3
, x = 3 k 2 , y = 2 k2 k1 , z = k2, t = k1
2
2
2
7. Show that the equations
3x + 4y + 5z = a, 4x + 5y + 6z = b, 5x + 6y + 7z = c
don’t have a solution unless a + c = 2b.
Solve the equations when a = b = c = – 1
Ans. x = k + 1, y = – 2 k – 1,z = k
8. Find the values of k, such that the system of equations
4 x1 + 9x2 + x3 = 0 , kx1 + 3x2 + kx3 = 0, x1 + 4x2 + 2x3 = 0
has non-trivial solution. Hence, find the solution of the system.
Ans. k = 1, x1 = 2 , x2 = –, x3 =
9. Find values of for which the following system of equations has a non-trivial solution.
3x1 + x2 – x3 = 0, 2x1 + 4x2 + x3 = 0, 8x1 – 4x2 – 6x3 = 0
Ans. = 1
10. Find value of so that the following system of homogeneous equations have exactly two linearly
independent solutions
x1 – x2 – x3 = 0, – x1 + x2 – x3 = 0, – x1 – x2 + x3 = 0,
Ans. = – l
11. Find the values of k for which the following system of equations has a non-trivial solution.
(3k – 8) x + 3y + 3z = 0, 3x + (3k – 8) y + 3z = 0, 3x + 3y + (3k – 8) z = 0 (AMIETE, June 2010)
Ans. k =
12.
Solve the homogeneous system of equations :
4x + 3y – z = 0, 3x + 4y + z = 0, x – y – 2z = 0, 5x + y – 4z = 0
2 11
,
3 3
Ans. x = k, y = – k, z = k
1 2 1
13. If A = 3 1 2
Ans. (i) 1, (ii) – 1
1
0
find the values of for which equation AX = 0 has (i) a unique solution, (ii) more than one solution.
14. Show that the following system of equations:
x + 2y – 2u = 0, 2x – y – u = 0, x + 2z – u = 0, 4x – y + 3z – u = 0
do not have a non-trivial solution.
15. Determine the values of and such that the following system has (i) no solution (ii) a unique solution
(iii) infinite number of solutions:
2x – 5y + 2z = 8,
2x + 4y + 6z = 5,
x + 2y + z =
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Determinants and Matrices
313
5
5
(ii) 3, (iii) = 3, =
2
2
16. Test the following system of equations for consistency. If possible, solve for non-trivial solutions.
Ans. (i) = 3,
3x + 4y – z – 6t = 0, 2x + 3y + 2z – 3t = 0, 2x + y – 14z – 9t = 0, x + 3y + 13z + 3t = 0
(A.M.I.E.T.E., Winter 2000) Ans. x = 11k1 + 6k2, y = –8k1 – 3k2, z = k1 t = k2
17. Given the following system of equations
2x – 2y + 5z + 3z = 0, 4x – y + z + w = 0, 3x – 2y + 3z + 4w = 0, x – 3y + 7z + 6w = 0
Reduce the coefficient matrix A into Echelon form and find the rank utilising the property of rank, test the
given system of equation for consistency and if possible find the solution of the given system.
(A.M.I.E.T.E., Summer 2001) Ans. x = 5k, y = 36k, z = 7k, w = 9k
18. Find the values of for which the equations
(2 – ) x + 2y + 3 = 0,
2x + (4 – ) y + 7 = 0,
2x + 5y + (6 – ) = 0
are consistent and find the values of x and y corresponding to each of these values of .
(R.G.P.V, Bhopal I sem. 2003, 2001) Ans. = 1, – 1, 12.
4.45 LINEAR DEPENDENCE AND INDEPENDENCE OF VECTORS
Vectors (matrices) X1, X2, .... Xn are said to be dependent if
(1) all the vectors (row or column matrices) are of the same order.
(2) n scalars 1, 2, ... n (not all zero) exist such that
1 X1 + 2 X2 + 3 X3 + ..... + n Xn = 0
Otherwise they are linearly independent.
Remember: If in a set of vectors, any vector of the set is the combination of the remaining
vectors, then the vectors are called dependent vectors.
Example 46. Examine the following vectors for linear dependence and find the relation if it
exists.
X1 = (1, 2, 4), X2 = (2, –1, 3), X3 = (0, 1, 2), X4 = (–3, 7, 2)
(U.P., I Sem. Winter 2002)
Solution. Consider the matrix equation
1 X1 + 2 X2 + 3 X3 + 4 X4 = 0
1 (1, 2, 4) + 2 (2, –1, 3) + 3 (0, 1, 2) + 4 (– 3, 7, 2) = 0
1 + 22 + 03 – 34 = 0
21 – 2 + 3 + 74 = 0
41 + 32 + 23 + 24 = 0
This is the homogeneous system
1
1 2 0 3 0
2 1 1 7 2
= 0 or A = 0
3
2 0
4 3 2
4
1
1 2 0 3
0
0 5 1 13 2
= 0
3
0 5 2 14
0
4
1
1 2 0 3 0
0 5 1 13 2
= 0
3
0 0 1 1 0
4
R2 R2 2 R1
R3 R3 4 R1
R3 R3 R2
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314
Determinants and Matrices
1 + 2 2 – 3 4 = 0
–5 2 + 3 + 13 4 = 0
3 + 4 = 0
4 = t, 3 + t = 0, 3 = – t
Let
12 t
5
9 t
24 t
1
3 t = 0 or =
1
5
5
Hence, the given vectors are linearly dependent.
Substituting the values of in (1), we get
– 52 – t + 3 t = 0, 2 =
9 X 1 12 X 2
9 t X 1 12 t
X3 X4 = 0
X2 t X3 t X4 = 0
5
5
5
5
9 X1 – 12 X2 + 5 X3 – 5 X4 = 0
–
Ans.
Example 47. Define linear dependence and independence of vectors.
Examine for linear dependence [1, 0, 2, 1], [3, 1, 2, 1], [4, 6, 2, –4], [–6, 0, –3, –4] and find
the relation between them, if possible.
Solution. Consider the matrix equation
1 X1 + 2 X2 + 3 X3 + 4 X4 = 0
...(1)
1 (1, 0, 2, 1) + 2 (3, 1, 2, 1) + 3 (4, 6, 2, – 4) + 4 (– 6, 0, – 3, – 4) = 0
1 + 3 2 + 4 3 – 6 4 = 0
0 1 + 2 + 6 3 + 0 4 = 0
2 1 + 2 2 + 2 3 – 3 4 = 0
1 + 2 – 4 3 – 4 4 = 0
1
0
2
1
3
4 6 1
0
0
1 6 0 2
=
2 2 3 3
0
1 4 4 4
0
0
1 3 4 6 1
0
0
1 6
0 2
=
0 R3 R3 2 R1
0 4 6
9 3
2 4
0 R4 R4 R1
0 2 8
1 3 4 6 1
0
0 1 6
0
0 2
=
0 0 18 9 3
0 R3 R3 4 R2
2 4
0 0 4
0 R4 R4 2 R2
1
0
0
0
3 4 6 1
0
0
1 6
0 2
=
0 18 9 3
0
2
0 0
0 4
0 R4 R4 R3
9
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Determinants and Matrices
315
1 + 3 2 + 4 3 – 6 4 = 0
2 + 6 3 = 0
18 3 + 9 4 = 0
t
2
2 – 3 t = 0 or 2 = 3 t
1 + 9 t – 2t – 6t = 0
1 = – t
Substituting the values of 1, 2, 3 and 4 in (1), we get
Let 4 = t,
18 3 + 9 t = 0 or 3 =
t
X + t X4 = 0 or 2 X1 – 6 X2 + X3 – 2 X4 = 0
Ans.
2 3
4.46 LINEARLY DEPENDENCE AND INDEPENDENCE OF VECTORS BY RANK
METHOD
1. If the rank of the matrix of the given vectors is equal to number of vectors, then the vectors
are linearly independent.
2. If the rank of the matrix of the given vectors is less than the number of vectors, then the
vectors are linearly dependent.
Example 48. Is the system of vector
– t X1 + 3 t X2
T
T
T
X 1 2, 2,1 , X 2 1, 3,1 , X 3 1, 2, 2
linearly dependent .
2
1
1
2 , X 3 X 2
X
Solution . Here 1 2 3
1
1
2
Consider the matrix equation
(T stands for transposition )
1 X 1 2 X 2 3 X 3 0
...(1)
2
1
1 0
1 2 2 3 3 2 0
1
1
2 0
21 2 3 0
21 3 2 23 0
1 2 23 0
which is the homogeneous equation.
R1 , R3
2 1 1 1 0
2 3 2 0
2
or
1 1 2 3 0
R 2 – 2R 1, R 3 – 2R 1
1 1 2 1 0
0 1 2 0
2
0 1 3 3 0
1 1 2 1 0
2 3 2 0
2
2 1 1 3 0
R3 + R2
1 1 2 1 0
or 0 1 2 2 0
0 0 5 3 0
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316
Determinants and Matrices
1 + 2 + 23 = 0
2 – 23 = 0
–53 = 0 3 = 0
2 = 0 and 1 = 0
Thus non-zero values of 1, 2, 3 do not exist which can satisfy (1). Hence by definition,
the given system of vectors is not linearly dependent.
Ans.
Example 49. Show using a matrix that the set of vectors
X = [1, 2,– 3, 4], Y = [3, – 1, 2, 1] , Z = [1, –5, 8, –7] is linearly dependent.
Solution. Here, we have
X = [1, 2, –3, 4], Y = [3, –1, 2, 1], Z = [1, –5, 8, –7]
Let us form a matrix of the above vectors
4
1 2 3 4 1 2 3
1
3 1 2
1 0 7 11 11 R2 R2 3R1 0
1 5 8 7 0 7 11 11 R3 R3 R1
0
Here the rank of the matrix = 2 < Number of vectors
Hence, vectors are linearly dependent.
Example 50. Show using a matrix that the set of vectors
[4, 5, 14, 14], [5, 10, 8, 4] is linearly independent.
Solution. Here, the given vectors are
[2, 5, 2, –3], [3, 6, 5, 2], [4, 5, 14, 14], [5, 10, 8, 4]
Let us form a matrix of the above vectors :
2 3 4
7 11 11
0 0
0 R3 R3 R2
Proved.
: [2, 5, 2, –3], [3, 6, 5, 2],
2 5 2 3 2 5 2 3
3 6 5 2 1 1 3
5 R2 R2 R1
4 5 14 14 1 1 9 12 R3 R3 R2
5 10 8 4 1 5 6 10 R4 R4 R3
5 R1 R2
1
3
5
1 1 3
1
0
2 5 2 3 R R
3 4 13 R2 R2 2 R1
1
2
0 2
1 1 9 12
6
7 R3 R3 R1
4 9 15 R4 R4 R1
0
1 5 6 10
3
5
1 1
1 1
0 3 4 13
0 3
10
5
2
0 0
R3 R3 R2 0 0
3
3
3
7
4
0 0 11
0 0
R4 R4 R2
3
3
3
Here, the rank of the matrix = 4 = Number of vectors
Hence, the vectors are linearly independent.
3
5
4 13
10 5
3
3
11
1
R4 R4 R3
0
10
2
Proved.
EXERCISE 4.17
Examine the following system of vectors for linear dependence. If dependent, find the relation between them.
1.
X1 = (1, –1, 1), X2 = (2, 1, 1), X3 = (3, 0, 2).
2.
X1 = (1, 2, 3), X2 = (2, – 2, 6).
Ans. Dependent, X1 + X2 – X3 = 0
Ans. Independent
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Determinants and Matrices
317
3.
X1 = (3, 1, – 4), X2 = (2, 2, – 3), X3 = (0, – 4, 1).
Ans. Dependent, 2 X1 – 3 X2 – X3 = 0
4.
X1 = (1, 1, 1, 3), X2 = (1, 2, 3, 4), X3 = (2, 3, 4, 7).
Ans. Dependent, X1 + X2 – X3 = 0
5.
X1 = (1, 1, –1, 1), X2 = (1, –1, 2, –1), X3 = (3, 1, 0, 1).
Ans. Dependent, 2 X1 + X2 – X3 = 0
6.
X1 = (1, –1, 2, 0), X2 = (2, 1, 1, 1), X3 = (3, –l, 2, –l), X4 = (3, 0, 3, 1).
Ans. Dependent, X1 + X2 – X4 = 0
7. Show that the column vectors of following matrix A are linearly independent:
1 0 0
A = 6 2 1
4 3 2
8. Show that the vectors x1 = (2, 3, 1, –1), x2 = (2, 3, 1, –2), x3 = (4, 6, 2, 1) are linearly dependent. Express
one of the vectors as linear combination of the others.
9. Find whether or not the following set of vectors are linearly dependent or independent:
(i) (1, –2), (2, 1), (3, 2)
(ii) (1, 1, 1, 1), (0, 1, 1, 1), (0, 0, 1, 1), (0, 0, 0, 1).
Ans. (i) Dependent (ii) Independent
10. Show that the vectors x1 = (a1, b1), x2 = (a2, b2) are linearly dependent if a1 b2 – a2 b1 = 0.
4.47 ANOTHER METHOD (ADJOINT METHOD) TO SOLVE LINEAR EQUATIONS
Let the equations be
a1 x + a2 y + a3 z = d1
b1 x + b2 y + b3 z = d2
c1 x + c2 y + c3 z = d3
We write the above equations in the matrix form
a1 a2
a1 x a2 y a3 z d1
b b
b x b y b z d
2
2
3
1
= 2 or 1
c1 c2
c1 x c2 y c3 z d3
AX = B
a3 x
d1
b3 y = d2
d3
c3 z
...(1)
d1
x
a1 a2 a3
where A = b1 b2 b3 , X = y and B = d2
d3
z
c1 c2 c3
–
1
Multiplying (1) by A .
A– 1 AX = A– 1 B or IX = A–1 B or X = A–1 B.
Example 51. Solve, with the help of matrices, the simultaneous equations
x + y + z = 3, x + 2y + 3z = 4, x + 4y + 9z = 6
(A.M.I.E., Summer 2004, 2003)
Solution. The given equations in the matrix form are written as below:
1 1 1 x
3
1 2 3 y
= 4
1 4 9 z
6
AX = B
1 1 1
x
1 2 3 , X
where
A=
= y , B =
1 4 9
z
–
1
Now we have to find out the A .
3
4
6
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318
Determinants and Matrices
| A | = l × 6 + l × (– 6) + l × 2 = 6 – 6 + 2 = 2
1
2
6 5
6 6
6
5
8 2
8 3 , Adjoint A =
Matrix of co-factors =
1
1
2 3
1 2
1
6 5
1
1
–
1
8 2
A =
Adjoint A = 6
2
| A|
1
2 3
1 3
6 5
1
8 2 4
X = A–1 B = 6
2
1 6
2 3
x
18 20 6
4 2
1
1
y
=
18 32 12
2 1
2
2
z
0 0
6 12 6
x = 2, y = 1, z = 0
Example 52. Given the matrices
1 2 3
x
1
3 –1 1 , X y and C 2
A
4 2 1
z
3
Ans.
Write down the linear equations given by AX = C and solve for x, y, z by the matrix method.
Solution.
AX = C
1 2 3 x
1
3 –1 1 y
= 2
4 2 1 z
3
X = A–1 . C
x
1 2 3
y
= 3 –1 1
z
4 2 1
1
1
2
3
1 10
3
4 11 6
Matrix of co-factors of A
=
5
8 7
| A | = 1 (–3) + 2 (1) + 3 (10) = –3 + 2 + 30 = 29
5
3 4
1 11 8
Adj. A =
10 6 7
5
3 4
1
1
Adj. A =
1 11 8
|A|
29
10 6 7
X = A–1 C
A–1 =
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Determinants and Matrices
319
5 1
3 4
x
1
y
1 11 8 2
= 29
10 6 7 3
z
20
29
3 8 15
20
x
1
1
3
y
1 22 24 =
3 =
= 29
29
29
10 12 21
1
z
1
29
20
3
1
Hence,
x =
,y=
,z=
29
29
29
Example 53. Let y1 5 x1 3x2 3x3 , y2 3x1 2 x2 2 x3 , y3 2 x1 x2 2 x3
Ans.
be a linear transformation from x1 , x2 , x3 to y1 , y2 , y3 and
z1 4 x1 2 x3 ,
z2 x2 4 x3 ,
z3 5x3
be a linear transformation from x1 , x2 , x3 to z1 , z2 , z3 .Find the linear transformation
from z1 , z2 , z3 to y1 , y2 , y3 by inverting appropriate matrix and matrix multiplication.
(A.M.I.E.T.E.,Dec. 2004)
y1 5 3 3 x1
y 3 2 2 x2
Solution:
Hence 2
...(1)
y3 2 1 2 x3
z1 4 0 2 x1
z 0 1 4 x2
and 2
z3 0 0 5 x3
1
x1 4 0 2 z1
5 0 2 z1
1
x2 0 1 4 z 2
0 20 16 z2
20
x3 0 0 5 z3
0 0
4 z3
...(2)
x1
x
Putting the value of 2 from (2) in (1) , we get
x3
y1 5 3 3
5 0 2 z1
25 60 46 z1
1
1
y2 3 2 2 20 0 20 16 z2 20 15 40 46 z2
y3 2 1 2
0 0
10 20 20 z3
4 z3
Ans.
EXERCISE 4.18
Solve the following equations
1.
3x + y + 2z = 3, 2x – 3y – z = – 3, x + 2y + z = 4
2.
x + 2y + 3z = 1, 2x + 3y + 8z = 2, x + y + z = 3
(A.M.I.E. Winter 2001)
Ans. x = 1, y = 2, z = – 1
9
1
Ans. x , y 1, z
2
2
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320
Determinants and Matrices
3.
4.
5.
4x + 2y – z = 9, x – y + 3z = – 4, 2x + z = 1
5x + 3y + 3z = 48, 2x + 6y – 3z = 18, 8x – 3y + 2z = 21
x + y + z = 6, x – y + 2z = 5, 3x + y + z = 8
Ans. x =1, y = 2, z = – 1
Ans. x = 3, y = 5, z = 6
Ans. x = 1, y = 2, z = 3
6.
x + 2y + 3z = 1, 3x – 2y + z = 2, 4x + 2y + z = 3
Ans. x
7.
9x + 4y + 3z = – 1, 5x + y + 2z = 1, 7x + 3y + 4z = 1
8.
x + y + z = 8, x – y + 2z = 6, 9x + 5y – 7z = 14
9.
3x + 2y + 4z = 7, 2x + y + z = 4, x + 3y + 5z = 2
10.
Represent each of the transformations
x1 = 3 y1 + 2 y2, x2 = – y1 + 4 y2
and
7
3
1
,y
,z
10
40
20
Ans. x = 0, y = – 1, z = 1
5
4
Ans. x = 5, y = , z =
3
3
Ans. x
9
9
5
,y ,z
4
8
8
y1 = z1 + 2z2, y2 = – 3 z1
by the use of matrices, find the composite transformation which expresses x 1, x 2 in terms
of z1, z2.
Ans. x1 = – 3z1 + 6 z2, x2 = – 13z1 – 2z2
4.48 PARTITIONING OF MATRICES
Sub matrix. A matrix obtained by deleting some of the rows and columns of a matrix A is said
to be sub matrix.
4 1 0
4 1 5 2 1 0
For example, A = 5 2 1 , then
,
,
are the sub matrices.
5 2 6 3 2 1
6 3 4
Partitioning: A matrix may be subdivided into sub matrices by drawing lines parallel to its
rows and columns. These sub matrices may be considered as the elements of the original
matrix.
2 1 : 0 4 1
1 0 : 2 3 4
For example,
A=
.... .... : .... .... ....
4 5 : 1 6 5
2 1
0 4 1
A11 = 1 0 ,
A12 = 2 3 4
A21 = [4 5],
A22 = [1 6 5]
A11 A12
A = A
21 A22
So, the matrix is partitioned. The dotted lines divide the matrix into sub-matrices. A11, A12, A21,
A22 are the sub-matrices but behave like elements of the original matrix A. The matrix A can be
partitioned in several ways.
Addition by submatrices: Let A and B be two matrices of the same order and are partitioned
identically.
For example;
3 1 4 6
2 3 4 5
2 1 0 4
0 1 2 3
B
A=
4 5 1 2
3 4 5 6
4 5 0 1
1 3 4 5
Then we may write
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Determinants and Matrices
321
A11 A12
B11 B12
A
, B B
A
B22
A = 21
22
21
A31 A32
B31 B32
A11 B11 A12 B12
A B
A22 B22
21
A + B = 21
A31 B31 A32 B32
4.49 MULTIPLICATION BY SUB-MATRICES
Two matrices A and B, which are conformable to the product AB are partitioned in such a way
that the columns of A partitioned in the same way as the rows of B are partitioned. But the rows of
A and columns of B can be partitioned in any way.
For example, Here A is a 3 × 4 matrix and B is 4 × 3 matrix.
4 5 6
3 2 1
1 2 3 4
0 1 2 3 and B 1 0 4
A =
1 4 1 2
2 5 3
The partitioning of the columns of A is the same as the partitioning of the rows of B. Here, A
is partitioned after third column, B has been partitioned after third row.
Example 54. If C and D are two non-singular matrices, show that if
C
A =
0
Solution. Let
Then
C 1
0
1
,
then
A
D
0
E F
A–1 = G H
C 0 E
AA–1 = 0 D G
CE 0G CF 0 H
0 E DG 0 F DH
CE + 0G
CF + 0H
0E + DG
OF + DH
Since, C is non singular and
CF
CE
Similarly, D is non singular and DG
Putting these values in (1), we get
So that
0
D 1
...(1)
F CE 0G CF 0 H
H 0 E DG 0 F DH
I 0
= 0 I
= I CE = I
= 0 CF = 0
= 0 DG = 0
= I DH = I
= 0,
F=0
= I E = C–1
= 0 G = 0 and DH = I H = D–1
C 1
0
=
Proved.
1
0 D
4.50 Inverse By Partitioning: Let the matrix B be the inverse of the matrix A. Matrices A and B
are partitioned as
A11 A12
B11 B12
A = A
, B B
A
21
22
21 B22
Since,
AB = BA = I
A–1
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Determinants and Matrices
A11
A
21
A11 B11 A12 B21
A B A B
21 11
22 21
A12 B11
A22 B21
B12
B11
=
B
B22
21
B12 A11
B22 A21
A11 B12 A12 B22
B11 A11 B12 A21
=
B A B A
A21 B12 A22 B22
21 11
22 21
A12 I 0
A22 0 I
B11 A12 B12 A22 I 0
B21 A12 B22 A22 0 I
Let us solve the equations for B11, B12, B21 and B22.
Let,
B22 = M –1
From (2),
1
1
B12 = A11
( A22 B22 ) ( A11
A22 ) M 1
From (3),
1
1
1
B21 = ( B22 A21 ) A11 M ( A21 A11 )
From (1),
1
1
1
1
B11 = A11
A11
( A12 B21 ) A11
( A11
A12 ) B21
1
1
1
= A11
( A11
A12 ) M 1 ( A21 A11
)
1
M = A22 – A21 ( A11
A22 )
Here
Note: A is usually taken of order n – 1.
Example 55. Find the inverse of the following matrix by partitioning
1 3 3
1 4 3
1 3 4
Solution. Let the matrix be partitioned into four submatrices as follows:
Let
1 3 3
A = 1 4 3
1 3 4
1 3
3
; A12
A11 =
1 4
3
A21 = [1
B11
We have to find A–1 =
B21
3]; A22 = [4]
B12
where
B22
1
1
1
B11 = A11
( A11
A12 ) ( M 1 ) ( A21 A11 )
1
B21 = M 1 ( A21 A11
)
B12 = A111 A12 M 1 ; B22 = M
and
Now
–1
M = A22 A21 ( A111 A12 )
4 3
4 3 3
3
A111 = 1 1 ; A111 A12 = 1 1 3 = 0
4 3
A21 A111 = 1 3 1 1 = 1 0
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Determinants and Matrices
323
3
M = [4] [1 3] 0 = [4] – [3] = [1]
M–1 = [3]
4 3 3
4 3 3 0
7 3
B11 = 1 1 0 [1 0] = 1 1 0 0 B111 = 1 1
B21 = [1] [1 0] = [1 0]
3
B12 =
0
B22 = [1]
B11
A–1 = B
21
7 3 3
B12
= 1 1 0
B22
1 0
1
Ans.
1 2 3 1
1 3 3 2
by partitioning.
Example 56. Find the inverse of A =
2 4 3 3
1 1 1 1
1 2 3
1 3 3
and partition so that
Solution. (a) Take G3 =
2 4 3
1 2
3
A11 = 1 3 , A12 = 3 , A21 = 2 4 , and A22 = [3]
Now,
3 2 3
3
3 2
1
, A11
A111 =
A12 = 1 1 3 = 0 ,
1 1
3 2
1
= 2 4 1 1 = 2 0
A21 A11
3
1
M = A22 A21 ( A11
A12 ) = [3] [2 4] 0 = [–3], And M–1 = [1 3]
Then
3 2 3 1
3 2 2 0
1
1
1
B11 = A11
( A11
A12 ) M 1( A21 A11
) = 1 1 0 3 [2 0] = 1 1 0 0
=
1 3 6
3
3 3
1 3
1
B12 = ( A11
A12 ) M 1 = 3 0
1
1
B21 = M 1 ( A21 A11
) = [2 0]
3
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324
Determinants and Matrices
1
B22 = M–1 =
3
1
3
and
G
3 6 3
B12
1
= 3 3 0
B22
3
2 0 1
B11
= B
21
1 2 3
1 3 3
, A12 =
(b) Partition A so that A11 =
2 4 3
1
Now, A11
1
2
, A21 = 1 1 1 , and A22 = [1].
3
3 6 3
0
1
1
1
1
1
3
3
0
=
, A11 A12 = 3 3 , A21 A11 = 3 2 3 2
3
2
0 1
1
0
1
1
M = [1] [1 1 1] 3 = . and M–1 = [3]
3
3
1
Then
B11
3
1
= 3
3
2
3
1
= 3
3
2
6
3
0
1
1
3 0 3 [3] [2 3 2]
3
3
1
0 1
6
3
3
0
0 1
0
1
0 6 9
6 1
3
2
0 1
3 2 0
2
2
1
2
1 1
0
3 ,
B12 = B21 = [– 2 3 – 2], B22 = [3]
1
1
0
1 2
2 3
B11 B12 1 2
A–1 =
1 1
1
B21 B22 0
3 2
3
2
Ans.
EXERCISE 4.19
1. Compute A + B using partitioning
4
6
A=
0
1
1 0 5
3
1
7 8 1
, B
2
2 1 1
2 0 1
0
2 1 1
0 1 1
1 2 1
1 2 3
2. Compute AB using partitioning
2
1 2 0 1
0
A = 4 1 3 2 , B
4
2 1 3 0
2
3 1
4 6 11
1 4
24 18 18
Ans.
1 2
16 10 12
1 2
A B
3. Find the inverse of
C 0
where B, C are non-singular.
0
Ans. 1
B
C 1
B
1
AC
1
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Determinants and Matrices
325
Find the inverse of the following metrices by partitioning:
4.
2 1 1
1 3 2
1 2
1
6.
2 3 4
4 3 1
1 2 4
8.
3
2
52
2
Ans.
1 3 5
1
3 1
5
10
5 5 5
4
9
10
1
Ans. 15 4 14
5
5
1
6
5.
7.
1 2 1
3 1 5
1 1 2
1
Ans.
5 3 1
14
2 1
1
1 5 3
1 2 3
2 4 5
3 5 6
4 2 7
3 3 2
7 3 9
3 2 3
1 3 2
3
3 1
Ans.
2 1 0
1 11 7 26
16
1 1 7 3
Ans.
1 1
0
2 1
2
1 1 1
Choose the correct answer:
9.
If 3x + 2y + z = 0, x + 4y + z = 0, 2x + y + 4z = 0, be a system of equations then
(i) System is inconsistent
(ii) it has only trivial solution
(iii) it can be reduced to a single equation thus solution does not exist
(iv) Determinant of the coefficient matrix is zero.
(AMIETE, June 2010) Ans. (ii)
4.51 EIGEN VALUES
y1
a1n x1
y
a2 n x2
2
a3 n x3 = y3
yn
ann xn
AX = Y
...(1)
Where A is the matrix , X is the column vector and Y is also column vector.
Here column vector X is transformed into the column vector Y by means of the square
matrix A.
Let X be a such vector which transforms intoX by means of the transformation (1). Suppose
the linear transformation Y = AX transforms X into a scalar multiple of itself i.e. X.
AX = Y = X
AX – IX = 0
(A – I) X = 0
...(2)
Thus the unknown scalar is known as an eigen value of the matrix A and the corresponding
non zero vector X as eigen vector.
The eigen values are also called characteristic values or proper values or latent values.
a11 a12 a13
a
21 a22 a23
Let a31 a32 a33
an1 an 2 an 3
Let
2 2 1
A 1 3 1
1 2 2
1
2 2 1
1 0 0 2 2
A I 1 3 1 0 1 0 1
3 1
1 2 2
0 0 1 1
2
2
characteristic matrix
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Determinants and Matrices
(b) Characteristic Polynomial: The determinant | A – I | when expanded will give a
polynomial, which we call as characteristic polynomial of matrix A.
2
For example; 1
1
2
1
3 1
2
2
= ( 2 – ) (6 – 5 + 2 – 2) – 2 (2 – – 1) + 1( 2 – 3 + )
= – 3 + 7 2 – 11 + 5
(c) Characteristic Equation: The equation | A – I | = 0 is called the characteristic equation
of the matrix A e.g.
3 – 72 + 11 – 5 = 0
(d) Characteristic Roots or Eigen Values: The roots of characteristic equation | A – I | = 0
are called characteristic roots of matrix A. e.g.
3 – 7 2 + 11 – 5 = 0
( – 1) ( – 1) ( – 5) = 0
= 1, 1, 5
Characteristic roots are 1, 1, 5.
Some Important Properties of Eigen Values
(AMIETE, Dec. 2009)
(1) Any square matrix A and its transpose A have the same eigen values.
Note. The sum of the elements on the principal diagonal of a matrix is called the trace of the
matrix.
(2) The sum of the eigen values of a matrix is equal to the trace of the matrix.
(3) The product of the eigen values of a matrix A is equal to the determinant of A.
(4) If 1, 2, ... n are the eigen values of A, then the eigen values of
m
(i) k A are k 1, k 2, ....., k n
(ii) Am are 1m , m
2 ,......., n
(iii) A–1 are
1
1
,
, ....,
.
1 2
n
6 2 2
Example 57. Find the characteristic roots of the matrix 2 3 1
2 1 3
Solution. The characteristic equation of the given matrix is
6
2
2
2
3
1
0
2
1
3
(6 – ) (9 – 6 + 2 – 1) + 2 (–6 + 2 + 2) + 2(2 – 6 + 2) = 0
–3 + l2 2 – 36 + 32 = 0
By trial, = 2 is a root of this equation.
( – 2) (2 – 10 + 16) = 0 – 2) ( – 2) ( – 8) = 0
= 2, 2, 8 are the characteristic roots or Eigen values.
1 2 3
Example 58. The matrix A is defined as A 0 3 2
0 0 2
Find the eigen values of 3 A3 + 5 A2 – 6A + 2I.
Solution. | A – I | = 0
Ans.
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Determinants and Matrices
327
1
2
0
3
3
2
0
0
0
2
(1 – ) (3 – ) (–2 – ) = 0 or = 1, 3, – 2
Eigen values of A3 = 1, 27, –8;
Eigen values of A2 = 1, 9, 4
Eigen values of A = 1, 3, –2;
Eigen values of I = 1, 1, 1
3
2
Eigen values of 3 A + 5A – 6A + 2I
First eigen value
= 3 (1)3 + 5 (1)2 – 6 (1) + 2(1) = 4
Second eigen value = 3 (27) + 5 (9) – 6 (3) + 2(1) = 110
Third eigen value = 3 (–8) + 5 (4) – 6 (–2) + 2 (1) = 10
Required eigen values are 4, 110, 10
m
Ans.
Example 59.
If 1, 2, .... n are the eigen values of A, find the eigen values of the
a
r
t
r
i
x
2
(A – I) .
Solution. (A – I)2 = A2 – 2 AI + 2 I2 = A2 – 2 A + 2 I
Eigen values of A2 are 12 , 22 , 33 ... 2n
Eigen values of 2 A are 2 1,
2 2,
Eigen values of 2 I are 2.
Eigen values of A2 – 2 A + 2 I
12 21 2 ,
2
2 3 ... 2 n.
22 2 2 2 , 32 23 2 ... .....
2
2
Ans.
1 ,
2 , 3 , ... ( n )2
Example 60. Prove that a matrix A and its transpose A have the same characteristic roots.
Solution. Characteristic equation of matrix A is
| A – I | = 0
... (1)
Characteristic equation of matrix A is
| A – I | = 0
...(2)
Clearly both (1) and (2) are same, as we know that
| A | = | A |
i.e., a determinant remains unchanged when rows be changed into columns and columns into
rows.
Proved.
Example 61. If A and P be square matrices of the same type and if P be invertible, show that
the matrices A and P–1 AP have the same characteristic roots.
Solution. Let us put B = P–1 AP and we will show that characteristic equations for both A and
B are the same and hence they have the same characteristic roots.
B –I = P– 1 AP – I = P–1 AP – P–1 lP = P–1 (A – I) P
| B – I | = |P–1 (A – I) P | = | P –1 | |A – I| | P |
= |A – I | | P–1 | | P | = |A – I| | P–1P|
= |A –I | | I | = | A – I| as | I | = 1
Thus the matrices A and B have the same characteristic equations and hence the same
characteristic roots.
Proved.
Example 62. If A and B be two square invertible matrices, then prove that AB and BA have
the same characteristic roots.
Solution. Now
AB = IAB = B–1 B (AB) = B –1 (BA) B
...(1)
But by Ex. 8, matrices BA and B–1 (BA) B have same characteristic roots or matrices BA and
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Determinants and Matrices
AB by (1) have same characteristic roots.
Proved.
Example 63. If A and B be n rowed square matrices and if A be invertible, show that the
matrices A–1 B and BA–1 have the same characteristics roots.
Solution.
A–1 B = A–1 BI = A–1 B (A–1A) = A–1 (BA–1) A.
...(1)
–1
–1
–1
But by Ex. 8, matrices BA and A (BA )A have same characteristic roots or matrices
BA–1 and A–1 B by (1) have same characteristic roots.
Proved.
Example 64. Show that 0 is a characteristic root of a matrix, if and only if, the matrix is
singular.
Solution. Characteristic equation of matrix A is given by
| A – I | = 0
If = 0, then from above it follows that | A | = 0 i.e. Matrix A is singular.
Again if Matrix A is singular i.e., | A | = 0 then
| A – I | = 0 | A | – | I | = 0, 0 – · 1 = 0 = 0.
Proved.
Example 65. Show that characteristic roots of a triangular matrix are just the diagonal
elements of the matrix.
Solution. Let us consider the triangular matrix.
a11 0
a
21 a22
A
a31 a32
a
41 a42
Characteristic equation is |A – I| = 0
a11
a21
a31
a41
or
0
a22
a32
a42
0
0
a33
a43
0
0
0
a44
0
0
a33
a43
0
0
0
0
a44
On expansion it gives (a11 – ) (a22 – ) (a33 – ) (a44 – ) = 0
= a11, a22, a33, a44
which are diagonal elements of matrix A.
Proved.
1
Example 66. If is an eigen value of an orthogonal matrix, then
is also eigen value.
[Hint: AA = I if is the eigen value of A, then 1,
1
]
Example 67. Find the eigen values of the orthogonal matrix.
1 2 2
1
B= 2
1 –2
3
1
2 – 2
Solution. The characteristic equation of
2
2
1
A 2 1 2
2 2 1
is
1
2
2
1 2
2
2
2
0
1
1 1 1 4 2 2 1 4 2 4 2 1 0
(1–) (1 – 2 + 2 – 4) – 2 (2 – 2 + 4) + 2 (– 4 – 2 + 2) = 0
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Determinants and Matrices
329
3 3 2 9 27 0
3 2 3 0
The eigen values of A are 3, 3, –3, so the eigen values of B
Note. If = 1 is an eigen value of B then its reciprocal
1
A are 1, 1, –1.
3
1 1
1 is also an eigen value of B. Ans.
1
EXERCISE 4.20
Show that, for any square matrix A.
1. If be an eigen value of a non singular matrix A, show that
| A|
is an eigen value of the matrix
adj A.
2. There are infinitely many eigen vectors corresponding to a single eigen value.
3 3 3
3. Find the product of the eigen values of the matrix 2 1 1
1 5 6
Ans. 18
3 2 1
4. Find the sum of the eigen values of the matrix 1 3 2
4 1 5
Ans. 11
4
5. Find the eigen value of the inverse of the matrix 1
1
6
3
4
6
2
3
1 0 1
6. Find the eigen values of the square of the matrix 1 2 1
2 2 3
3
3 1 4
7. Find the eigen values of the matrix 0 2 6
0 0 5
Ans. –1, 1,
1
4
Ans. 1, 4, 9
Ans. 8, 27, 125
2 2 1
8. The sum and product of the eigen values of the matrix A 1 3 1 are respectively
1 2 2
(a) 7 and 7
(b) 7 and 5
(c) 7 and 6
(d) 7 and 8
(AMIETE, June 2010) Ans. (b)
4.52 CAYLEY-HAMILTON THEOREM
Satement. Every square matrix satisfies its own characteristic equation.
n
If | A I | 1 n a1 n 1 a2 n 2 an be the characteristic polynomial of n n
matrix A = (aij), then the matrix equation
X n a1 X n 1 a2 X n 2 an I 0 is satisfied by X = A i.e.,
An a1 An1 a2 An 2 an I 0
Proof. Since the elements of the matrix A – I are at most of the first degree in , the
elements of adj. (A – I) are at most degree (n –1) in . Thus, adj. (A – I) may be written as
a matrix polynomial in , given by
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Determinants and Matrices
Adj A I B0 n 1 B1 n 2 Bn 1
where B0 , B1 , , Bn 1 are n n matrices, their elements being polynomial in .
We know that
A I Adj A I | A I | I
A I B0 n1 B1 n – 2 .... Bn 1 1n n a1 n 1 ... an I
Equating coefficient of like power of on both sides, we get
n
IB0 1 I
n
AB0 IB1 1 a1 I
n
AB1 IB2 1 a2 I
.................................
n
ABn 1 1 an I
On multiplying the equation by An , An 1 ,..., I respectively and adding, we obtain
n
0 1 An a1 An 1 ... an I
Thus
An a1 An1 ... an I 0
for example, Let A be square matrix and if
...(1)
3 2 2 3 4 0
be its characteristic equation, then according to Cayley Hamilton Theorem (1) is satisfied
by A.
...(2)
A3 2 A2 3 A 4 I 0
We can find out A1 from (2). On premultiplying (2) by A1 , we get
A2 – 2 A 3I 4 A1 0
1
A1 A2 2 A 3I
4
Example 68. Find the characteristic equation of the symmetric matrix
2 1 1
A 1
2 1
1 1
2
and verify that it is satisfied by A and hence obtain A–1.
Express A6 – 6A5 + 9A4 – 2A3 – 12A2 + 23A – 9I in linear polynomial in A.
(A.M.I.E.T.E., Summer 2000)
Solution.
Characteristic equation is |A – I| = 0
2
1
1
1
2
1
1
1 0
2
(2 – ) [(2 – )2 – 1] + 1 [–2 + + 1] + 1 [1 – 2 + ] = 0
or
(2 – )3 – (2 – ) + – 1 + – 1 = 0
or
(2 – )3 – 2 + + – 1 + – 1 = 0 or (2 – )3 + 3 – 4 = 0
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Determinants and Matrices
331
or
8 – 3 – 12 + 2 + 3– 4 = 0
or
– 3 + 2 – 9+ 4 = 0 or 3 – 2 + 9– 4 = 0
By Cayley-Hamilton Theorem A3 – 6A2 + 9A – 4I = 0
... (1)
Verification:
2 1 1 2 1 1
A 1 2 1 1 2 1
1 1
2 1 1
2
2
2 2 1
2 1 2 6 5
4 1 1
2 2 1 1 4 1 1 2 2 5 6
1 1 4 5 5
2 1 2 1 2 2
6 5
A 5 6
5 5
3
5 2 1
5 1 2
6 1 1
5
5
6
1
1
2
6 5 10 22 21
12 5 5 6 10 5
10 6 5
5 12 5 5 6 10 21
22
=
5 5 12 21 21
10 5 6 5 10 6
21
21
22
A3 – 6A2 + 9A – 4I
22 21
21
22
=
21 21
21
6 5
21 6 5 6
22
5 5
22 36 18 4 21 30 9 0
= 21 30 9 0 22 36 18 4
21 30 9 0 21 30 9 0
5
2 1
5 9 1 2
6
1 1
1
1 0
1 4 0 1
2
0 0
0
0
1
21 30 9 0 0 0 0
21 30 9 0 0 0 0 0
22 36 18 4 0 0 0
So it is verified that the characteristic equation (1) is satisfied by A.
Inverse of Matrix A,
A3 – 6A2 + 9A – 4I = 0
On multiplying by A–1, we get
A2 – 6A + 9I – 4A–1 = 0
6 5
4 A 5 6
5 5
1
or
5
2 1
5 6 1 2
6
1 1
6 12 9 5 6 0
5 6 0 6 12 9
5 6 0
5 6 0
or
4A–1 = A2 – 6A + 9I
1
1 0 0
1 9 0 1 0
2
0 0 1
560
3 1 1
1
5 6 0 , A1 1 3
1
4
6 12 9
3
1 1
A6 – 6A5 + 9A4 – 2A3 – 12A2 + 23A – 9I
= A3 (A3 – 6A2 + 9A – 4I) + 2(A3 – 6A2 + 9A – 4I) + 5A – I
= 5A – I
Ans.
Ans.
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Determinants and Matrices
2 1 1
Example 69. Find the characteristic equation of the matrix A 0 1 0
1 1 2
Verify Cayley Hamilton Theorem and hence prove that :
8 5 5
A8 5 A7 7 A6 3 A5 A4 5 A3 8 A2 2 A I 0 3 0
5 5 8
(Gujarat, II Semester, June 2009)
Solution. Characteristic equation of the matrix A is
2 1
1
0
1 0
1
1
0
2
2 [1 2 ] 1 0 1 0 1 0
3 5 2 7 3 0
According to Cayley-Hamilton Theorem
...(1)
A3 5 A2 7 A 3I 0
We have to verify the equation (1).
2 1 1 2 1 1 5 4 4
A 0 1 0 0 1 0 0 1 0
1 1 2 1 1 2 4 4 5
2
2 1 1 5 4 4 14 13 13
A3 A2 . A 0 1 0 0 1 0 0 1 0
1 1 2 4 4 5 13 13 14
14 13 13 5 4 4
2 1 1 1 0 0
A 5 A 7 A 3I 0 1 0 5 0 1 0 7 0 1 0 3 0 1 0
13 13 14 4 4 5
1 1 2 0 0 1
3
2
14 25 14 3 13 20 7 0 13 20 7 0 0 0 0
0 0 0 0
1 5 7 3
0 0 0 0 0 0 0 0
13 20 7 0 13 20 7 0 14 25 14 3 0 0 0
Hence Cayley Hamilton Theorem is verified.
Now, A8 5 A7 7 A6 3 A5 A4 5 A3 8 A2 2 A I
5
=A
A
3
5 A2 7 A 3I A A3 5 A2 7 A 3 I A2 A I
5
2
2
A O A O A A I A A I
5 4 4
0 1 0
4 4 5
5 2 1
0 0 0
4 1 0
2 1 1 1 0 0
0 1 0 0 1 0
1 1 2 0 0 1
4 1 0
1 1 1
4 1 0
4 1 0
8 5 5
0 0 0 0 3 0
5 5 8
5 2 1
Proved.
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Determinants and Matrices
333
4.53 POWER OF MATRIX (by Cayley Hamilton Theorem)
Any positive integral power Am of matrix A is linearly expressible in terms of those of lower
degree, where m is a positive integer and n is the degree of characteristic equation such that
m n.
Example 70. Find A4 with the help of Cayley Hamilton Theorem, if
1 0 1
A 1 2 1
2 2 3
Solution. Here, we have
1 0 1
A 1 2 1
2 2 3
Characteristic equation of the matrix A is
1 0
1
3 6 2 11 6 0
1
2
1
0
1 2 3 0
2
2
3
Eigen values of A are 1, 2, 3.
Let 4 3 6 2 11 6 Q a 2 b c 0
Put = 1 in (1), (1)4 = a + b + c
4
Put = 2 in (1), (2) = 4a + 2b + c
Put = 3 in (1), (3)4 = 9a + 3b + c
...(1)
a+b+c=1
4a + 2b + c = 16
9a + 3b + c = 81
(where Q () is quotient)
...(2)
... (3)
... (4)
Solving (2), (3) and (4), we get
a = 25, b = –60, c = 36
Replacing by matrix A in (1), we get
A4 A3 6 A2 11A 6 Q A aA2 bA c
2
= O + aA + bA + cI
1 0 1 1 0 1
1 0 –1
1 0 0
25 1 2 1 1 2 1 60 1 2 1 36 0 1 0
2 2 3 2 2 3
2 2 3
0 0 1
0
60 36 0 0
25 50 100 60
125 150 100 60 120 60 0 36 0
250 250 225 120 120 180 0 0 36
50 0 0 100 60 0 49 50 40
25 60 36
125 60 0 150 120 36
100 60 0 65 66 40
250 120 0 250 120 0 225 180 36 130 130
81
(It is also solved by diagonalization method on page 496 Example 38.)
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Determinants and Matrices
EXERCISE 4.21
1. Find the characteristic polynomial of the matrix
3 1 1
1 5 1
A =
1 1 3
Ans. A1
Verify Cayley-Hamilton Theorem for this matrix. Hence find A–1.
7 2 3
1
1 4
1
20
2 2 8
2. Use Cayley-Hamilton Theorem to find the inverse of the matrix
cos sin
sin cos
3. Using Cayley-Hamilton Theorem, find A–1, given that
cos sin
Ans.
sin cos
2 1 3
1 0 2
A =
4 2 1
4 5 2
1
7 10 1
Ans.
5
0
1
2
4. Using Cayley-Hamilton Theorem, find the inverse of the matrix
5
5 1
0 2
0
5
3 15
3 0 1
Ans. 1 0 5 0
10
1 1 1
5. Find the characteristic equation of the matrix
1 3 7
A 4 2 3
1 2 1
(R.G.P.V., Bhopal, Summer 2004)
Ans. 3 – 4 2 – 20 – 35 0
and show that the equation is also satisfied by A.
6. Find the eigenvalues of the matrix
2 3 1
3 1
3
5
2 4
Ans. Eigenvalues are 0, +1, –2
7. Using, Cayley-Hamilton Theorem obtain the inverse of the matrix
1 1 3
1 3 3
(R.G.P.V. Bhopal, I Sem., 2003)
2 4 4
1 2 2
8. Show that the matrix A 1 2 3
0 1 2
satisfies its characteristic equation. Hence find A–1.
24
1
10
8
2
7
1
Ans. 2
9
1
Ans.
8 12
2 6
2 2
2 10
2 1
1
4
9. Use Cayley Hamilton Theorem to find the inverse of
1 2 4
A 1 0 3
3 1 2
8
6
3
1
Ans. A
7 14 7
7
1
5
2
1
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335
10. Verify Cayley-Hamilton Theorem for the matrix
5 1
2
1 1 2
1
1 3 5
A= 3 1 1
Hence evaluate A–1.
Ans. 11
7 1 2
2 3 1
1 4
5
4
3
11. If A
1A2 – A –10I in terms of A.
, then express A – 4A – 7A + 11
2 3
(A.M.I.E.T.E., Winter 2001)
12. If 1, 2 and 3 are the eigenvalues of the matrix
Ans. A + 5 I
5
2 9
5 10 7
then 1 + 2 + 3 is equal to
9 21 14
(i) –16
(ii) 2
(iii) –6
(iv) –14
Ans. (ii)
1 0
–1
13. The matrix A
is given. The eivenvalues of 4A + 3A + 2l are
2 4
(A) 6, 15;
(B) 9, 12
(C) 9, 15;
(D) 7, 15
Ans. (C)
14. A(3 × 3) real matrix has an eigenvalue i, then its other two eigenvalues can be
(A) 0, 1
(B) –1, i
(C) 2i, –2i
(D) 0, –i (A.M.I.E.T.E, Dec. 2004)
15. Verify Cayley-Hamilton theorem for the matrix
1 2 3
2 4 2
A =
1 1 2
16. Find adj. A by using Cayley-Hamilton thmeorem where A is given by
1 2 1
0
0
1 1
A =
(R.G.P.V., Bhopal, April 2010) Ans. 3
3 1 1
3
3 3
2 1
7
1
1 0 0
1 0 0
32
17. If a matrix A 0 1 0 , find the matrix A , using Cayley Hamilton Theorem. Ans. 0 1 0
32 0 1
1 0 1
4.54 CHARACTERISTIC VECTORS OR EIGEN VECTORS
As we have discussed in Art 21.2,
A column vector X is transformed into column vector Y by means of a square matrix A.
Now we want to multiply the column vector X by a scalar quantity so that we can find the
same transformed column vector Y.
i.e.,
AX = X
X is known as eigen vector.
Example 71. Show that the vector (1, 1, 2) is an eigen vector of the matrix
3 1 1
A 2 2 1 corresponding to the eigen value 2.
2 2 0
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Determinants and Matrices
Solution. Let X = (1, 1, 2).
Now,
3 1 1 1 3 1 2 2
1
AX 2 2 1 1 2 2 2 2 2 1 2 X
2 2 0 2 2 2 0 4
2
Corresponding to each characteristic root , we have a corresponding non-zero vector X
which satisfies the equation [ A I ] X 0. The non-zero vector X is called characteristic
vector or Eigen vector.
4.55 PROPERTIES OF EIGEN VECTORS
1. The eigen vector X of a matrix A is not unique.
2. If 1 , 2 , .... , n be distinct eigen values of an n × n matrix then corresponding eigen
vectors X1, X2, ......., Xn form a linearly independent set.
3. If two or more eigen values are equal it may or may not be possible to get linearly
independent eigen vectors corresponding to the equal roots.
4. Two eigen vectors X1 and X2 are called orthogonal vectors if X 1 X 2 0.
5. Eigen vectors of a symmetric matrix corresponding to different eigen values are orthogonal.
a
Normalised form of vectors. To find normalised form of b , we divide each element by
c
a 2 b2 c 2 .
1 1/ 3
For example, normalised form of 2 is 2 / 3
2 2 / 3
12 22 2 2 3
4.56 NON-SYMMETRIC MATRICES WITH NON-REPEATED EIGEN VALUES
3 1 4
Example 72. Find the eigen values and eigen vectors of matrix A 0 2 6
0 0 5
3 1
4
Solution. | A I | 0
0
2
6
(3 ) (2 ) (5 )
0
5
Hence the characteristic equation of matrix A is given by
| A I | 0
(3 ) (2 ) (5 ) 0
2, 3, 5.
Thus the eigen values of matrix A are 2, 3, 5.
The eigen vectors of the matrix A corresponding to the eigen value is given by the nonzero solution of the equation ( A I ) X 0
or
4 x1 0
3 1
0
x 0
2 6
2
0
0
5 x3 0
... (1)
When 2, the corresponding eigen vector is given by
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3 2
0
0
337
x1 0
2 2 6 x2 0
0
5 2 x3 0
1
4
1 1 4 x1 0
0 0 6 x 0
2
0 0 3 x3 0
x1 x2 4 x3 0
0 x1 0 x2 6 x3 0
x
x1
x
2 3 k
60 06 00
k
Hence X1 k k
0
x1 x2 x3
k
1 1 0
x1 k , x2 k , x3 0
1
1 can be taken as an eigen vector of A corresponding to the eigen
0 value 2
When 3, substituting in (1), the corresponding eigen vector is given by
4 x1 0
33 1
0
2 3 6 x2 0
0
0
5 3 x3 0
0x1 + x2 + 4x3 = 0
0 1 4 x1 0
0 1 6 x 0
2
0 0 2 x3 0
0x1 – x2 + 6x3 = 0
x
x1
x
2 3
6 4 00 00
x1 = k, x2 = 0, x3 = 0
x1 x2 x3
k
10 0
0 10
k
1
Hence, X 2 0 k 0 can be taken as an eigen vector of A corresponding to the
0
0
eigen value = 3.
When 5.
Again, when 5, substituting in (1), the corresponding eigen vector is given by
4 x1 0
1 4 x1
3–5 1
–2
0
0
2 – 5 6 x2 0
0 –3 6 x2 = 0
0
5 – 5 x3 0
0
0 0 0 x3
0
–2 x1 x2 4 x3 0
–3x2 6 x3 0
By cross-multiplication method, we have
x
x1
x
2 3
6 12 0 12 6 – 0
x1 = 3k, x2 = 2k, x3 = k
x1 x2 x3
18 12 6
x1 x2 x3
=k
3
2
1
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Determinants and Matrices
3k
3
Hence, X 3 2k k 2 can be taken as an eigen vector of A corresponding to the eigen
k
1
value 5.
Ans.
EXERCISE 4.22
Non-symmetric matrix with different eigen values:
Find the eigen values and the corresponding eigen vectors for the following matrices:
1 1 2
1. 1 2 1
0 1 1
(A.M.I.E.T., June 2006)
2 2 3
1 1
2. 1
1 3 1
4 2 2
2 1 0
2. 5 3 2 Ans. 1, 2, 5; 1 , 1 , 1
2 4
1
4 2 0
1
Ans. 1, 1, 2, 0 ,
1
3
2 ,
1
1
3
1
11 1 1
Ans. 2, 1, 3; 1 , 1 , 1
14 1 1
9 2 6
4 6 6
6 0 3
2 1 2
1 3 2
5
0
3
3.
Ans. 1, 1, 4; 2 , 1 , 1
Ans. 1, 1, 2; 1 , 1 , 1 4.
1 4 3
16 4 11
7 1 1
3 2 4
2 1 1
1 1 1
0 1 4
0 1 2
11 4 5
1 2 1
0,1,5; 1 ,
0 , 5
Ans. –1, 1, 2; 1 , 2 , 3
4.
Ans.
5.
1 1 0
3 2 3
1 1 11
1 1 1
T
8. Show that the matrices A and A have the same eigenvalues. Further if l, m are two distinct
eigenvalues, then show that the eigenvector corresponding to l for A is orthogonal to eigenvector corresponding to m for AT.
4.57 NON-SYMMETRIC MATRIX WITH REPEATED EIGEN VALUES
Example 73. Find all the Eigen values and Eigen vectors of the matrix
2 2 3
A 2
1 6
1 2 0
Solution. Characteristic equation of A is
2
2
3
2
1
1
6
2
0
(AMIETE, Dec. 2009)
0
2
(2 ) [ 12] 2(2 6) 3 (4 1 ) 0
3 2 – 21 – 45 0
By trial: If 3, then 27 9 63 45 0, so ( 3) is one factor of (1).
The remaining factors are obtained on dividing (1) by 3.
–3
1
1
–21
–45
–3
6
45
1
–2
–15
0
.... (1)
2 2 15 0
( 5) ( 3) 0
( 3) ( 3) ( 5) 0 5, 3, 3
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339
To find the eigen vectors for corresponding eigen values, we will consider the matrix equation
(A I)X 0
i.e.,
2
2
1
2
1
2
3
6
x 0
y 0
0 z 0
... (2)
7 2 3 x 0
On putting 5 in eq. (2), it becomes 2 4 6 y 0
1 2 5 z 0
We have
– 7x + 2y – 3z = 0,
2x – 4y – 6z = 0
x
y
z
or
12 12 6 42 28 4
x = k, y = 2k, z = – k
x
y
z
24 48 24
or
x y
z
k
1 2 1
k
1
Hence, the eigen vector X 1 2k = k 2
k
1
1 2 3 x 0
Put 3 in eq. (2), it becomes 2 4 6 y 0
1 2
3 z 0
We have x + 2y – 3z = 0,
2x + 4y – 6z = 0,
– x – 2y + 3z = 0
Here first, second and third equations are the same.
1
Let x = k1, y = k2 then z (k1 2k 2 )
3
k1
Hence, the eigen vector is
k2
1
(k1 2k 2 )
3
0
Let k1 0, k 2 3, Hence X 2 3
2
Since the matrix is non-symmetric, the corresponding eigen vectors X2 and X3 must be
linearly independent. This can be done by choosing
3
k1 = 3, k2 = 0, and Hence X 3 0
1
1
Hence, X 1 2 ,
1
0
X 2 3 ,
2
3
X 3 0 .
1
Ans.
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340
Determinants and Matrices
EXERCISE 4.23
Non-symmetric matrices with repeated eigen values
Find the eigen values and eigen vectors of the following matrices:
4 0
2 2 2
1. 1
1 1 Ans. –2, 2, 2; 1 , 1
7 1
1 3 1
2 1 1
3. 2 3 2
3 3 4
1 1 0
0 1 0
5.
0 0 1
2 2 1
2. 1 3 1
1 2 2
1 1
2 , 1
Ans. 1, 1, 5;
5 1
0 1 1
9 4 4
1 , 1 , 1
8 3 4
1,
1,
3;
4.
Ans.
16 8 7
1 1 2
0 1 1
1 , 0 , 2
1,
1,
7;
Ans.
1 1 3
1
Ans. 1, 1, 1, 0
1
(AMIETE, Dec. 2010)
4.58 SYMMETRIC MATRICES WITH NON REPEATED EIGEN VALUES
Example 74. Find the eigen values and the corresponding eigen vectors of the matrix
2 5 4
5 7 5
4 5 2
Solution.
| A I | 0
2
5
5
7
4
By trial:
Take
5
4
5
3 32 90 216 0
0
2
3, then – 27 – 27 + 270 – 216 = 0
By synthetic division
–3
1
1
–3
–90
–216
–3
18
216
–6
–72
0
2 6 72 0 ( 12) ( 6) 0 3, 6, 12
Matrix equation for eigen vectors [ A I ] X 0
5
4 x 0
2
5
7
5 y 0
4
5
2 z 0
Eigen Vector
On putting 3 in (1), it will become
1 5 4 x 0
5 10 5 y 0
4 5 1 z 0
...(1)
x 5 y 4z 0
5 x 10 y 5z 0
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341
x
y
z
25 40 20 5 10 25
1
Eigen vector X 1 1 .
1
or
x y z
1 1 1
Eigen vector corresponding to eigen value – 6.
Equation (1) becomes
4 5 4 x 0
5 13 5 y 0
4 5 4 z 0
or
x
y
z
25 52 20 20 52 25
4x 5 y 4 z 0
5 x 13 y 5z 0
or
x y
z
1 0 1
1
eigen vector X 2 0
1
Eigen vector corresponding to eigen value = 12.
Equation (1) becomes
4 x 0
14 5
5 5
5 y 0
4 5 14 z 0
or
14 x 5 y 4 z 0
5x 5 y 5z 0
x
y
z
25 20 20 70 70 25
or
x y z
1 2 1
1
Eigen vector X 3 2
1
Ans.
EXERCISE 4.24
Symmetric matrices with non-repeated eigen values
Find the eigen values and eigen vectors of the following matrices:
5 0 1
0 2 0
1.
1 0 5
8 6 2
3. 6
7 4
2 4
3
0 1 1
Ans. 2, 4, 6; 1 , 0 , 0
0 1 1
(U.P., I Semester, Jan 20111)
1 2 1
2 4 6
4. 4
2 6 Ans. –2, 9, –18; 1 , 2 , 1
0 1 4
6 6 15
1 1 1
3 1 1
2. 1 5 1 Ans. 2, 3, 6; 0 , 1 , 2
1 1 1
1 1 3
1 2 2
Ans. 0, 3, 15; 2 , 1 , 2
2 2 1
1 1 1
1 1 3
5. 1 5 1 Ans. 2, 3, 6; 0 , 1 , 2
1 1 1
3 1 1
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Determinants and Matrices
4.59 SYMMETRIC MATRICES WITH REPEATED EIGEN VALUES
Example 75. Find all the eigen values and eigen vectors of the matrix
2 1 1
1 2 1
1 1 2
Solution. The characteristic equation is
2
1
1
1
2
1
1
1
2
0
(2 )[(2 )2 1] 1[2 1] 1[1 2 ] 0
(2 ) (4 4 2 1) ( 1) 1 0
8 8 2 2 2 4 4 2 3 2 2 0
3 6 2 9 4 0
... (1)
3 62 9 4 0
On putting 1 in (1), the equation (1) is satisfied. So 1 is one factor of the equation (1).
The other factor ( 2 5 4) is got on dividing (1) by 1.
( 1) ( 2 5 4) 0 or ( 1) ( 1) ( 4) 0
The eigen values are 1, 1, 4.
1
1
24
When 4 1
24
1
1
1
2 4
2 x1 x2 x3 0
= 1, 1, 4
2 1 1 x1 0
x1 0
1 2 1 x2 0
x2 0
x 0
1 1 2 x3 0
3
x1 x2 2 x3 0
x1
x
x
2 3
2 1 1 4 2 1
x1 k ,
x2 k ,
x3 k
k
X 1 k k
k
1
1
1
1
X 1 1
1
or
2 1
1
1
When 1
x1 x2 x3
k
1 1 1
1 1 1 x1
1 1 1 x2 0
1 1 1 x
3
1
2 1
1
1
1
2 1
x1
x2 0
x
3
1 1 1 x1
0 0 0 x2 0, R2 R2 R1
0 0 0 x
R3 R3 R1
3
x1 x2 x3 0
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343
Let x1 = k1 and x2 = k2
k1 – k2 + x3 = 0
or
k1
X 2 k2
k2 k1
x3 = k2 – k1
1
X 2 1
0
k1 1
k 1
2
l
Let X 3 m
n
As X3 is orthogonal to X1 since the given matrix is symmetric
l
[1, 1, 1] m 0
n
or
l–m+n=0
... (2)
As X3 is orthogonal to X2 since the given matrix is symmetric
l
[1, 1, 0] m 0
n
Solving (2) and (3), we get
or
l+m+0=0
l
m
n
0 1
1 0
11
... (3)
l
m n
1 1 2
1
X 3 1
2
Ans.
EXERCISE 4.25
Symmetric matrices with repeated eigen values
Find the eigen values and the corresponding eigen vectors of the following matrices:
1.
1 2 3
2 3 1
2 4 6
1 , 6 , 2
Ans.
0,
0,
14;
3 6 9
0 5 3
3.
6 2 2
2
3 1
2 1 3
4.
2 0 1
2. 0 3 0
1 0 2
2 1 1
Ans. 8, 2, 2; 1 , 0 , 2
1 2 0
1 1 1
Ans. 1, 3, 3; 0 , 1 , 2
1 1 1
6 3 3
4. 3 6 3
3 3 6
Ans. 3, 3, 12
Choose the correct or the best of the answers given in the following Parts;
(i) Two of the eigenvalues of a 3 × 3 matrix, whose determinant equals, 4, are –1 and +2 the
third eigen value of the matrix is equal to
(a) –2
(b) –1
(c) 1
(d) 2
(ii) If a square matrix A has an eigenvalue , then an eigenvalue of the matrix (kA)T where, k
0,is a scalar is
(a) k
(b) k /
(c) k
(d) None of these
(iii) An eigenvalue of a square matrix A is Then
(a) | A |
(b) A is symmetric
(c) A is singular;
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Determinants and Matrices
(d) A is skew-symmetric; (e) A is an even order matrix;
1
(iv) The matrix A is defined as A = 2
1
(a) –1, –9, –4,
(b)1, 9, 4
1
(v) If the matrix is A= 0
0
0
3
4
0
0 .The eigenvalues of A2 are
2
(c) –1, –3, 2,
2
3
0
(f) A is an odd order matrix.
(d) 1, 3, –2.
3
5 then the eigenvalues of A3 + 5A + 8 I, are
2
(a) –1, 27, –8; (b) –1, 3, –2;
(c) 2, 50, –10, (d) 2, 50, 10.
(vi) The matrix A has eigen values i 0 .Then A–1 – 2I + A has eigenvalues
1
(a) 1 + 2 i +i 2 (b) 2 i
2 1
i i2
i
(viii) The eigen values of a matrix A are 1,–2, 3. The eigen of 3I–2A + A2 are
(a) 2, 11, 6
(b) 3, 11, 18
(c) 2, 3, 6
(d) 6, 3, 11
(c) 1–2i +i 2
Ans. (i)(b), (ii)(c), (iii)(c), (iv)(b), (v)(c),
(d) 1
(vi)(b), (vii)(a)
4.60 DIAGONALISATION OF A MATRIX
Diagonalisation of a matrix A is the process of reduction of A to a diagonal form ‘D’. If A is
related to D by a similarity transformation such that D = P–1 AP then A is reduced to the diagonal
matrix D through modal matrix P. D is also called spectral matrix of A.
4.61 THEOREM ON DIAGONALIZATION OF A MATRIX
Theorem. If a square matrix A of order n has n linearly independent eigen vectors, then a matrix
P can be found such that P–1 AP is a diagonal matrix.
Proof.
We shall prove the theorem for a matrix of order 3. The proof can be easily extended to
matrices of higher order.
a1 b1 c1
A a2 b2 c2
Let
a3 b3 c3
and let 1 , 2 , 3 be its eigen values and X1, X2, X3 the corresponding eigen vectors, where
x1
x2
X 1 y1 ,
X 2 y2 ,
z1
z2
For the eigen value 1 , the eigen vector is given by
We have
(a1 1 ) x1 b1 y1 c1 z1 0
a2 x1 (b2 1 ) y1 c2 z1 0
a3 x1 b3 y1 (c3 1 ) z1 0
a1 x1 b1 y1 c1 z1 1 x1
a2 x1 b2 y1 c2 z1 1 y1
a3 x1 b3 y1 c3 z1 1 z1
x3
X 3 y3
z3
...(1)
...(2)
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Similarly for 2 and 3 we have
a1 x2 b1 y2 c1 z2 2 x2
a2 x2 b2 y2 c2 z2 2 y2
a3 x2 b3 y2 c3 z2 2 z2
a1 x3 b1 y3 c1 z3 λ3 x3
a
and
2 x3 b2 y3 c2 z3 λ 3 y3
a3 x3 b3 y3 c3 z3 λ 3 z3
x1 x2 x3
y y y
2
3
We consider the matrix
P= 1
z1 z 2 z3
Whose columns are the eigenvectors of A.
a1 b1 c1 x1
a b c y
Then
A P = 2 2 2 1
a3 b3 c3 z1
...(3)
...(4)
x2
y2
z2
x3
y3
z3
a1 x1 b1 y1 c1 z1 a1 x2 b1 y2 c1 z2 a1 x3 b1 y3 c1 z3
a2 x1 b2 y1 c2 z1 a2 x2 b2 y2 c2 z2 a2 x3 b2 y3 c2 z3
a x b y c z a x b y c z
a3 x3 b3 y3 c3 z3
3 2
3 2
3 2
3 1 3 1 3 1
1 x1 2 x2 3 x3
1 y1 2 y2 3 y3
[Using results (2), (3) and (4)]
z z z
2 2
3 3
11
x1 x2 x3 1 0 0
y1 y 2 y3 0 2 0 PD
z z
z3 0 0 3
2
1
1 0 0
where D is the Diagonal matrix 0 2 0
0 0
3
AP = PD
P–1 AP = P–1 PD = D
Notes 1. The square matrix P, which diagonalises A, is found by grouping the eigen vectors of A
into square-matrix and the resulting diagonal matrix has the eigen values of A as its
diagonal elements.
2. The transformation of a matrix A to P–1 AP is known as a similarity transformation.
3. The reduction of A to a diagonal matrix is, obviously, a particular case of similarity
transformation.
4. The matrix P which diagonalises A is called the modal matrix of A and the resulting
diagonal matrix D is known as the spectra matrix of A.
6 2 2
Example 76. Let A 2 3 1 Find matrix P such that P–1 AP is diagonal matrix.
2 1 3
Solution. The characteristic equation of the matrix A is
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Determinants and Matrices
6
2
2
3
2
1
2
1 0
3
2
(6 )[9 6 1] 2 [6 2 2] 2 [2 6 2] 0
(6 )( 2 6 8) 8 4 8 4 0
6 2 36 48 3 62 8 16 8 0
3 12 2 36 32 0
( 2)2 ( 8) 0
Eigen vector for = 2
3 12 2 36 32 0
= 2, 2, 8
2 1 1 x1 0
4 2 2 x1 0
2 1 1 x 0 or 2 1 1 x 0 R2 R1 R2
2 R R R
2
2
3
2 1 1 x3 0 3
2 1 1 x3 0
2 1 1 x1 0
0 0 0 x 0 or 2x – x + x = 0
1
2
3
2
0 0 0 x3 0
This equation is satisfied by x1 = 0, x2 = 1, x3 = 1
0
X 1 1
1
and again
x1 = 1, x2 = 3, x3 = 1.
1
X 2 3
1
Eigen vector for = 8
2 2 2 x1 0
2 5 1 x 0
2
2 1 5 x3 0
2 x1 2 x2 2 x3 0
2 x1 5x2 x3 0
x1
x2
x
x
x
x
x
3 x1 x2 3 1 2 3
2 10 4 2 10 4
2 1 1
12 6 6
2
X 3 1
1
0 1 2
P 1 3 1 ,
1 1 1
P
1
1 7
4
1
2 2 2
6
2
1 1
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347
1 7 6 2 2 0 1 2 2 0 0
4
1
Ans.
P AP 2 2 2 2
3 1 1 3 1 0 2 0
6
2
1 1 2 1 3 1 1 1 0 0 8
a h
–1
Example 77. The matrix A
T, where
is transformed to the diagonal form D = T AT
h
b
Now
1
cos sin
T
Find the value of which gives this diagonal transformation.
sin cos
cos sin
cos sin
1
T
Solution. T
sin cos
sin cos
cos sin a h cos sin
Now T 1 AT
sin cos h b sin cos
a cos h sin h cos b sin cos sin
a sin h cos h sin b cos sin cos
a cos 2 2h sin cos b sin 2
(a b)sin cos h sin 2 h cos 2
2
2
a sin 2 2h sin cos b cos2
(a b)sin cos h cos h sin
a cos 2 h sin 2 b sin 2 (a b) sin cos h cos 2
(a b)sin cos h cos 2 a sin 2 h sin 2 b cos 2
0
d
1
being diagonal matrix
0 d2
(a b)sin cos h cos 2 0
a b
a b
sin 2 h cos 2
sin 2 h cos 2 0
2
2
2h
1
2h
tan 2
tan 1
ba
2
ba
Ans.
EXERCISE 4.26
1. Find the
8
A 4
3
matrix B which transforms the matrix
8 2
3 2 to a diagonal matrix.
4
1
4 3 2
Ans. B 3 2 1
2 1 1
4 1 0
2. For the matrix A 1 4 1 , determine a matrix P such that P–1AP is diagonal matrix.
0 1 4
1
1
1
2
Ans. P 0 2
1
1
1
5 7 5
3. Determine the eigen values and the corresponding eigen vectors of the matrix A 0 4 1
2 8 3
2 1 1
Hence find the matrix P such that P–1AP is diagonal matrix.
Ans. P 1 1 1
3 2
1
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Determinants and Matrices
4. Reduce the following matrix A into a diagonal matrix
8 6 2
A 6 7 4
2 4
3
0 0 0
Ans. 0 3 0
0 0 15
5. Prove that similar matrices have the same eigenvalues. Also give the relationship between
the eigenvectors of two similar matrices.
(A.M.I.E.T.E, June 2005)
6. Let a 4 × 4 matrix A have eigenvalues 1, –1, 2, –2 and matrix B = 2A + A–1 – I Find
(i) determinant of matrix B.
(ii) trace of matrix B.
(A.M.I.E.T.E, June 2005)
4.62 POWERS OF A MATRIX (By diagonalisation)
We can obtain powers of a matrix by using diagonalisation.
We know that
D = P–1 AP
Where A is the square matrix and P is a non-singular matrix.
D2 = (P–1 AP) (P–1 AP) = P–1 A (P P–1) AP = P–1 A2 P
Similarly
D3 = P–1 A3 P
In general Dn = P–1 An P
Pre-multiply (1) by P and post-multiply by P–1
P Dn P–1 = P (P–1 An P) P–1
= (P P–1) An (P P–1)
= An
Procedure:(1) Find eigen values for a square matrix A.
(2) Find eigen vectors to get the modal matrix P.
(3) Find the diagonal matrix D, by the formula D = P–1 AP
(4) Obtain An by the formula An = P Dn P–1.
...(1)
1 0 1
Example 78. Find a matrix P which transform the matrix A 1 2 1 to diagonal
2 2 3
form. Hence A4.
Solution. Characteristic equation of the matrix A is
1 0
1
or 3 62 11 6 0
1
2 1
0
or ( 1) ( 2) ( 3) 0
2
2
3
1, 2, 3
For 1, eigen vector is given by
1 x1 0
1 1 0
0 0 1 x1 0
1
1 1 1 x 0
2 1 1
x2 0
2
2
2 2 2 x3 0
2
3 1 x3 0
0 x1 0 x2 x 3 0
x
x1
x2
3 or x1 = 1, x2 = –1, x3 = 0
x1 x2 x3 0
0 1 1 0 0
Eigen vector is [1, –1, 0].
For = 2, eigen vector is given by
1 2
1
2
0
22
2
1
x1 0
x 0
1
2
3 2 x3 0
1
1
2
0 1 x1 0
0 1 x2 0
2 1 x3 0
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349
0 0 0 x1 0
1 0 1 x 0 R R + R
1
2
2 1
2 2 1 x3 0
x1 0 x2 x3 0
2 x1 2 x2 x3 0
x
x1
x
2 3
0 2 2 1 2 0
Eigen vector is [–2, 1, 2].
For = 3, eigen vector is given by
1
1 3 0
1
23 1
2
2
33
x1 = – 2,
x1 0
x 0
2
x3 0
x2 = 1,
x3 = 2
2 0 1 x1 0
1 1 1 x 0
2
2 2 0 x3 0
2 x1 0 x2 x3 0
x1 x2 x3 0
x
x1
x2
3
0 1 1 2 2 0
Eigen vector is [–1, 1, 2].
x1 = – 1,
0
1 2 1
1
1
Modal matrix P 1 1 1 and P 2
2
2
0 2 2
1
0 1 2 1 0 1 1 2
Now P 1 AP 1 1 0 1 2 1 1 1
1 2 2 3 0 2
1 1
2
A4 PD 4 P 1
x2 = 1,
x3 = 2
2 1
2 0
2 1
1 1 0 0
1 0 2 0 D
2 0 0 3
0 1
1 2 1 1 0 0
1 1 1 0 16 0 1 1
0
2 2 0 0 81
1
1
EXERCISE 4.27
1
2 49 50 40
0 65
66 40
1 130 130
81
2
Ans.
Find a matrix P which transforms the following matrices to diagonal form. Hence calculate the power
matrix.
1 1 3
1. If A = 1 5 1 , calculate A4.
3 1 1
2. If
3 1 1
A 1 5 1 , calculate A4.
1 1 3
2 1 1
3. If A 1 2 1 , calculate A6.
1 1 2
251 405 235
Ans. 405 891 405
235 405 251
251 405 235
Ans. 405 891 405
235 405 251
1366 1365 1365
Ans. 1365 1366 1365
1365 1365 1366
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Determinants and Matrices
1 1 1
0 2 1 ,
8
A
4. If
calculate A .
4 4 3
12099 12355 6305
Ans. 12100 12356 6305
13120 13120 6561
3 1 1
5. Show that the matrix A is diagonalisable A 2 1 2 . If so obtain the matrix P such that
0 1 2
–1
P AP is a diagonal matrix.
(AMIETE, June 2010)
4.63 SYLVESTER THEOREM
Let
and
P(A) = C0 An + C1 An–1 + C2 An–2 + … + Cn–1 A + Cn I
| I A | f () and Adjoint matrix of [ I A] [ f ()]
z ( )
[ f ()] Adjoint matrix of [ I A]
f ()
f ()
Then according to Sylvester’s theorem
P ( A) P (1 ). Z (1 ) P ( 2 ). Z ( 2 ) P(3 ). Z (3 )
n
P ( ). Z ( )
r
r
r 1
2 0
100
Example 79. If A
, find A .
0 1
Solution. f ( ) | I A |
0
0
2 0
0 1
2
0
0
1
0
f () ( 2) ( 1) 0 or 1 1, 2 2
f ( ) 2 3 2, f () 2 3
f (2) = 4 – 3 = 1, f (1) = 2 – 3 = – 1
0
1
[ f ()] = Adjoint matrix of the matrix [ I A]
0
2
Z ( 1 ) Z (1)
[ f (1)] 1 0 0 0 0
f (1) 1 0 1 0 1
Z ( 2 ) Z (2)
[ f (2)] 1 1 0
f (2) 1 0 0
By Sylvester theorem P ( A) P(1 ). Z (1 ) P( 2 ). Z ( 2 )
A100 P( 1 ) Z (1 ) P( 2 ) Z ( 2 )
0 0 100 1 0 100 0 0 100 1 0
100
1
2 0 0 1 0 1 2 0 0
0 1
0 0 2100
0 1 0
0 2100
0 0
0
1
Ans.
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EXERCISE 4.28
1 4
1. Verify Sylvesters theorem for A3, where A
3 2
Use sylvesters theorem in solving the following:
1 0
256
2. Given A
, find A .
0 3
e 1
1 0
0
A
,
3. Given A
show
that
e
.
0
2
0 e 2
1 3
4. Given A
, show that 2 sin A = | sin 2 | A.
1 1
1 4
5. Prove that 3 tan A = A tan (3) where A
2 1
1 2
6. Prove that sin2A + cos2 A = 1, where A
1 4
1 2 3
7. Given A 0 2 0 , find A–1.
0 0 3
0
1
Ans.
256
0 3
1 1 1
1
0
Ans. 0
2
1
0
0
3
1 20 0
1 , find tan A.
8. Given A 1 7
3 0 2
18 60 20
20 80 20
20 100 20
tan 3
Ans. tan1 0 0 0 tan 2 1 4
1
2 10 2
1
2
2
18 60 20
15 60 15
12 60 12
4.64 QUADRATIC FORMS
The quadratic forms are defined as a homogeneous polynomial of second degree in any
number of variables.
For example
1. Two variables ax2 + 2hxy + by2 = Q (x, y)
2. Three variables ax2 + 2hxy + by2 + cz2 + 2hxy + 2gyz + 2fzx = Q (x, y, z)
3. Four variables
ax2 + by2 + cz2 + dw2 + 2hxy + 2gyz + 2fzx + 2lxw + 2myw + 2nzw = Q (x, y, z, w)
4. n variables = Q (x1, x2, ........xn.)
4.65 QUADRATIC FORM EXPRESSED IN MATRICES
Quadratic form can be expressed as a product of matrices.
Quadratic form = Q (x) = X AX
x1
a11 a12 a13
a
X
x
A
where
2 and
21 a22 a23
x3
a31 a32 a33
X is the transpose of X.
a11 a12 a13 x1
X AX x1 x2 x3 a21 a22 a23 x2
a31 a32 a33 x3
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Determinants and Matrices
x1
a11 x1 a21 x2 a31 x3 a12 x1 a22 x2 a32 x3 a13 x1 a23 x2 a33 x3 x2
x3
2
2
a11 x1 a21 x1 x2 a31 x1x3 a12 x1 x2 a22 x2 a32 x2 x3 a13 x1 x3 a23 x2 x3 a33 x32
a12
a11 x12 a22 x22 a33 x32 (a12 a21 ) x1 x2 (a23 a32 ) x2 x3 (a31 a13 ) x1 x3
and a21 are the coefficients of x1x2 it means (a12 + a21) are the coefficient of x1x2. In
general aij and aji are the both coefficients of xi,xj (i j ).
So (a ij + a ji ) are the coefficient of xi xj
Let us have new coefficients of xi xj.
cij = cji =
1
(a + aji)
2 ij
1
( A A) = symmetric matrix C
2
Thus, the coefficient matrix in quadratic form is always symmetric matrix without loss of
generality.
We know
Then X AX c11 x12 c22 x22 c33 x2 2c12 x1 x2 2c23 x2 x3 2c31 x1 x3
Matrix A is known as the coefficient matrix or matrix of quadratic form and (R) is the
discriminant of the quadratic form.
Example 80. Write down the quadratic form corresponding to the matrix
1 2 5
A 2 0 3
5 3 4
Solution. Quadratic form = X AX
1 2 5 x1
x1
[ x1 x2 x3 ] 2 0 3 x2 [ x1 2 x2 5 x3 , 2 x1 3 x3 , 5 x1 3 x2 4 x3 ] x2
=
5 3 4 x3
x3
2
2
x1 2 x1 x2 5 x3 x1 2x1x2 3x2 x3 5 x1 x3 3x2 x3 4 x3
x12 4 x32 4 x1 x2 10 x1 x3 6 x2 x3
Ans.
Example 81. Find a real symmetric matrix C of the quadratic form:
Q ( x1, x2 , x3 ) x12 4 x22 6 x32 2 x1 x2 x2 x3 3x1x3
Solution. On Comparing the coefficients in the given quadratic form, with the standard
quadratic form, we get
Here
a11 1, a22 4, a 33 6, a12 2, a21 0, a23 1, a32 0, a13 3, a31 0
Q ( x1 , x2 , x3 ) [ x1
x2
1 2 3 x1
x3 ] 0 4 1 x 2 x1
0 0 6 x
3
2x1 4 x2
x1
3x1 x2 6 x3 x 2
x
3
x12 2 x1 x2 4 x22 3x1 x3 x2 x3 6 x32
1 2 3 1 0 0
2 2 3
1
1
1
C ( A A) 0 4 1 2 4 0 2 8 1
2
2
2
0 0 6 3 1 6
3 1 12
1
1
3
2
1
4
1
2
3
2
1
2
6
Ans.
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4.66 LINEAR TRANSFORMATION OF QUADRATIC FORM
(Diagonalisation of the matrix)
Let the given quadratic form be XAX where A is a symmetric matrix.
Consider the linear transformation X = PY
Then
X = (PY) = Y P
XAX = (Y P) A (PY) = Y (P AP) Y = Y BY
where
B = PAP
(Transformed quadratic form)
Now
B = (PAP) = PAP = B
Rank (B) = Rank (A)
Therefore, A and B are congruent matrices and the transformation X = PY is known as
congruent transformation.
4.67 CANONICAL FORM OF SUM OF THE SQUARES FORM USING LINEAR
TRANSFORMATION
When a quadratic form is linearly transformed then the transformed quadratic of new variable
is called canonical form of the given quadratic form.
When XAX is linearly transformed then the transformed quadratic YBY is called the canonical
form of the given quadratic X AX.
n
If B = PAP = Diag (1 , 2 , 3 ....... n ) than XAX = YBY =
Y
i i
2
i 1
Remarks. (1) i (eigen values) can be positive or negative or zero.
(2) If Rank (A) = r, then the quadratic form XAX will contain only r terms.
4.68 CANONICAL FORM OF SUM OF THE SQUARES FORM USING
ORTHOGONAL TRANSFORMATION
Real symmetric matrix A can be reduced to a diagonal form MAM = D
...(1)
where M is the normalised orthogonal modal matrix of A and D is its spectoral matrix.
Let the orthogonal transformation be
X = MY
Q = XAX = (MY) A (MY) = (YM) A (MY) = Y (MAM) Y
[ MAM = D]
= YDY
=Y Diag. (1 2 n ) Y
1 0.....0 y1
0 .....0 y
2
2
y1 y2 ..... yn
0 0 ..... n yn
1 y1
y1
y
2
2 y2 ..... n yn
yn
1 y12 2 y22 ..... n yn2 , which is called canonical form.
Now, we have seen that quadratic form XAX can be reduced to the sum of the squares by the
transformation X = PI where P is the normalised modal matrix of A.
Canonical form. B is a diagonal matrix, then the transformed quadratic is a sum of square
terms, known as canonical form.
Index. The number of positive terms in canonical form of a quadratic form is known as index
(s) of the form.
Rank of form. Rank (r) of matrix B (or A) is called the rank of the form.
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Determinants and Matrices
Signature of quadratic form. The difference of positive terms (s) and negative terms (r–s)
is known as the signature of quadratic form.
Signature = s – (r – s) = s – r + s = 2s – r
4.69 CLASSIFICATION OF DEFINITENESS OF A QUADRATIC FORM A
Let Q be XAX and variables (x1, x2, x3 … xn),
Rank (A) = r,
Index = s
1. Positive definite
If rank and index are equal i.e., r = n, s = n or if all the eigen values of A are positive.
2. Negative definite
If index = 0, i.e., r = n, s = 0 or if all the eigen values of A are negative.
3. Positive semi-definite
If rank and index are equal but less than n, i.e.,
s=r<n
[ | A | = 0]
or all eigen values of A are positive at least one eigen value is zero.
4. Negative semi-definite
If index is zero, i.e.,
s = 0,
r<n
[ | A | = 0]
or all eigen values of A are negative and at least one eigen value is zero.
5. Indefinite
If some eigen values are positive and some eigen values are negative.
6. Notes :
(1) If Q is negative definite (semi-definite) then – Q is positive definite (semi-definite).
(2) The classification of the definiteness of a quadratic form depends upon the location
of eigen values of A.
Example 82. Reduce to diagonal form the following symmetric matrix by congruent
transformation and interpret the result in terms of quadratic form
3 2 1
A 2 2 3
1 3 1
2 1
3
Solution. A 2
2 3
1
3 1
Let us reduce A into diagonal matrix.
IAI = A
1 0 0 1 0 0 3 2 1
0 1 0 A 0 1 0 2 2 3
| A| 0
0 0 1 0 0 1 1 3 1
R
S
Row transformation carried out on R.H.S. will be applied on R prefactor matrix.
Column transformation applied on R.H.S. will be applied on S post factor matrix.
1 0 0
3 2 1
1 0 0
2 1 0 A 0 1 0 0 2 11
3
3 3
1
0 0 1 11 2
0
0 1
3 3
3
2
R2 R1 ,
3
R
R3 1
3
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1 0 0 1
2 1 0 A
3
0
1
0 1 0
3
1
0
0 1
2
1
0 A 0
3
11
4
1 0
2
1
0
0 1
– 2
1
0 A 0
3
11 0
4 –
1
2
2
3
1
0
2
3
1
0
–
2
3
1
0
1 3 0
3
2
0
0
3
11
1 0
3
1
3 0
3
2
0 0 3
1 0 0
4 3
11 0
–
2
1 0
0
11
3
2
3
2
1
C2 C1 , C3 C1
3
3
0
11
3
39
2
0
2
3
0
R3
11
R2
2
0
0
39
–
2
C3 –
11
C2
2
Thus the matrix A is reduced to the diagonal form B.
3
P AP 0
0
0
2
3
0
2
1 3
0
0 where P 0 1
39
0 0
2
The canonical form (sum of the squares) is
3 0
2
Q Y BY [ y1 y2 y3 ] 0
3
0 0
1
x1
X = PY
i.e. x2 0
x3
0
x1 y1
2
y2 4 y3 ,
3
4
11
2
1
0
y1
0 y2 3 y 2 2 y 2 39 y 2
1
2
3
3
2
y3
39
2
2
3
1
0
x2 y2
4
y1
11 y2
2 y3
1
11
y3 ,
2
x3 = y3
The rank of A (r) = 3
The index of quadratic form (s) = 2
The signature of quadratic form [r – (r – P)] = 2 – (3 – 2) = 1
Ans.
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Determinants and Matrices
Example 83. Reduce the quadratic form 6 x12 3 x22 3x32 4 x1 x2 2 x2 x3 4 x3 x1
to the sum of square, by Lagrange Reduction Method
Solution.
Q 6 x12 3 x22 4 x1 x2 4 x1 x3 2 x2 x3
2
6 x12 x1 ( x2 x3 ) 3x22 3 x32 2 x2 x3
3
2
1
2
6 x1 ( x2 x3 ) 3x22 3x32 2 x2 x3 ( x2 x3 )2
3
3
2
1
1 7
2
7
6 x1 x2 x3 x22 x2 x3 x32
3
3
3
3
3
2
1
1 7
2
7
6 x1 x2 x3 x22 x2 x3 x32
3
3
3
7
3
2
2
2
2
1
1 7
1 7
7 1
6 x1 x2 x3 x2 x3 x32 x32
3
3 3
7 3
3 49
1
1 7
1 16
7
16
6 x1 x2 x3 x2 x3 x32 6 y12 y22 y32
3
3 3
7
7
3
7
where
1
1
y1 x1 x2 x3
3
3
1
y2 x2 x3
7
y3 x3
x3 y3
1
y3
7
1
2
x1 y1 y2 y3
3
7
x2 y2
EXERCISE 4.29
1. Express the quadratic form x12 2 x22 2 x32 2 x1x2 2 x2 x3
as product of matrices.
Ans. x1
x2
1 1 0 x1
x3 1 2 1 x2
0 1 2 x3
2. Write down the matrix of the quadratic form
1 2 4 0
2 2 0 0
x12 2 x22 7 x32 x42 4 x1 x2 8 x1 x3 6 x3 x4 Ans.
4 0 7 3
0 3 1
0
3. Find the transformation that will transform 10x2 + 2 y2 + 5 z2 + 6 yz – 10 z x – 4 x y
into a sum of square and find its reduced form.
1
8
Ans. Q 10 y12 y22 , P 0
5
0
1
5
1
4
5
1
4
0
1
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4. Find the transformation which will transform the following form into a sum of squares and
find the reduced form :
1 1 3
2
Q = 4x2 + 3y2 + z2 – 8 xy – 6 yz + 4 xz
Ans. Q 4 y12 y22 y32 , P
0 1 1
0 0 1
5. Reduce to sum to squares
1 2 4
0 1 4
2
2
2
2
2
2
Ans.
P
=
Q x1 2 x2 7 x3 4 x1 x2 8 x1 x3
Q y1 2 y2 9 y3 ,
0 0 1
6. Express the following quadratic form as “sum of squares” by congruent transformation and
write down the corresponding linear transformation
Q 10 x12 x22 x32 6 x1 x2 2 x2 x3 x3 x1
1 2
3
y2 , x1 y1 y2 , x2 = y2 + y3, x3 = y3
10
10
7. Reduce to the diagonal matrix by rational congruent transformation and interpret the result
in terms of quadratic form.
Ans. 10 y12
1 2 1
A 2 1 3
Ans. Q x12 x22 x32 4 x1 x2 6 x2 x3 2 x3 x1 ; Q y12 4 y22 25 y32
4
1 3 1
Determine the definiteness of the quadratic forms.
8. Q x12 2 x22 3x32 2 x2 x3 2 x3 x1 2 x1 x2
Ans. Indefinite.
9. Q 4 x12 x22 15 x32 4 x1 x2 .
Ans. Positive semi-definite
10. Q 5 x12 26 x22 10 x32 4 x2 x3 14 x3 x1 6 x1 x2
Ans. Positive semi-definite
11. Q x12 5 x22 x32 2 x1 x2 2 x2 x3 6 x3 x1.
Ans. Indefinite
12. Q 8 x12 7 x22 3x32 12 x1 x2 8 x2 x3 4 x3 x2
Ans. Positive semi-definite
13. Q – 4 x12 – 2 x22 –13x32 4 x1 x2 8 x2 x3 – 4 x3 x1.
Ans. Negative definite
3 2 4
2 2 6
A
14. Find the eigenvalues and corresponding eigen vector of
4 6 1
Verify that the eigen vectors are orthogonal and write down an orthogonal matrix M such
that MAM = D, where D is diagonal matrix.
Ans. – 9, 6, 3, [12 – 2], [212], [–2 2 1], M
P
, where P is modal matrix
3
4.70
DIFFERENTIATION AND INTEGRATION OF MATRICES
If the elements of a matrix A are function of scalar variable t, the matrix is called a matrix
function of t.
A = A(t) = [aij (t)]
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Determinants and Matrices
The differential coefficient of A w.r.t. “t” is defined as
d
d
A aij
dt
dt
dA
Hence the elements of the differentiated matrix
are the derivatives of the corresponding
dt
elements of A.
da1n
da11 da12
...
dt
dt
dt
d
da
da22
da2 n
A t 21
...
dt
dt
dt
dt
dan1 dan 2 ... dann
dt
dt
dt
It is easy to prove that
d
dB
da
A B A.
B
dt
dt
dt
The integral of the matrix A is defined as
Adt a dt
ij
Thus the integral of A is obtained by integrating each element of A.
Power series. Let A be a square matrix with all eigenvalues less than 1 in absolute value,
then a0I + aa A + a2A 2 + ... is convergent.
The following series are also convergent
A 1
e A I A2 ....
1 2
cos A 1
1 2 1 4
A A ....
2
4
sin A A
1 3 1 5
A A ....
3
5
1 A 1 1 A A2 ....
d tA
tA 1
1
3
tA
tA
2
Example 84. Prove that dt e Ae if e 1 1 2 (t A) 3 t A ....
1
d tA
d tA 1
e 1 t 2 A2 t 3 A3 ....
1
2
3
dt
dt
Solution.
d
d
d1
1
1 t A t 2 A2 t 3 A3 .....
dt
dt
dt 2
3
1
1
0 A t A2 t 2 A3 ....
1
2
1
1
A 1 t A t 2 A2 .... AetA
2
1
d2x
dx
4 12 x 0.
dt 2
dt
x 0 0; x 0 8 by Matrix Method .
Ans.
Example 85. Solve
...(1)
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Determinants and Matrices
359
x x1 and
Solution. Let
dx1
x2
dt
...(2)
(1) becomes
or
dx1
d dx1
12 x1
4
dt dt
dt
...(3)
dx2
12 x1 4 x2
dt
(2) and (3) are written in matrix form.
d x1 0 1 x1
dt x2 12 4 x2
From R.H.S. we have to find eigenvector.
1
0
Characteristic equation is 12 4 0
or
λ 4 12 0 or 2 4 12 0
or
2 6 0 2, 6
1
1
2 and 6
For = 2 and = –6, eigenvectors are
1
1 1 1 1 6
Matrix of eigen vectors = P =
,P
8 2 1
2 6
1 1 e 2t
Pe λ t P 1
2 6 0
Now
0 1 6 1
e 6t 8 2 1
e 6t 6 1 1 6e 2t 2e 6t
1 e 2t
= 8 2t
6e 6t 2 1 8 12e 2t 12e 6t
2e
By initial conditions x (0) = 0 , x(0) = 8
x1 1 6e 2t 2e 6t
x 2t
6 t
2 8 12e 12e
e 2t e 6t
2e 2t 6e 6t
e 2t e 6t 0 e 2t e 6t
2e 2t 6e 6t 8 2e 2t 6e6t
dx
2e 2t 6e 6t
dt
Example 86. Solve by matrix method.
x1 x e 2t e 6t , x2
Ans.
d2x
dx
5 6 x 0, x(0) 1. x '(0) 2.
2
dt
dt
Solution.
d2x
dx
5 6x 0
2
dt
dt
dx1
x2
dt
On substitution (1) becomes
Let x = x1 and
dx2
dx2
5 x2 6 x1 or
6 x1 5 x2
dt
dt
Equations (2) and (3) are written in a single matrix equation
...(1)
...(2)
...(3)
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Determinants and Matrices
d x1 0 1 x1
dt x2 6 5 x2
From R.H.S we have to find eigen vector.
0 λ 1
Characteristic equation is
0
5 λ
6
–( 5 – ) + 6 = 0 or – 5+ 6 = 0 = 2, 3
1
1
Eigen vectors for and = 3 are and
2
3
1 1
Matrix of eigen vectors = P =
,
2 3
3 1
P 1
2 1
1 1 e 2t
Pe λ t P 1
2 3 0
0 3 1
e3t 2 1
e 2t
e3t 3 1 3e 2t 2e3t
= 2t
3e3t 2 1 6e 2t 6e3t
2e
By initial conditions x (0) = 1 and x(0) = 2
x1 3e 2t 2e3t
x 2t
3t
2 6e 6e
e 2t e3t
2e 2t 3e3t
e 2 t e 3 t 1
2e 2t 3e 3t 2
3e 2t 2e3t 2e 2t 2e3t e 2t
= 2t
3t
4e 2t 6e3t 2e 2t
6e 6e
x1 = x = e2t
dx
x2
2e 2 t
dt
Example 87. Use matrices to solve the differential equation
d2 y
4 y 0, y (0) 1, y' (0) 0
dx 2
dy1
Solution. Let
y = y1,
y2
dx
dy2
d2 y
d dy1
4 y 0, or
4 y1
4 y1 0 or
2
dx
dx dx
dx
Differential equations (1) and (2) are written in matrix form
Ans.
...(1)
...(2)
d y1 0 1 y1
dx y2 4 0 y2
0 1
The characteristic equation of
is
4 0
0 λ
4
1
0 λ 2 4 0 λ = ±i 2
0 λ
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Determinants and Matrices
361
Eigenvector for= – i 2
0 i2
4
1
x1 0
i 2
or
0 i 2 x2 0
0
1 x1 0
0 x2 0
R2 R2 + 2iR1
x1 1
–i 2 x1 + x2 = 0 or
x2 i 2
Eigenvector for = –i 2
or
0 i2
4
1
x1 0
i 2
or
0 i 2 x2 0
4
i 2
0
1 x1 0
x 1
or i 2 x1 x2 0 or 1
0 x2 0
x2 i 2
1
2
1
1
, P 1
Let P
1
2i 2i
2
λx
Pe P
1
1 ei 2 x
1
2i 2i 0
1 i 2x 1 i2 x
2 e 2 e
= i2x
ie ie i 2 x
1 x1 0
i 2 x2 0
1
4i
1
4i
1
0 2
ei 2 x 1
2
1
i2x
4i e
i2 x
1
2ie
4i
1
e i 2 x 2
2ie i 2 x 1
2
1 i 2 x 1 i 2 x
e
e
cos 2 x
4i
4i
1 i2 x 1 i2 x
2 sin 2 x
e e
2
2
1
4i
1
4i
1
sin sx
2
cos 2 x
Applying the initial conditions, we get
1
sin 2 x 1 cos 2 x
or y1 cos 2 x and y2 2 sin 2 x
2
0 2 sin 2 x
cos 2 x
EXERCISE 4.30
Solve the following differential equations by matrix method:
y1 cos 2 x
y
2
2 sin 2 x
1.
2.
3.
4.
5.
d 2 y 4dy
3 y 0, y (0) 2, y' (0) 1
dx 2
dx
d 2 y 3dy
2 y 0, y (0) 5, y' (0) 8
dx 2 dx
d 2 y 5dy
14 y 0, y (0) 2, y' (0) 5
dx 2 dx
d2 y
μ 2 y 0, y (0) 1, y' (0) μ
2
dx
d2 y
9 y 0, y (0) 1, y' (0) 3
dx 2
Ans. y
Ans.
5 x e3 x
e
2
2
Ans. y = 2e x + 3e 2x
Ans. y = e 2x + e –7x
Ans. y = cosx + sinx
Ans.y = cosx + sinx
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Determinants and Matrices
4.71 COMPLEX MATRICES
Conjugate of a Complex Number
z = x + i y is called a complex number where 1 = i, x, y are real numbers. z x iy is
called the conjugate of the complex number z, e.g.,
Complex number
2 + 3i
– 4 – 5i
6i
2
Conjugate number
2 – 3i
– 4 + 5i
– 6i
2
Conjugate of a matrix. The matrix formed by replacing the elements of a matrix by their
respective conjugate numbers is called the conjugate of A and is denoted by A .
A (aij ) m n , then A (aij )m n
Example
3 4i 2 i 4
3 4i 2 i 4
If A
then A
i
2
3
i
2
3 i
i
4.72 THEOREM
If A and B be two matrices and their conjugate matrices are A and B respectively, then
(i) ( A) A
Proof. Let
(ii) ( A B) A B
A = [aij]m × n, then
(iii) (k A) k A
(iv) ( AB) A B
A [a ij ]m n where aij is the conjugate complex of a .
ij
The (i, j) th element of ( A) = the conjugate complex of the (i, j)th element of A
= the conjugate complex of aij
= aij = the (i, j)th element of A.
Hence
(ii) Let
( A) A.
A = [aij]m × n and B = [bij]m × n
Proved.
A [ a ij ]m n and B [bij ]m n
(i, j) th element of ( A B) = conjugate complex of (i, j) th element of (A + B)
= conjugate complex of (aij + bij) ( aij bij ) a ij b ij
= (i, j)th element of A + (i, j)th element of B
= (i, j)th element of ( A B)
Hence, ( A B) A B
(iii) Let
A = [aij]m × n, let k be any complex number.
Proved.
The (i, j)th element of (kA) = conjugate complex of the (i, j)th element of kA
= conjugate complex of kaij
= kaij k aij k (i, j )th element of A (i, j)th element of k . A
Hence,
(iv) Let
kA k A
A = [aij]m × n, B = [bij]n × p
Proved.
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Determinants and Matrices
Then
363
A [ aij ]m n , B [bij ]n p
The (i, j)th element of ( AB) = conjugate complex of (i, j)th element of AB
aij b jk
= conjugate complex of
j 1
n
aij b jk
j 1
n
n
a
ij
b jk
j 1
= (i, j)th element of A B
Hence,
Proved.
( AB) A B
4.73 TRANSPOSE OF CONJUGATE OF A MATRIX
The transpose of a conjugate of a matrix A is denoted by A or A* .
( A) A
The (i, j)th element of A = (j, i)th element of A
= conjugate complex of (j, i)th element of A.
2 3i 1 2i 2 4i
Example 88. If A 3 4i 4 3i 2 6i , find A
5
5 6i
3
2 3i 1 2i
Solution. We have, A 3 4i 4 3i
5
5 6i
2 3i
A ( A) 1 2i
2 4i
2 4i
2 3i 1 2i 2 4i
2 6i A 3 4i 4 3i 2 6i
5
3
5 6i
3
3 4i
5
4 3i 5 6i
2 6i
3
Ans.
EXERCISE 4.31
1 i 3 5i
1. If the matrix A
, find (i) A (ii) ( A) (iii) A (iv) ( A )
2
i
5
1 i 2i
1 i 3 5i
Ans. (i) A
(ii) ( A) 3 5i 5
2
i
5
1
i
3
5
i
1
i
2
i
(iv) ( A )
(iii) A
2i
3 5i 5
5
4.74 HERMITIAN MATRIX
Definition. A square matrix A = [aij] is said to be Hermitian if the (i, j)th element of A, i.e.,
aij a ji for all i and j.
b id
3 4i a
2
For example,
,
c
1 b id
3 4i
Hence all the elements of the principal diagonal are real.
A necessary and sufficient condition for a matrix A to be Hermitian is that A A .
Example 89. The characteristic roots of a Hermitian matrix are all real.
(A.M.I.E.T.E., June 2006)
Solution. We know that matrix A is Hermitian if
A A i.e., where A ( A ') or ( A) '
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Determinants and Matrices
Also
( A) A and ( AB ) B A
If is a characteristic root of matrix A then AX = X.
(AX) = (X)
or XA X.
But A is Hermitian A = A.
... (1)
X A X X AX X X
Again from (1)
I XAX = XX = XX.
Hence from (2) and (3) we conclude that showing that is real.
... (2)
... (3)
Deduction 1.From above we conclude that characteristic roots of real symmetric matrix are all
real, as in this case, real symmetric matrix will be Hermitian.
For symmetric, we know that
A = A.
( A ') A .
or
A A
A A as A is real. Rest as above.
Example 90. Prove that the following
(i) ( A ) A
(ii) ( A B) A B
(iii) (kA) k A
(iv) ( AB) B A
where A and B be the transposed conjugates of A and B respectively, A and B
being conformable to multiplication.
Solution.
(ii)
as ( A) A
( A ) [{( A )}] [ A] A
(i)
( A B) ( A B) ( A B) ( A) ( B) A B
(kA) (kA) (k A) k ( A) k A
(iii)
(iv)
( AB) ( AB) ( A B) ( B ) ( A) B A
1
Example 91. Prove that matrix A 1 i
2
Solution.
1 1 i 2
A 1 i 3 i
2
i
0
A A
1 i
3
i
Proved.
2
i is Hermitian.
0
1 1 i 2
( A) 1 i 3
i
2
i 0
A is Hermitian matrix.
Proved.
3 2i 2 i
i
0
3 4i is Skew-Hermitian matrix.
Example 92. Show that A 3 2i
2 i 3 4i 2i
Solution.
3 2i 2 i
i
A 3 2i
0
3 4i
2 i 3 4i
2i
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365
3 2i 2 i
i
( A) 3 2i
0
3 4i
2i
2 i 3 4i
3 2i 2 i
i
A 3 2i
0
3 4i
2 i 3 4i
2i
[ A ( A) ]
3 2i 2 i
i
3 2i
0
3 4i A
2 i 3 4i 2i
A = – A A is Skew-Hermitian matrix.
Proved.
Example 93. Show that the matrix B AB is Hermitian or Skew-Hermitian according as A is
Hermitian or Skew-Hermitian.
Solution. (i) Let A be Hermitian A A
Now
( B A B) ( AB) ( B )
B A B
B A B
( A A)
Hence, A AB is Hermitian.
(ii) Let A be Skew-Hermitian A A
Now,
( B AB) ( AB) ( B )
B A B
B A B
( A A)
Hence, B AB is Skew-Hermitian.
Proved.
4.75 SKEW-HERMITIAN MATRIX
Definition. A square matrix A = (aij) is said to be Skew-Hermitian matrix if the (i, j)th element
of A is equal to the negative of the conjugate complex of the (j, i)th element of A, i.e.,
aij a ji for all i and j.
If A is a Skew-Hermitian matrix, then
aii a ii
aii aii 0
Obviously, aii is either a pure imaginary number or must be zero.
a ib
3 4i
0
0
For example,
and
are Skew-Hermitian matrixes.
0
0
a ib
3 4i
A necessary and sufficient condition for a matrix A to be Skew-Hermitian is that A A.
Deduction 2. Characteristic roots of a skew Hermitian matrix is either zero or a pure
imaginary numbers.
If A is skew Hermitian, then iA is Hermitian.
Also be a characteristic root of A then AX = X.
(i .A) X = (i) X.
Above shows that i is characteristic root of matrix iA, which is Hermitian and hence i
should be real, which will be possible if is either pure imaginary or zero.
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Determinants and Matrices
Example 94. Show that every square matrix can be expressed as R + iS uniquely where R
and S are Hermitian matrices.
Solution. Let A be any square matrix. It can be rewritten as
1
1
A ( A A ) i ( A A ) R iS
2
2i
1
1
where R ( A A ), S ( A A )
2i
2
Now we have to show that R and S are Hermitian matrices.
1
1
1
1
R ( A A ) [ A ( A ) ] ( A A) ( A A ) R
2
2
2
2
Thus R is Hermitian matrix.
1
1
S ( A A ) ( A A )
2i
2i
1
1
1
[ A ( A ) ]
( A A) ( A A ) S
2i
2i
2i
Thus S is a Hermitian matrix.
Hence A = R + iS, where R and S are Hermitian matrices.
Now, we have to show its uniqueness.
Let A = P + iQ be another expression, where P and Q are Hermitian matrices, i.e.,
Now,
P P, Q Q
Then
A ( P iQ) P (iQ ) P iQ P iQ
A = P + iQ and A P iQ
1
1
( A A ) R and Q ( A A ) S
2i
2
Hence A = R + iS is the unique expression, where R and S are Hermitian matrices. Proved.
P
2
5 5i
1 i
2 i 4 2i as the sum of Hermitian matrix
Example 95. Express the matrix A 2i
7
1 i 4
and Skew-Hermitian matrix.
2
5 5i
2
5 5i
1 i
1i
A 2i
2 i 4 2i
A – 2i 2 i 4 2i
Solution.
...(1)
1 i 4
1 i 4
7
7
2i 1 i
2i 1 i
1 i
1 i
...(2)
( A) 2
2i
4
A 2
2i
4
5 5i 4 2i
5 5i 4 2i
7
7
On adding (1) and (2), we get
Let
2 2i 4 6i
2
A A 2 2i
4
2i
4 6i 2i
14
1 i 2 3i
1
1
R (A A ) 1 i
2
i
2
2 3i i
7
...(3)
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367
On subtracting (2) from (1), we get
2 2i 6 4i
2i
A A 2 2i
2i
8 2i
6 4i 8 2i
0
1 i 3 2i
i
1
1 i
(
A
A
)
i
4 i
S=
2
3 2i 4 i
0
Let
...(4)
From (3) and (4), we have
1 i 2 3i i
1 i 3 2i
1
A 1 i
2
i 1 i
i
4 i
7 3 2i 4 i
0
2 3i i
Hermitian matrix
Skew-Hermitian matrix
Ans.
Example 96. For any square matrix, if AA I show that A A I .
Solution. AA I
So A is invertible.
Let B be another matrix such that
(given)
AB = BA = I
...(1)
Now
B = BI = B ( AA )
( AA I )
= (BA) A
= IA
[Using (1)]
We know that
BA = I
[From (1)]
Putting the value of B from (2) in (1), we get
A A = I
Proved.
4.76 PERIODIC MATRIX
A square matrix is said to be periodic, if Ak+1 = A, where k is a positive integer. If k is the
least positive integer for which Ak+1 = A, then A is said to be of period k.
4.77 IDEMPOTENT MATRIX
A square matrix is said to be idempotent provided A2 = A.
Example 97. Determine all the idempotent diagonal matrices of order n.
Solution. Let A = diag. [d1, d2, d3, ... dn] be an idempotent matrix of order n.
Here, for the matrix ‘A’ to be idempotent A2 = A
d1 0
0 d
2
0 0
0 0
0........0 d1 0
0........0 0 d 2
d3 .......0 0 0
0........dn 0 0
d12
0
0
0
0
d 22
0
0
0........0 d1
0........0 0
d3 .......0 0
0........dn 0
0........0 d1
0........0 0
d32 .......0 0
0........d n2 0
0
d2
0
0
0
d2
0
0
0........0
0........0
d3 .......0
0........d n
0........0
0........0
d3 .......0
0........d n
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Determinants and Matrices
d12 d1; d22 d 2 .........dn2 dn
i.e.,
d1 = 0, 1; d2 = 0, 1; d3 = 0, 1 ............ dn = 0, 1.
Hence diag. [d1, d2, d3 … dn], is the required idempotent matrix where
d1 = d2 = d3 = ... dn = 0 or 1.
Ans.
EXERCISE 4.32
1. Which of the following matrices are Hermitian:
2 i 3 i
2i
1
(a) 2 i
(b) 4
2
4i
3
3 i 4 i
3
2. Which of the following matrices are
2 i 5 2i
4
1
0 i 3
2i
1
2
5
i
6 (c)
(d) 7 0 5i Ans. (c)
2
7 2i
3i 1 0
5 2i 2 5i
Skew-Hermitian:
3
1
2
3i 1
3 7 i
1
1 i 2 3i
0
1 2i 6
3i i
6
(b)
(d)
0
6i
(c) 1 i
4 6 3i
0
2 3i 6i
4i
7 i 8
Ans. (a), (c)
3. Give an example of a matrix which is Skew-symmetric but not Skew-Hermitian.
2i 3 4
(a) 3 3i 5
4 5 4i
2 3i
0
Ans.
2
3
i
0
4. If A be a Hermitian matrix, show that iA is Skew-Hermitian. Also show that if B be a SkewHermitian matrix, then iB must be Hermitian.
5. If A and B are Hermitian matrices, then show that AB + BA is Hermitian and AB – BA is SkewHermitian.
6. If A is any square matrix, show that A A is Hermitian.
5 2i 3
3
7. If H 5 2i
7
4i , show that H is a Hermitian matrix.
3
4i
5
Verify that iH is a Skew-Hermitian matrix.
8. Show that for any complex square matrix A,
T
*
(i) ( A A* ) is a Hermitian matrix, where A A
(ii) ( A A* ) is Skew-Hermitian matrix.
(iii) AA* and A* A are Hermitian matrices.
9. Show that any complex square matrix can be uniquely expressed as the sum of a Hermitian matrix
and a Skew-Hermitian matrix.
2 3i 4 5i
i
10. Express A 6 i
0
4 5i as the sum of Hermitian and Skew-Hermitian matrices.
2 i 2 i
i
11. Prove that the latent roots of a Hermitian matrix are all real.
2 i 3 1 3i
12. If A =
show that AA* is a Hermitian matrix; where A* is the conjugate
5 i 4 2i
transpose of A.
(AMIETE, June 2010)
4.78 UNITARY MATRIX
A square matrix A is said to be unitary matrix if A A A A I
Example 98. If A is a unitary matrix, show that AT is also unitary.
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Determinants and Matrices
369
Solution. A A A A I , since A is a unitary matrix.
( AA ) ( A A) I
(I I )
( AA ) ( A A) I
( A ) A A ( A ) I
AA A A I
[since ( A ) A ]
( AA )T ( A A)T ( I )T
( A )T AT AT ( A )T I
( AT ) AT AT ( AT ) I
Hence, AT is a unitary matrix.
Example 99. If A is a unitary matrix, show that A–1 is also unitary.
Proved.
Solution. AA A A I , since A is a unitary matrix.
( AA )1 ( A A)1 ( I )1
taking inverse
( A )1 A1 A1 ( A )1 I
( A1 ) A1 A1 ( A1 ) I
Hence, A–1 is a unitary matrix.
Proved.
Example 100. If A and B are two unitary matrices, show that AB is a unitary matrix.
Solution. A A A A I since A is a unitary matrix.
...(1)
B B B B I
Similarly,
Now,
...(2)
( AB)( AB) ( AB)( B A ) A( BB ) A
A I A
[From (2)]
[From (1)]
AA I
Again,
( AB) ( AB) ( B A ) ( AB)
B ( A A) B
[From (1)]
B IB B B
=I
[From (2)]
Hence, AB is a unitary matrix.
Proved.
1 1 1 i
Example 101. Prove that the matrix
is unitaryy.
3 1 i 1
Solution. Let A
1 1 1 i
3 1 i 1
1 1 1 i
3 1 i 1
1 1 1 i 1 1 1 i
A A
3 1 i 1
3 1 i 1
(1 i ) (1 i ) 1 3 0 1 0
1 1 (1 1)
I
(1 1) 1 3 0 3 0 1
3 (1 i ) 1(1 i )
Hence, A is a unitary matrix.
A
Proved.
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370
Determinants and Matrices
1 2i
0
Example 102. Define a unitary matrix. If N
is a matrix, then show that
0
1 2i
(I – N) (I + N)-1 is a unitary matrix, where I is an identity matrix.
(U.P., I Semester, Winter 2000)
Solution. Unitary matrix: A square matrix ‘A’ is said to be unitary if A A I , where
A ( A)T and I is an identity matrix.
we have
1 2i
0
N
1
2
i
0
1 2i 1
1 2 i
1 0 0
I N
0 1 – 2i
1
0 1 1 2i
Now we have to find (I + N)–1
...(1)
1 2i 1
1 2i
1 0 0
IN
0 1 2i
1
0 1 1 2i
| I + N | = 1 – (– 1 – 4) = 6
1 2i
1
Adj. ( I N )
1
1 2i
( I N ) 1
1 2i
Adj ( I N ) 1 1
1
|IN|
6 1 2i
...(2)
For unitary matrix, A A I
From (1) and (2), we get
( I – N ) ( I N ) 1
Now
( B )T
( B )T B
1
6
1 2i 1
1
1 2i
1 1 2i
1 2i 1 4
2 4i
B (say)
1 6 2 4i
4
2 4i
1 4
2 4i
4
6
1
36
2 4i 4
2 4i 1 36 0
4
I.
2 4i
2 4i
4
4 36 0 36
Hence the result.
Proved.
4.79 THE MODULUS OF EACH CHARACTERISTIC ROOT OF A UNITARY
MATRIX IS UNITY.
(U.P., I Semester, Compartment 2002)
Solution. Suppose A is a unitary matrix. Then
A A I .
Let be a characteristic root of A. Then
AX X
Taking conjugate transpose of both sides of (1), we get
( AX ) X
X A X
...(1)
...(2)
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Determinants and Matrices
371
From (1) and (2), we have
( X A ) ( AX ) X X
X ( A A) X X X
X IX X X
X X X X
( A . A I )
X X ( 1) 0
...(3)
Since, X X 0 therefore (3) gives
1 0. or 1
or
| |2 1
| | 1
Proved.
EXERCISE 4.33
1. Show that the matrix A
1 1
2 i
i
is unitary..
1
2. Prove that a real matrix is unitary if it is orthogonal.
3. Prove that the following matrix is unitary:
1
2 (1 i )
1 (1 i )
2
1 1
1
4. Show that U
1
3
2
1
1
( 1 i )
2
1
(1 i )
2
1
2 is a unitary matrix, where is the complex cube root of unity..
5. Prove that the latent roots of a unitary matrix have unit modulus.
6. Verify that the matrix
A
1 1 i 1 i
2 1 i 1 i
has eigen values with unit modulus.
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372
Vectors
5
Vectors
5.1
VECTORS
A vector is a quantity having both magnitude and direction such as force, velocity
acceleration, displacement etc.
5.2
ADDITION OF VECTORS
Let a and b be two given vectors
B
OA = a and AB = b then vector OB is called the
—
b
—
—
O
OA AB = OB
A
a
—
b
a+
sum of a and b .
Symbolically
a b = OB
5.3
RECTANGULAR RESOLUTION OF A VECTOR
^
^
^
Let OX, OY, OZ be the three rectangular axes. Let i , j , k be three unit vectors and
parallel to three axes.
If OP = n and the co-ordinates of P be (x, y, z)
^
OA = x i ,
^
OB = y j
and
Z
C
^
OC = z k
k
OP = OF FP
OP = OA OB OC
r
^
xi
^
^
2
OP = OF + FP
zk
O
yj
j
A
= xi + y j + zk
2
–
r
OP = (OA AF ) FP
(x, y, z)
P
X
2
B
F
(x, y)
Y
xi
= (OA2 + AF2) + FP2 = OA2 + OB2 + OC2 = x2 + y2 + z2
OP =
x 2 y2 z 2
x 2 y2 z 2
|r| =
5.4
UNIT VECTOR
^
^
Let a vector be x i + y ^j + z k .
^
Unit vector =
^
^
xi y j zk
x 2 y 2 z2
372
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373
Vectors
Example 1. If a and b be two unit vectors and be the angle between them, then find
the value of such that a + b is a unit vector.
(Nagpur, University, Winter 2001)
Solution.Let OA = a be a unit vector and AB = b is another unit vector and
A
be the angle between a and b .
If OB = c = a + b is also a unit vector then, we have
|OA| = 1
a
b
| OB| = 1
c
| OB| = 1
OAB is an equilateral triangle.
O
B
a+b=c
Hence each angle of OAB is
3
5.5
Ans.
POSITION VECTOR OF A POINT
The position vector of a point A with respect to origin O is the vector OA which is
used to specify the position of A w.r.t. O.
—
To find AB if the position vectors of the point A and point B are given.
If the position vectors of A and B are a and b . Let the origin be O.
OA = a ,
Then
B (b)
OB b
OA AB = OB
AB = OB OA
A (a)
O
AB = b a
AB = Position vector of B – Position vector of A
Example 2. If A and B are (3, 4, 5) and (6, 8, 9), find AB .
Solution.
AB = Position vector of B – Position vector of A
= (6 iˆ 8 ˆj 9 kˆ ) (3 ˆi 4 ˆj 5 kˆ )
= 3 iˆ 4 ˆj 4 kˆ
5.6
Ans.
RATIO FORMULA
To find the position vector of the point which divides the line joining two given
points.
Let A and B be two points and a point C divides AB in the ratio of m : n.
Let O be the origin, then
OA = a ,
and
OB b ,
OC ?
A
(a)
m
C
n
B
(b)
OC = OA AC
m
m
AB AC m n AB
mn
m
( AB b a )
= a m n . (b a)
= OA
O
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374
Vectors
mb na
OC =
mn
Cor. If m = n = 1, then C will be the mid-point, and
a b
OC =
2
5.7
PRODUCT OF TWO VECTORS
The product of two vectors results in two different ways, the one is a number and
the other is vector. So, there are two types of product of two vectors, namely scalar
product and vector product. They are written as a . b and a b .
5.8
SCALAR, OR DOT PRODUCT
The scalar, or dot product of two vectors a and b is defined to be a
b cos i.e.,
scalar where is the angle between a and b .
Symbolically, a . b = a b cos
B
Due to a dot between a and b this product is also called dot product.
The scalar product is commutative
Proof.
a . b = b . a
To Prove.
b . a =
=
b
b a cos ( )
a b cos
0
A
a
= a .b
Proved.
Geometrical interpretation. The scalar product of two vectors is the product of one
vector and the length of the projection of the other in the direction of the first.
B
OA = a and OB b
Let
then
b
a . b = (OA) . (OB) cos
ON
= OA . OB .
OB
= OA . ON
5.9
0
a
= (Length of a ) (projection of b along a )
A
N
USEFUL RESULTS
^
^
^
^
i . i = (1) (1) cos 0° = 1
i . j = (1) (1) cos 90° = 0
^
^
^
^
Similarly, j . j = 1,
^
^
^
^
k.k =1
Similarly, j . k = 0,
k.i
=0
Note. If the dot product of two vectors is zero then vectors are prependicular to each other.
5.10
WORK DONE AS A SCALAR PRODUCT
F
If a constant force F acting on a particle displaces it from A to B then,
Work done = (component of F along AB). Displacement
= F cos . AB
= F . AB
Work done = Force . Displacement
A
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B
375
Vectors
5.11
VECTOR PRODUCT OR CROSS PRODUCT
1.
The vector, or cross product of two vectors a
and b is defined to be a vector such that
b
(i) Its magnitude is a b sin , where is the
angle between a and b .
(ii) Its direction is perpendicular to both vectors
a
a and b .
(iii) It forms with a right handed system.
^
Let be a unit vector perpendicular to both the vectors a and b .
2.
a b sin .
a b =
Useful results
^
^
^
Since i , j , k are three mutually perpendicular unit vectors, then
^
^
^
^
^
^
^
^
i i = j j kk0
^
^
^
^
^
^
^
^
^
^
^
^
^
^
^
i j = j i k
^
^
j i i j
^
ĵ kˆ = k j i
and
k j jk
^
^
^
kˆ iˆ = i k j
5.12
i k k i
VECTOR PRODUCT EXPRESSED AS A DETERMINANT
If
^
^
^
^
^
^
a = a1 i a 2 j a3 k
b = b1 i b2 j b3 k
^
^
^
^
^
^
a b = ( a1 i a 2 j a3 k ) ( b1 i b2 j b3 k )
^
^
^
^
^
^
^
^
^
^
= a1 b1 ( i i ) a1 b2 ( i j ) a1b3 ( i k ) a2 b1 ( j i ) a2 b2 ( j j )
^
^
^
^
^
^
^
^
a2 b3 ( j k ) a3 b1 ( k i ) a3 b2 ( k j ) a3 b3 ( k k )
^
^
^
^
^
^
= a1 b2 k a1b3 j a2 b1 k a2 b3 i a3 b1 j a3 b2 i
^
^
^
= ( a2 b3 a3 b2 ) i ( a1 b3 a3 b1 ) j ( a1 b2 a2 b1 ) k
^
^
j
k
a1
b1
a2
b2
a3
b3
i
=
5.13
^
AREA OF PARALLELOGRAM
Example 3. Find the area of a parallelogram whose adjacent sides are i – 2j + 3 k and
2i + j – 4k.
^
Solution.
Vector area of gm =
^
^
i
j
k
1 2
3
2 1 4
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376
Vectors
^
^
^
^
^
^
= (8 3) i ( 4 6) j (1 4) k = 5 i 10 j 5 k
(5)2 (10)2 (5)2 = 5 6
Area of parallelogram =
5.14
Ans.
O
MOMENT OF A FORCE
Let a force F ( PQ ) act at a point P.
r
Moment of F about O
= Product of force F and perpendicular
^
distance (ON. )
N
P
F
Let a rigid body be rotating about the axis OA with the angular
velocity which is a vector and its magnitude is radians per second
and its direction is parallel to the axis of rotation OA.
^
^
Q
= (PQ) (ON)( ) = (PQ) (OP) sin ( ) = OP PQ
5.15
M r F
ANGULAR VELOCITY
V
B
P
A
Axis
Let P be any point on the body such that OP = r and
AOP = and AP OA. Let the velocity of P be V.
be a unit vector perpendicular to and
r .
Let
^
^
r = ( r sin ) = ( AP) = (Speed of P)
r
= Velocity of P to and r
5.16
Hence
V = r
O
SCALAR TRIPLE PRODUCT
Let a , b , c be three vectors then their dot product is written as a . ( b c ) or [ a b c ] .
^
^
^
^
^
^
^
^
^
a = a1 i a2 j a3 k , b b1 i b2 j b3 k , and c c1 i c 2 j c3 k
If
^
^
^
^
^
^
^
^
^
^
^
^
a . ( b c ) = ( a1 i a 2 j a3 k ) . [( b1 i b2 j b3 k ) ( c1 i c 2 j c 3 k )]
^
^
^
= ( a1 i a2 j a3 k ) . [( b2 c3 b3 c2 ) i ( b3 c1 b1 c3 ) j ( b1c2 b2 c1 ) k ]
= a1 (b2c3 – b3c2) + a2 (b3c1 – b1c3) + a3 (b1c2 – b2c1)
=
a1
a2
a3
b1
b2
b3
c1
c2
c3
Similarly, b . ( c a ) and c . ( a b ) have the same value.
a . (b c ) = b . (c a) = c . (a b)
The value of the product depends upon the cyclic order of the vector, but is
independent of the position of the dot and cross. These may be interchanged.
The value of the product changes if the order is non-cyclic.
Note. a ( b . c ) and ( a . b ) c are meaningless.
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377
Vectors
5.17
GEOMETRICAL INTERPRETATION
The scalar triple product a . ( b c ) represents the volume of the parallelopiped
having a , b , c as its co-terminous edges.
F
G
^
a . ( b c ) = a . Area of gm OBDC
= Area of gm OBDC × perpendicular distance A
–
between the parallel faces OBDC and AEFG.
a
= Volume of the parallelopiped
^
n
E
B
D
–
b
Note. (1) If a . ( b c ) = 0, then a , b , c are O
coplanar.
1
(a b c).
(2) Volume of tetrahedron
6
c–
C
Example 4. Find the volume of parallelopiped if
^
^
^
^
^
^
^
^
^
a 3 i 7 j 5 k,
b 3 i 7 j 3 k , and c 7 i 5 j 3 k
are the three co-terminous edges of the parallelopiped.
Solution.
Volume = a . ( b c )
3
7
5
= 3
7
3 = – 3 (–21 – 15) – 7 (9 + 21) + 5 (15 – 49)
7 5
3
= 108 – 210 – 170 = – 272
Volume = 272 cube units.
Ans.
Example 5. Show that the volume of the tetrahedron having A B , B C , C A as
concurrent edges is twice the volume of the tetrahendron having A, B , C as concurrent edges.
1
Solution. Volume of tetrahendron = ( A B ) . [( B C ) ( C A )]
6
1
( A B ) . [ B C B A C C C A]
=
[ C C 0]
6
1
(A B) . ( B C B A C A )
=
6
1
[ A . ( B C ) A . ( B A ) A . ( C A) B . ( B C ) B . ( B A ) B . ( C A )]
6
1
1
= [ A . ( B C ) B . (C A)] A . ( B C )
6
3
1
= 2 [A B C]
6
=
= 2 Volume of tetrahedron having A, B , C , as concurrent edges. Proved.
EXERCISE 5.1
1. Find the volume of the parallelopiped with adjacent sides.
OA = 3 i j ,
OB j 2 k , and OC i 5 j 4 k
extending from the origin of co-ordinates O.
Ans. 20
2. Find the volume of the tetrahedron whose vertices are the points A (2, –1, –3), B (4, 1, 3)
C (3, 2, –1) and D (1, 4, 2).
Ans. 7
1
3
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378
Vectors
^
^
^
^
^
3. Choose y in order that the vectors a 7 i y j kˆ , b 3 i 2 j k ,
^
^
^
c 5 i 3 j k are linearly dependent.
4. Prove that
Ans. y = 4
[a b , b c , c a] 2 [a b c]
5.18
COPLANARITY QUESTIONS
Example 6. Find the volume of tetrahedron having vertices
^
^
^
^
^
^
^
^
^
^
^
( j k ), ( 4 i 5 j q k ), ( 3 i 9 j 4 k ) and 4 ( i j k ) .
Also find the value of q for which these four points are coplanar.
(Nagpur University, Summer 2004, 2003, 2002)
^
^
^
^
^
^
^
^
^
^
^
Solution. Let A = j k , B 4 i 5 j q k , C 3 i 9 j 4 k , D 4( i j k )
^
^
^
^
^
^
^
AB = B A 4 i 5 j q k ( j kˆ ) 4 i 6 j (q 1) k
^
^
^ ^
^
^
^
AC = C A (3 i 9 ˆj 4 k ) ( j k ) 3 i 10 j 5 k
^
^
^
^
^
^
^
^
AD = D A 4 ( i j k ) ( j k ) 4 i 5 j 5 k
1
Volume of the tetrahedron =
[ AB AC AD ]
6
4
6
q1
1
1
3
10
5
{4 (50 25) 6 (15 20) ( q 1) (15 40)}
=
=
6
6
4 5
5
1
1
( 110 55 55 q)
{100 210 55 ( q 1)} =
=
6
6
1
55
( 55 55 q)
( q 1)
=
6
6
If four points A, B, C and D are coplanar, then ( AB AC AD ) = 0
i.e., Volume of the tetrahedron = 0
55
( q 1) = 0
q=1
Ans.
6
Example 7. If four points whose position vectors are a , b , c , d are coplanar, show that
[ a b c ] [ a d b ] [ a d c ] [ d b c ] (Nagpur University, Summer 2005)
Solution. Let A, B, C, D be four points whose position vectors are a , b , c , d .
AD = d a ,
BD d b
and
CD d c
If AD , BD , CD are coplanar, then
AD . (BD CD ) = 0
( d a ) . [( d b ) ( d c )] = 0
(d a) . [d d d c b d b c ]= 0
( d a ) . [ d c b d b c ] = 0
d . ( d c ) d . ( b d ) d . ( b c ) a . ( d c ) a . ( b a ) a .( b c ) = 0
0 0 [d b c ] [ d d c ] [ d b d ] [ a b c ] = 0
[ a b c ] [ a b d ] [ a d c ] [ d b c ] Proved.
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379
Vectors
EXERCISE 5.2
1. Determine such that
^
^
^
^
^
^
^
^
a i j k , b 2 i 4 k , and c i j 3 k are coplanar.
2. Show that the four points
^
^
^
^
^
^
^
^
^
^
Ans. = 5/3
^
^
6 i 3 j 2 k , 3 i 2 j 4 k , 5 i 7 j 3 k and 13 i 17 j k are coplanar.
3. Find the constant a such that the vectors
^
^
^ ^
^
^
^
^
^
2 i j k , i 2 j 3 k , and 3 i a j 5 k are coplanar.
4. Prove that four points
^
^
^
^
^
^
^
^
^
^
Ans. – 4
^
4 i 5 j k , ( j k ), 3 i 9 j 4 k , 4 ( i j k ) are coplanar.
5. If the vectors a , b and c are coplanar, show that
a
a . a
c
a . b
b . a
5.19
b
a . c
b . b
=0
b . c
VECTOR PRODUCT OF THREE VECTORS
(A.M.I.E.T.E., Summer, 2004, 2000)
Let a , b and c be three vectors then their vector product is written as
^
^
^
^
^
^
a (b × c ).
a = a1 i a2 j a3 k ,
Let
b = b1 i b2 j b3 k ,
^
^
^
c = c1 i c2 j c3 k
^
^
^
^
^
^
^
^
^
^
^
^
a ( b c ) = ( a1 i a2 j a3 k ) (b1 i b2 j b3 k ) ( c1 i c2 j c3 k )
^
^
^
= ( a1 i a2 j a3 k ) [( b2 c 3 b3 c 2 ) i ( b3 c1 b1c 3 ) j ( b1c 2 b2 c1 ) k ]
^
^
= [ a2 ( b1c2 b2 c1 ) a3 ( b3 c1 b1 c3 )] i [ a3 ( b2 c3 b3 c2 ) a1 ( b1 c2 b2 c1 )] j
^
[ a1 ( b3 c1 b1c 3 ) a2 ( b2 c3 b3 c 2 ) k ]
^
^
^
^
^
^
= ( a1 c1 a2 c 2 a3 c 3 ) ( b1 i b2 j b3 k ) ( a1 b1 a2 b2 a3 b3 ) ( c1 i c 2 j c 3 k )
= (a . c) b (a . b) c .
Ans.
Example 8. Prove that :
a ( b c ) b ( c a ) c ( a b ) 0 (Nagpur University, Winter 2008)
Solution. Here, we have
a (b c ) b (c a) c ( a b)
= [( a . c ) b ( a . b ) c ] [( b . a ) c ( b . c ) a ] [( c . b ) b ( c . a ) b ]
= [( b . a ) c ( a . b ) c ] [( c . b ) a ( b . c ) a ] [( a . c ) b ( c . a ) b ]
= [( a . b ) c ( a . b ) c ] [( b . c ) a ( b . c ) a ] [( c . a ) b ( c . a ) b ]
=0+0+0=0
Proved.
Example 9. Prove that :
^
^
^
^
^
^
i ( a i ) j ( a j) k ( a k) 2 a
^
^
(Nagpur University, Winter 2003)
^
Solution. Let a = a1 i a2 j a3 k
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380
Vectors
Now,
^
^
^
^
^
^
L.H.S. = i ( a i ) j ( a j ) k ( a k )
^
^
^
^
^
^
^
^
^
^
= i ( a1 i a2 j a3 k ) i j ( a1 i a2 j a3 k ) j
^
^
^
^
^
k ( a1 i a2 j a3 k ) k
^
^ ^
^
^
^
^
^
^
^
^ ^
^ ^
= i a1 ( i i ) a2 ( j i ) a3 ( k i ) j a1 ( i j ) a2 ( j j ) a3 ( k j )
^
^
^
^
^
^ ^
k a1 ( i k ) a2 ( j k ) a3 ( k k )
^
^
^
^
^
^
^
^
^
= i 0 a2 k a3 j j a1 k 0 a3 i k a1 j a2 i 0
^
^
^
^
^
^
^
^
^
^
^
^
= a2 ( i k ) a3 ( i j ) a1 ( j k ) a3 ( j i ) a1 ( k j ) a2 ( k i )
^
^
^
^
^
^
^
^
^
= a2 j a3 k a1 i a3 k a1 i a2 j = 2 a1 i 2 a2 j 2 a3 k
^
^
^
= 2 ( a1 i a2 j a3 k ) 2 a
Proved.
Example 10. Show that for any scalar , the vectors x , y given by
q (a b)
x a
a2
, y
(1 p ) p ( a b )
a
satisfy the equations
q
a2
p x q y a and x y b .
Solution. The given equations are
(Nagpur University, Winter 2004)
px qy = a
...(1)
x y = b
...(2)
Multiplying equation (1) vectorially by x , we get
x ( px qy ) = x a
p (x x) q (x y) = x a
as x x 0
q (x y) = x a,
x a = qb ,
[From (2) x y b ]
...(3)
Multiplying (3) vectorially by a , we have
a (x a) = a q b
( a . a) x ( a . x) a = q ( a b)
x =
( a . x) a
a2
a2 x = ( a . x ) a q ( a b )
a2 x ( a . x ) a = q ( a b )
q (a b)
a2
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381
Vectors
q (a b)
x = a
a2
a . x
where =
a2
q ( a b )
p
a
Substituting the value of x in (1), we get
q y = a
2
a
q (a b)
q y = a p a
a2
(1 p ) a
p (a b)
q
a2
EXERCISE 5.3
y =
Ans.
1. Show that a ( b a ) ( a b ) a
2. Write the correct answer
(a) ( A B ) C lies in the plane of
(i) A and B
(iii) C and A
(ii) B and C
Ans. (ii)
(b) The value of a . ( b c ) ( a + b c ) is
5.20
(ii) [ a , b , c ] [ b , c , a ] (iii) [ a , b , c ] (iv) None of these
Ans. (ii)
SCALAR PRODUCT OF FOUR VECTORS
(i) Zero
Prove the identity
( a b) . ( c d) = ( a . c ) (b . d) ( a . d) (b . c )
Proof. ( a b ) . ( c d ) = ( a b ) . r
= a . ( b r ) dot and cross can be interchanged.
Put c d r
= a . [ b ( c d )] = a . [( b . d ) c ( b . c ) d ]
= (a . c ) (b . d) (a . d) (b . c )
a . c
=
a .d
b . c
Proved.
b .d
EXERCISE 5.4
1. If a 2 i 3 j k , b i 2 j 4 k , c i j k , find ( a b ) . ( a c ).
2
Ans. –74
2. Prove that ( a b ) . ( a c ) a ( b . c ) ( a . b ) ( a . c ) .
5.21
VECTOR PRODUCT OF FOUR VECTORS
Let a , b , c and d be four vectors then their vector product is written as
( a b) ( c d)
Now, ( a b ) ( c d ) = r ( c d )
[Put a b r ]
= (r . d) c (r . c) d
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Vectors
= [( a b ) . d ] c [( a b ) . c ] d
= [ a b d] c [a b c ] d
( a b ) ( c d ) lies in the plane of c and d .
...(1)
Again, ( a b ) ( c d ) = ( a b ) s
[Put = c d s ]
= s (a b) = ( s . b) a ( s . a) b
= [( c d ) . b ] a [( c d ) . a ] b = [( b c d ] a [ a c d ] b
( a b ) ( c d ) lies in the plane of a and b .
...(2)
Geometrical interpretation : From (1) and (2) we conclude that ( a b ) ( c d ) is
a vector parallel to the line of intersection of the plane containing a , b and plane
containing c , d .
Example 11. Show that
( B C ) ( A D ) (C A) ( B D ) ( A B ) (C D ) 2 ( A B C ) D
Solution. L.H.S. = ( B C ) ( A D ) ( C A ) ( B D ) ( A B ) ( C D )
= [( B C D ) A ( B C A) D ] [( C A D ) B (C A B ) D] [( B C D ) A ( A C D ) B ]
= ( B C D ) A ( B C D ) A (C A D ) B ( A C D ) B ( B C A) D (C A B ) D
= ( A C D) B ( A C D) B ( A B C ) D ( A B C) D
= 2 ( A B C ) D = R.H.S.
Proved.
EXERCISE 5.5
Show that:
1. ( b c ) ( c a ) c ( a b c ) when ( a b c ) stands for scalar triple product.
2. [ b c ,
a b )] [ a v c ]2
c a,
3. d [ a { b ( c d )}] [( b . d )[ a . ( c d )]
4.
a [ a [ a ( a b )] a 2 ( b a )
5. [( a b ) ( a c )] . d ( a . d ) [ a b c ]
2
6. 2a
7.
8.
^
2
a i
^
^
2
a j
^
^
^
2
a k
^
^
^
a b [( i a ) . b] i [( j a ) . b ] j [( k a ) . b ] k
p [( a q ) ( b r )] q [( a r ) ( b p )] r [( a p ) ( b q )] 0
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383
5.22 VECTOR FUNCTION
If vector r is a function of a scalar variable t, then we write
r = r (t )
If a particle is moving along a curved path then the position vector r of the particle is a
function of t. If the component of f (t) along x-axis, y-axis, z-axis are f1(t), f2(t), f3(t) respectively.
Then,
—
f1 (t ) i f 2 (t ) j f3 (t ) k
f (t ) =
5.23 DIFFERENTIATION OF VECTORS
Let O be the origin and P be the position of a moving particle at time t.
—
Let
OP = r
Let Q be the position of the particle at the time t + t and
Q
t
en
ng
Ta
—
the position vector of Q is OQ = r r
—
—
—
PQ = OQ OP
r
P (r)
= (r r) r r
r+r
r
is a vector. As t 0, Q tends to P and the chord
t
becomes the tangent at P.
r
dr
lim
We define
= t 0 t , then
dt
r
O
dr
is a vector in the direction of the tangent at P.
dt
dr
is also called the differential coefficient of r with respect to ‘t’.
dt
d2 r
Similarly,
is the second order derivative of r .
2
dt
dr
d2 r
gives the velocity of the particle at P, which is along the tangent to its path. Also
dt
dt 2
gives the acceleration of the particle at P.
5.24 FORMULAE OF DIFFERENTIATION
d d F dG
(F G)
(i)
dt
dt
dt
d
d
dF
(ii)
( F )
F
dt
dt
dt
(U.P. I semester, Dec. 2005)
d dG dF
d dG dF
(iii)
(F . G) F .
.G (iv)
(F G) F
G
dt
dt
dt
dt
dt
dt
d d a d b d c
(v)
[a b c ]
b c a
c a b
dt
dt
dt
dt
d
da
d b d c
(vi)
[ a ( b c )]
(b c ) a
c a b
dt
dt
dt
dt
The order of the functions F , G is not to be changed.
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Vectors
Example 12. A particle moves along the curve r (t 3 4 t ) i (t 2 4 t ) j (8 t 2 3 t 3 ) k ,
where t is the time. Find the magnitude of the tangential components of its acceleration
at t = 2.
(Nagpur University, Summer 2005)
Solution. We have,
r
= (t 3 4t ) i (t 2 4t ) j (8t 2 3t 3 ) k
dr
(3t 2 4) i (2t 4) j (16t 9t 2 ) k
Velocity =
dt
t = 2,
Velocity = 8 i 8 j 4 k
At
Acceleration = a =
At
d2 r
dt 2
t = 2
6t i 2 j (16 18t ) k
a 12 i 2 j 20 k
The direction of velocity is along tangent.
So the tangent vector is velocity.
Unit tangent vector,
v
8 i 8 j 4k 8 i 8 j 4k 2 i 2 j k
T =|v|
12
3
64 64 16
Tangential component of acceleration, at = a.T
24 4 20 48
2i 2 j k
= (12 i 2 j 20 k ).
=
= 16 Ans.
3
3
3
d
da
db
[a b] u (a b)
u a and
u b then prove that
dt
dt
dt
(M.U. 2009)
Solution. We have,
Example 13. If
d b
da
d
b
[a b] = a
dt
dt
dt
= a (u b) (u a) b
=
a ( u b ) b (u a )
= (
a . b ) u ( a . u ) b [( b . a ) u (b . u ) a ]
(Vector triple product)
= (a . b ) u (u . a ) b (a . b ) u (u . b ) a
= (u . b ) a (u . a ) b
=
Proved.
u (a b)
2
2
2
2
Example 14. Find the angle between the surface x + y + z = 9 and z = x + y2 – 3 at
(2, –1, 2).
(M.D.U. Dec. 2009)
Solution. Here, we have
x2 + y2 + z2 = 9
...(1)
z = x2 + y2 – 3
...(2)
2
2
2
Normal to (1) 1= (x + y + z – 9)
2
2
2
ˆj
kˆ
= iˆ
( x y z – 9) = 2 x iˆ + 2 y ĵ + 2 z k̂
x
y
z
Normal to (1) at (2, – 1, 2), 1 = 4 iˆ – 2 ĵ + 4 k̂
...(3)
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Vectors
385
2 = (z – x2 – y2 + 3)
Normal to (2),
2
2
ˆj
kˆ
( z – x – y 3) = – 2 x iˆ – 2 y ĵ + k̂
= iˆ
y
z
x
Normal to (2) at (2, – 1, 2), 2 = – 4 iˆ + 2 ĵ + k̂
...(4)
1.2 = | 1 | | 2 | cos
1.2
(4iˆ – 2 ˆj 4kˆ).(– 4iˆ 2 ˆj kˆ)
– 16 – 4 4
=
= ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
| 1 | | 2 |
16 4 16 16 4 1
|4i – 2 j 4k | | – 4i 2 j k |
– 16
–8
=
=
6 21
3 21
–1 – 8
= cos
3 21
–8
Hence the angle between (1) and (2) cos –1
Ans
3 21
cos =
EXERCISE 5.6
1. The coordinates of a moving particle are given by x = 4t
t2
t3
and y = 3 6t . Find the
2
6
Ans. 4.47, 2.24
velocity and acceleration of the particle when t = 2 secs.
2. A particle moves along the curve
x = 2t2,
y = t2 – 4t
and
z = 3t – 5
where t is the time. Find the components of its velocity and acceleration at time t = 1, in the
8 14
14
,
7
7
3. Find the unit tangent and unit normal vector at t = 2 on the curve x = t2 – 1, y = 4t – 3,
1
1
z = 2t2 – 6t where t is any variable.
Ans. (2 i 2 j k ),
(2 i 2 k )
3
3 5
direction i 3 j 2 k .
(Nagpur, Summer 2001) Ans.
4. Prove that
d dG d F
( F G) F
G
dt
dt
dt
5. Find the angle between the tangents to the curve r t 2 i 2t j t 3 k , at the points t = ± 1.
9
Ans. cos 1
17
6. If the surface 5x2 – 2byz = 9x be orthogonal to the surface 4x2y + z3 = 4 at the point (1, –1, 2)
then b is equal to
(a) 0
(b) 1
(c) 2
(d) 3
(AMIETE, Dec. 2009) Ans. (b)
5.25 SCALAR AND VECTOR POINT FUNCTIONS
^
Point function. A variable quantity whose value at any point
N
R
in a region of space depends upon the position of the point, is
Q +
called a point function. There are two types of point functions.
n r
d
=
(i) Scalar point function. If to each point P (x, y, z) of a
c
=
P
region R in space there corresponds a unique scalar f (P), then f is
c
called a scalar point function. For example, the temperature
distribution in a heated body, density of a body and potential due to gravity are the examples of
a scalar point function.
(ii) Vector point function. If to each point P (x, y, z) of a region R in space there corresponds
a unique vector f (P), then f is called a vector point function. The velocity of a moving fluid,
gravitational force are the examples of vector point function.
(U.P., I Semester, Winter 2000)
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386
Vectors
Vector Differential Operator Del i.e.
The vector differential operator Del is denoted by . It is defined as
j
k
= i
x
y
z
5.26 GRADIENT OF A SCALAR FUNCTION
If (x, y, z) be a scalar function then i
is called the gradient of the scalar
j
k
x
y
z
function .
And is denoted by grad .
Thus,
grad = i
j
k
x
y
z
j
k ( x, y , z )
gard = i
y
z
x
gard =
( is read del or nebla)
5.27 GEOMETRICAL MEANING OF GRADIENT, NORMAL
(U.P. Ist Semester, Dec 2006)
If a surface (x, y, z) = c passes through a point P. The value of the function at each point
on the surface is the same as at P. Then such a surface is called a level surface through P. For
example, If (x, y, z) represents potential at the point P, then equipotential surface (x, y, z) = c
is a level surface.
Two level surfaces can not intersect.
Let the level surface pass through the point P at which the value of the function is . Consider
another level surface passing through Q, where the value of the function is + d.
—
Let r and r r be the position vector of P and Q then PQ r
j
k
.(i dx j dy k dz )
.dr = i
y
z
x
=
dx
dy
dz d
x
y
z
If Q lies on the level surface of P, then d = 0
Equation (1) becomes
. dr = 0. Then is to dr (tangent).
...(1)
Hence, is normal to the surface (x, y, z) = c
Let = ||
N is a unit normal vector. Let n be the perpendicular distance
N , where
between two level surfaces through P and R. Then the rate of change of in the direction of the
normal to the surface through P is
d
=
dn
.
n
.d r
lim
n 0 n
n 0
n
lim
| | N .d r
= lim
n 0
n
=
lim
n 0
N . r | N | | r | cos
| r | cos n
| | n
| |
n
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Vectors
387
n
Hence, gradient is a vector normal to the surface = c and has a magnitude equal to the
rate of change of along this normal.
|| =
5.28 NORMAL AND DIRECTIONAL DERIVATIVE
(i) Normal. If (x, y, z) = c represents a family of surfaces for different values of the constant
c. On differentiating , we get d = 0
But
d = . d r
so . d r = 0
The scalar product of two vectors and d r being zero, and d r are perpendicular to
each other. d r is in the direction of tangent to the given surface.
Thus is a vector normal to the surface (x, y, z) = c.
(ii) Directional derivative. The component of in the direction of a vector d is equal to
.d and is called the directional derivative of in the direction of d .
= lim
where, r = PQ
r
0
r
r
is called the directional derivative of at P in the direction of PQ.
r
Let a unit vector along PQ be N .
n
= cos
r
=
r
Now
r =
lim
r 0 n
.N
N
= N . N | |
n
cos
n
...(1)
N . N
N . N n
n
From (1), r
N.N
= N .
( N | | )
, directional derivative is the component of in the direction N .
r
= N . | | cos | |
r
Hence, is the maximum rate of change of .
Hence,
Example 15. For the vector field (i) A miˆ and (ii) A m r . Find . A and A .
Draw the sketch in each case.
(Gujarat, I Semester, Jan. 2009)
Solution. (i) Vector A m i is represented in the figure (i).
(ii)
(iii)
A = m r is represented in the figure (ii).
. A = i x j y k z .( x i y j z k ) 1 1 1 3
(iv)
. A = 3 is represented on the number line at 3.
j
k
A = i
(x i y j z k)
y
z
x
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388
Vectors
i
j
=
x y
x
y
are represented in the adjoining figure.
k
z
z
=0
k
k
k^
O
O
. i^
^i
r
m
r
j
j
0
1
2
3
Number line
m
i
(i)
(ii)
2
j
O
i
(iii)
(iv)
3 2
Example 16. If = 3x y – y z ; find grad at the point (1, –2, –1).
(AMIETE, June 2009, U.P., I Semester, Dec. 2006)
Solution.
grad =
j
k (3 x 2 y y 3 z 2 )
= i
y
z
x
2
3 2
2
3 2
2
3 2
= i x (3x y y z ) j y (3x y y z ) k z (3x y y z )
= i (6 xy ) j (3 x 2 3 y 2 z 2 ) k ( 2 y 3 z )
grad at (1, –2, –1) = i (6) (1) (2) j [(3) (1) 3(4) (1)] k ( 2)( 8) (1)
= 12 i 9 j 16 k
Ans.
Example 17. If u = x + y + z, v = x2 + y2 + z2, w = yz + zx + xy prove that grad u,
grad v and grad w are coplanar vectors.
[U.P., I Semester, 2001]
Solution. We have,
grad u = i
grad v = i
grad w = i
j
k ( x y z) i j k
x
y
z
j
k ( x2 y 2 z 2 ) 2 x i 2 y j 2 z k
x
y
z
j
k
( yz zx xy ) i ( z y ) j ( z x ) k ( y x)
x
y
z
[For vectors to be coplanar, their scalar triple product is 0]
Now, grad u.(grad v × grad w) =
1
= 2 x yz
zy
= 2( x y z )
1
1
1
2x
2y
2z
zy
zx
yx
1
1
x yz
x y z
z x
1
1
1
1
yz
zx
yx
1
1
2
1
1
1
x
y
z
zy
zx
yx
[Applying R2 R2 + R3]
0
x y
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389
Since the scalar product of grad u, grad v and grad w are zero, hence these vectors are
coplanar vectors.
Proved.
Example 18. Find the directional derivative of x2y2z2 at the point (1, 1, –1) in the direction
of the tangent to the curve x = et, y = sin 2t + 1, z = 1 – cos t at t = 0.
(Nagpur University, Summer 2005)
Solution. Let = x2 y2 z2
Directional Derivative of
2 2 2
j
k
= = i
(x y z )
y
z
x
= 2 xy 2 z 2 i 2 yx 2 z 2 j 2 zx 2 y 2 k
Directional Derivative of at (1, 1, –1)
= 2(1)(1) 2 (1)2 i 2(1)(1) 2 (1) 2 j 2(1)(1)2 (1)2 k
= 2i 2 j2k
t
= x i y j z k e i (sin 2t 1) j (1 cos t ) k
=
r
Tangent vector,
...(1)
T
d r
e t i 2 cos 2t j sin t k
dt
Tangent(at t = 0) = e0 i 2 (cos 0) j (sin 0) k i 2 j
Required directional derivative along tangent = (2 i 2 j 2 k )
...(2)
(i 2 j)
1 4
[From (1), (2)]
240
6
5
5
Example 19. Find the unit normal to the surface xy3z2 = 4 at (–1, –1, 2).
Solution. Let (x, y, z) = xy3z2 = 4
We know that is the vector normal to the surface (x, y, z) = c.
Normal vector = = i x j y k z
=
Now
= i
(M.U. 2008)
( xy 3 z 2 ) j ( xy 3 z 2 ) k ( xy 3 z 2 )
x
y
z
Ans.
3 2
2 2
3
Normal vector = y z i 3 xy z j 2 xy z k
Normal vector at (–1, –1, 2) = 4 i 12 j 4 k
Unit vector normal to the surface at (–1, –1, 2).
4 i 12 j 4 k
1
(i 3 j k )
=
Ans.
| |
16 144 16
11
Example 20. Find the rate of change of = xyz in the direction normal to the surface
x2y + y2x + yz2 = 3 at the point (1, 1, 1).
(Nagpur University, Summer 2001)
Solution. Rate of change of =
j
k ( x y z ) i yz j xz k xy
= i
y
z
x
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Vectors
Rate of change of at (1, 1, 1) = ( i j k )
Normal to the surface = x2y + y2x + yz2 – 3 is given as 2
j
k ( x y y 2 x yz 2 3)
= i
x
y
z
2
2
2
= i (2 xy y ) j ( x 2 xy z ) k 2 yz
()(1, 1, 1) = 3 i 4 j 2 k
Unit normal =
3i 4 j 2k
9 16 4
Required rate of change of
= ( i j k ).
(3 i 4 j 2 k )
=
3 42
9
Ans.
9 16 4
29
29
Example 21. Find the constants m and n such that the surface m x2 – 2nyz = (m + 4)x will
be orthogonal to the surface 4x2y + z3 = 4 at the point (1, –1, 2).
(M.D.U. Dec. 2009, Nagpur University, Summer 2002)
Solution. The point P (1, –1, 2) lies on both surfaces. As this point lies in
mx2 – 2nyz = (m + 4)x, so we have
m – 2n (–2) = (m + 4)
m + 4n = m + 4
n=1
Let 1 = mx2 – 2yz – (m + 4)x and 2 = 4x2y + z3 – 4
Normal to 1 = 1
j
k [ mx 2 2 yz ( m 4) x ]
= i
x
y
z
= i (2mx m 4) 2 z j 2 y k
Normal to 1 at (1, –1, 2) = i (2m m 4) 4 j 2 k = (m 4) i 4 j 2 k
Normal to 2 = 2
j
k (4 x 2 y z 3 4) = i 8 xy 4 x 2 j 3 z 2 k
= i
y
z
x
Normal to 2 at (1, –1, 2) = – 8 i 4 j 12 k
Sinec 1 and 2 are orthogonal, then normals are perpendicular to each other.
1 . 2 = 0
[( m 4) i 4 j 2 k ].[ 8 i 4 j 12 k ] = 0
– 8 (m – 4) – 16 + 24 = 0
m – 4 = –2 + 3
m=5
Hence m = 5, n = 1
Ans.
Example 22. Find the values of constants and so that the surfaces x2 – yz = ( + 2) x,
4x2y + z3 = 4 intersect orthogonally at the point (1, – 1, 2).
(AMIETE, II Sem., Dec. 2010, June 2009)
Solution. Here, we have
x2 – yz = ( + 2) x
...(1)
4x2 y + z3 = 4
...(2)
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391
Normal to the surface (1), λx yz (λ 2) x
ˆ 2
iˆ ˆj
k x yz ( 2) x
y
z
x
iˆ (2x 2) ˆj (z ) kˆ (y )
Normal at (1, –1, 2) = iˆ (2 – – 2) – ĵ (–2) + k̂
...(3)
= iˆ ( – 2) + ĵ z (2) + k̂
Normal at the surface (2)
iˆ ˆj kˆ (4 x 2 y z 3 4)
y
z
x
= iˆ (8 × y) + ĵ (4x2) + k̂ (3z2)
Normal at the point (1, –1, 2) = – 8 iˆ + 4 ĵ + 12 k̂
Since (3) and (4) are orthogonal so
...(4)
iˆ ( 2) ˆj (2 ) kˆ . 8 iˆ 4 ˆj 12 kˆ 0
8 ( 2) 4(2) 12 0 8 16 8 12 0
8 20 16 0
2 5 4 0
Point (1, – 1, 2) will satisfy (1)
4(2 5 4) 0
2 5 4
...(5)
(1)2 (1) (2) ( 2) (1) + 2 = + 2 = 1
Putting = 1 in (5), we get
9
2 5 4
2
9
and =1
Hence
Ans.
2
2
2
2
2
2
Example 23. Find the angle between the surfaces x + y + z = 9 and z = x + y – 3 at
the point (2, –1, 2).
(Nagpur University, Summer 2002)
Solution. Normal on the surface (x2 + y2 + z2 – 9 = 0)
2
j
k ( x y 2 z 2 9) (2 x i 2 y j 2 z k )
= i
y
z
x
Normal at the point (2, –1, 2) = 4 i 2 j 4 k
2
j
k ( x y 2 z 3)
Normal on the surface (z = x2 + y2 – 3) = i
y
z
x
...(1)
= 2x i 2 y j k
Normal at the point (2, –1, 2) = 4 i 2 j k
Let be the angle between normals (1) and (2).
(4 i 2 j 4 k ).(4 i 2 j k ) =
...(2)
16 4 16 16 4 1 cos
16 + 4 – 4 = 6 21 cos
16 = 6 21 cos
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392
Vectors
8
cos =
= cos 1
8
Ans.
3 21
3 21
1
Example 24. Find the directional derivative of in the direction r wheree r x i y j z k .
r
(Nagpur University, Summer 2004, U.P., I Semester, Winter 2005, 2002)
Solution. Here,
(x, y, z) =
1
r
1
x2 y 2 z 2
(x2 y2 z2 )
1
2
1
2
j
k ( x y2 z2 ) 2
Now 1 = i
y
z
x
r
1
1
1
2
( x y 2 z2 ) 2 i ( x2 y 2 z2 ) 2 j (x2 y 2 z2 ) 2 k
x
y
z
3
3
3
1
1
1
= ( x 2 y 2 z 2 ) 2 2 x i ( x 2 y 2 z 2 ) 2 2 y j ( x 2 y 2 z 2 ) 2 2 z k
2
2
2
=
=
(x i y j z k )
...(1)
( x 2 y 2 z 2 )3/ 2
r = unit vector in the direction of x i y j z k
and
=
xi y j zk
...(2)
x2 y 2 z2
So, the required directional derivative
= . r
xi y j zk
( x2 y 2 z 2 )
1
1
2
= 2
2
2
x y z
r
.
3/ 2
xi y j zk
( x 2 y 2 z 2 )1/ 2
x2 y 2 z 2
( x 2 y 2 z 2 )2
[From (1), (2)]
Ans.
Example 25. Find the direction in which the directional derivative of (x, y) =
x2 y 2
at
xy
(1, 1) is zero and hence find out component of velocity of the vector r (t 3 1) i t 2 j in
the same direction at t = 1.
(Nagpur University, Winter 2000)
x2 y2
i
j
k
Solution. Directional derivative = =
y
z xy
x
2
2
2
2
= i x y.2 x ( x y ) y j xy.2 y x ( y x )
2 2
2 2
x y
x y
x2 y y3
xy 2 x3
= i
j 2 2
2 2
x y
x y
Directional Derivative at (1, 1) = i 0 j 0 0
Since ()(1, 1) = 0, the directional derivative of at (1, 1) is zero in any direction.
Again
r = (t 3 1) i t 2 j
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Vectors
Velocity,
393
v =
dr
3t 2 i 2t j
dt
Velocity at
t = 1 is = 3 i 2 j
The component of velocity in the same direction of velocity
3 i 2 j 9 4 13
(3
i
2
j
).
=
Ans.
9 4
13
Example 26. Find the directional derivative of (x, y, z) = x2 y z + 4 x z2 at (1, –2, 1) in
the direction of 2 i j 2 k . Find the greatest rate of increase of .
(Uttarakhand, I Semester, Dec. 2006)
Solution. Here, (x, y, z) = x2y z + 4xz2
2
j
k ( x yz 4 xz 2 )
Now,
= i
y
z
x
= (2 xyz 4 z 2 ) i ( x 2 z ) j ( x 2 y 8 xz ) k
at (1, – 2, 1) = {2(1) (2)(1) 4(1)2 } i (1 1) j {1( 2) 8(1)(1)} k
= (4 4) i j (2 8) k = j 6 k
1
(2 i j 2 k )
=
unit
vector
=
a
3
4 1 4
So, the required directional derivative at (1, –2, 1)
1
1
13
= . a ( j 6 k ). (2 i j 2 k ) = (1 12)
3
3
3
Let
2 i j 2k
Greatest rate of increase of = j 6 k
=
1 36
= 37
Ans.
Example 27. Find the directional derivative of the function = x2 – y2 + 2z2 at the point P
(1, 2, 3) in the direction of the line PQ where Q is the point (5, 0, 4).
(AMIETE, Dec. 20010, Nagpur University, Summer 2008, U.P., I Sem., Winter 2000)
Solution. Directional derivative =
2
j
k ( x y2 2z2 ) 2x i 2 y j 4z k
= i
y
z
x
Directional Derivative at the point P (1, 2, 3) = 2 i 4 j 12 k
...(1)
PQ = Q P = (5, 0, 4) – (1, 2, 3) = (4, –2, 1)
Directional Derivative along PQ = (2 i 4 j 12 k ).
=
8 8 12
...(2)
(4 i 2 j k )
16 4 1
[From (1) and (2)]
28
Ans.
21
x
Example 28. For the function (x, y) = 2
, find the magnitude of the directional
x y2
derivative along a line making an angle 30° with the positive x-axis at (0, 2).
(A.M.I.E.T.E., Winter 2002)
21
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394
Vectors
Solution. Directional derivative =
x
= i x j y k z 2
x
y2
= i
y2 x2
1
x (2 x ) x (2 y )
2
j 2
= i 2
2
( x y 2 )2
( x y2 )2
x y
j
2 xy
j
A
( x 2 y 2 )2
( x 2 y 2 )2
Directional derivative at the point (0, 2)
= i
4 0
(0 4)
2
j
2(0) (2)
(0 4)
2
1
(0, 2)
i
4
1j
—
2
30°
C
i
3
—
i
2
—
Directional derivative at the point (0, 2) in the direction CA i.e. 3 i 1 j
2
2
CA OB BA i cos 30 j sin 30
i 3 1
3 1
.
i j
=
i j
4 2
2
2
2
3
=
Ans.
8
2
Example 29. Find the directional derivative of V , where V xy 2 i zy 2 j xz 2 k , at the
point (2, 0, 3) in the direction of the outward normal to the sphere x2 + y2 + z2 = 14 at the
point (3, 2, 1).
(A.M.I.E.T.E., Dec. 2007)
Solution.
V2 = V .V
2
2
2
2
2
2
= ( xy i zy j xz k ).( x y i z y j xz k ) = x2y4 + z2y4 + x2z4
Directional derivative
= V 2
2 4
j
k (x y z2 y4 x2 z4 )
= i
x
y
z
= (2 xy 4 2 xz 4 ) i (4 x 2 y 3 4 y 3 z 2 ) j (2 y 4 z 4 x 2 z 3 ) k
Directional derivative at (2, 0, 3) = (0 2 2 81) i (0 0) j (0 4 4 27) k
= 324 i 432 k 108 (3 i 4 k )
...(1)
Normal to x2 + y2 + z2 – 14 =
2
j
k ( x y 2 z 2 14)
= i
y
z
x
= (2 x i 2 y j 2 z k )
Normal vector at (3, 2, 1)
Unit normal vector =
= 6 i 4 j 2k
6 i 4 j 2k
36 16 4
...(2)
2(3 i 2 j k )
2 14
Directional derivative along the normal = 108(3 i 4 k ).
=
108 (9 4)
14
1404
14
3i 2 j k
14
3i 2 j k
14
[From (1), (2)]
.
Ans.
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Vectors
395
Example 30. Find the directional derivative of ( f) at the point (1, – 2, 1) in the direction
of the normal to the surface xy2z = 3x + z2, where f = 2x3y2z4. (U.P., I Semester, Dec 2008)
Solution. Here, we have
f = 2x3 y2 z4
3 2 4
f = i x j y k z (2 x y z ) = 6 x 2 y 2 z 4 i 4 x3 yz 4 j 8 x3 y 2 z 3 k
2 2 4
3 4
3 2 3
(f) = i x j y k z (6 x y z i 4 x yz j 8 x y z k )
= 12xy2z4 + 4x3z4 + 24x3y2z2
Directional derivative of ( f )
2 4
3 4
3 2 2
= i x j y k z (12 xy z 4 x z 24 x y z )
= (12 y 2 z 4 12 x 2 z 4 72 x 2 y 2 z 2 )i (24 xyz 4 48 x3 yz 2) j
+ (48xy2z3 + 16x3z3 + 48x3y2z) k
Directional derivative at (1, – 2, 1) = (48 + 12 + 288) i + (– 48 – 96) j + (192 + 16 + 192) k
= 348i – 144 j 400k
Normal to(xy2z – 3x – z2) = (xy2z – 3x – z2)
2
2
= i x j y k z ( xy z – 3x – z )
= ( y 2 z – 3)i (2 xyz ) j ( xy 2 – 2 z )k
Normal at(1, – 2, 1) = i – 4 j 2k
i – 4 j 2k
1
Unit Normal Vector =
=
(i – 4 j 2k )
1 16 4
21
Directional derivative in the direction of normal
1 (i – 4 j 2k )
= (348i – 144 j 400k )
21
1
1724
(348 576 800) =
=
Ans.
21
21
2
2
2
Example 31. If the directional derivative of = a x y + b y z + c z x at the point
x 1 y 3 z
,
(1, 1, 1) has maximum magnitude 15 in the direction parallel to the line
2
2
1
find the values of a, b and c.
(U.P. I semester, Winter 2001)
2
2
2
Solution. Given
= ax y+by z+cz x
j
k (a x2 y + b y2 z + c z2 x)
= i
x
y
z
2
2
2
= i (2a x y c z ) j (a x 2 b y z ) k (b y 2 c z x)
at the point (1, 1, 1) = i (2a c) j (a 2b) k (b 2c)
...(1)
We know that the maximum value of the directional derivative is in the direction of .
i.e. || = 15 (2a + c)2 + (2b + a)2 + (2c + b)2 = (15)2
But, the directional derivative is given to be maximum parallel to the line
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396
Vectors
x 1
y 3 z
i.e., parallel to the vector 2 i 2 j k .
=
2
2
1
On comparing the coefficients of (1) and (2)
2a c
2b a 2c b
=
2
2
1
2a + c = – 2b – a 3a + 2b + c = 0
and
2b + a = – 2(2c + b)
2b + a = – 4c – 2b
a + 4b + 4c = 0
Rewriting (3) and (4), we have
3a 2b c 0
a
b
c
= k (say)
4 11 10
a 4b 4 c 0
a = 4k, b = –11k and c = 10k.
Now, we have
(2a + c)2 + (2b + a)2 + (2c + b)2 = (15)2
(8k + 10k)2 + (–22k + 4k)2 + (20k – 11k)2 = (15)2
k = 5
9
20
55
50
a = , b=
and c =
9
9
9
...(2)
...(3)
...(4)
Ans.
Example 32. If r x i y j z k , show that :
r
1
(ii) grad 3 .
r
r
(i) grad r = r
r
(Nagpur University, Summer 2002)
Solution. (i) r = x i y j z k r = x 2 y 2 z 2 r2 = x2 + y2 + z2
r
r x
2r
= 2x
x
x r
y
r
r z
Similarly,
=
and
r
z r
y
r
r
r
j
k ri
j
k
grad r = r = i
y
z
x
y
z
x
x y z xi y j zk r
j k
Proved.
r
r
r
r
r
1
1
1
1
1 1
(ii) grad = i
j
k = i j k
x r
y r
z r
y
z r
r
r x
1 r
1 r
1 r
= i 2
k 2
j 2
x
y
r
r z
r
= i
1 x 1 y 1 z
xi y j zk
r
= i
Proved.
3
j 2 k 2 =
2
3
r r
r r
r r
r
r
2
2
Example 33. Prove that f (r ) f ´´ (r ) f ´ (r ) .
(K. University, Dec. 2008)
r
Solution.
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Vectors
397
j
k f (r )
f (r ) i
y
z
x
2
r
r x r y
2
2
2
2x
,
r x y z 2r
x
x r
y r
r
r
r
y
x
i f ´ (r )
j f ´(r )
k f ´(r )
f ´ (r ) i j k
x
y
z
r
r
xi y j zk
f ´ (r )
r
xi yj zk
j
k f ´(r )
2 f (r ) [ f (r )] i
y
z
r
x
x
y
z
f ´(r )
f ´ (r )
f ´(r )
x
r y
r z
r
r
r
r.1 y
r1 x
y
y
r x
x f ´´(r ) r
f ´´(r ) f ´(r )
r f ´(r )
2
x r
y
r
r2
and
r z
z r
z
r
z
r.1 z
r z
r
f ´´( r ) f ´( r )
z r
r2
x2
y2
z2
r
r
r
r f ´´(r ) y y f ´ (r )
r f ´´ (r ) z z f ´(r )
r
= f ´´(r ) x x f ´(r )
2
2
r
r
r
r
r r
r
r
r2
zz
r 2 z2
x x
r 2 x2
y
r 2 y2
f
´´
(
r
)
f
´
(
r
)
f
´´(
r
)
f
´(
r
)
= f ´´(r ) f ´( r )
r r
r
rr
r3
r3
r3
2
2
2
2
2
2
2
2
2
x
y z
y
x z
z
x y
f ´´(r ) 2 f ´(r )
f ´´(r ) 2 f ´(r )
f ´´(r ) 2 f ´(r )
3
3
r
r
r
r
r
r3
2
2
2
2
2
2
2
2
2
x
y z
y
z
z x
x y
= f ´´(r ) 2 2 2 f ´ (r )
3
3
r
r
r
r3
r
r
x2 y2 x2
2( x 2 y 2 z 2 )
r2
2r 2
f ´´ (r )
f
´(
r
)
f
´´(
r
)
f
´
(
r
)
=
r2
r3
r2
r3
2
= f ´´(r ) f ´(r )
Ans.
r
EXERCISE 5.7
1. Evaluate grad if = log (x2 + y2 + z2)
Ans.
2( x i y j z k )
x2 y 2 z 2
1 ˆ
2. Find a unit normal vector to the surface x2 + y2 + z2 = 5 at the point (0, 1, 2). Ans.
( j 2kˆ )
5
(AMIETE, June 2010)
3. Calculate the directional derivative of the function (x, y, z) = xy 2 + yz 3 at the point
5
(1, –1, 1) in the direction of (3, 1, –1) (A.M.I.E.T.E. Winter 2009, 2000) Ans.
11
4. Find the direction in which the directional derivative of f (x, y) = (x2 – y2)/xy at (1, 1) is zero.
(Nagpur Winter 2000)
Ans.
i j
2
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398
Vectors
5. Find the directional derivative of the scalar function of (x, y, z) = xyz in the direction of the outer
27
normal to the surface z = xy at the point (3, 1, 3).
Ans.
11
6. The temperature of the points in space is given by T(x, y, z) = x2 + y2 – z. A mosquito located at
(1, 1, 2) desires to fly in such a direction that it will get warm as soon as possible. In what direction
1
should it move?
Ans. 3 (2 i 2 j k )
7. If (x, y, z) = 3xz2y – y3z2, find grad at the point (1, –2, –1) Ans. (16 i 9 j 4 k )
8. Find a unit vector normal to the surface x2y + 2xz = 4 at the point (2, –2, 3).
1
( i 2 j 2 k )
3
9. What is the greatest rate of increase of the function u = xyz2 at the point (1, 0, 3)?
Ans. 9
10. If is the acute angle between the surfaces xyz2 = 3x + z2 and 3x2 – y2 + 2z = 1 at the point
Ans.
(1, –2, 1) show that cos = 3/7 6 .
11. Find the values of constants a, b, c so that the maximum value of the directional directive of
= axy2 + byz + cz2x3 at (1, 2, –1) has a maximum magnitude 64 in the direction parallel to the
axis of z.
Ans. a = b, b = 24, c = –8
12. Find the values of and µ so that surfaces x2 – µ y z = ( + 2)x and 4 x2 y + z3 = 4 intersect
9
, 1
2
13. The position vector of a particle at time t is R = cos (t – 1) i + sinh (t – 1) j + at2k. If at t = 1,
the acceleration of the particle be perpendicular to its position vector, then a is equal to
orthogonally at the point (1, –1, 2).
(a) 0
(b) 1
(c)
Ans. =
1
2
(d)
1
2
(AMIETE, Dec. 2009) Ans. (d)
5.29 DIVERGENCE OF A VECTOR FUNCTION
The divergence of a vector point function F is denoted by div F and is defined as below..
Let
F = F1 i F2 j F3 k
F1 F2 F3
div F = . F i x j y k z ( i F1 j F2 k F3 ) = x y z
It is evident that div F is scalar function.
5.30 PHYSICAL INTERPRETATION OF DIVERGENCE
Let us consider the case of a fluid flow. Consider a small rectangular parallelopiped of
dimensions dx, dy, dz parallel to x,y and z axes respectively.
Let V Vx i V y j Vz k be the velocity of the
fluid at P(x, y, z).
Mass of fluid flowing in through the face ABCD in unit time
= Velocity × Area of the face = Vx (dy dz )
Mass of fluid flowing out across the face PQRS per unit time
= Vx (x + dx) (dy dz)
O
Vx
dx ( dy dz )
= Vx
Y
x
Net decrease in mass of fluid in the parallelopiped
corresponding to the flow along x-axis per unit time
Z
C
S
D
Vx
A
R
dz
B
Q
P
X
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Vectors
399
Vx
dx dy dz
= Vx dy dz Vx
x
Vx
=
dx dy dz
x
(Minus sign shows decrease)
Similarly, the decrease in mass of fluid to the flow along y-axis =
and the decrease in mass of fluid to the flow along z-axis =
Vx
Total decrease of the amount of fluid per unit time =
x
Vx V y
Thus the rate of loss of fluid per unit volume =
x
y
Vy
y
dx dy dz
Vz
dx dy dz
z
V y Vz
dx dy dz
y
z
V
z
z
j
k .( i Vx jVy k Vz ) = .V div V
= i
y
z
x
If the fluid is compressible, there can be no gain or loss in the volume element. Hence
div V = 0
and V is called a Solenoidal vector function.
Equation (1) is also called the equation of continuity or conservation of mass.
Example 34. If v
xi y j zk
, find the value of div v .
x2 y 2 z 2
Solution. We have,
v =
...(1)
(U.P., I Semester, Winter 2000)
xi y j zk
x2 y 2 z2
x i y j zk
i
j
k
.
div v = . v =
y
z ( x 2 y 2 z 2 )1/ 2
x
x
y
z
=
2
2
2 1/ 2
2
2
2 1/ 2
2
2
x ( x y z )
y ( x y z )
z ( x y z 2 )1/ 2
1
1
( x 2 y 2 z 2 )1/ 2 x. ( x2 y 2 z 2 ) 2 .2 x
2
=
( x2 y 2 z 2 )
1
1
1
( x 2 y 2 z 2 ) 2 y. ( x 2 y 2 z 2 ) 2 2 y
2
(x2 y 2 z2 )
=
=
( x 2 y 2 z 2 ) x2
( x 2 y 2 z 2 )3/ 2
( x2 y 2 z 2 ) y 2
( x 2 y 2 z 2 )3/ 2
y 2 z 2 x2 z 2 x2 y 2
2
2
1 2
2
2
2 1/ 2
2
2 1/ 2
.2 z
( x y z ) z. 2 ( x y z )
( x2 y 2 z 2 )
=
2 3/ 2
(x y z )
(x2 y 2 z2 ) z2
( x 2 y 2 z 2 )3/ 2
2( x 2 y 2 z 2 )
2
2
2 3/ 2
(x y z )
2
(x2 y2 z2 )
Ans.
Example 35. If u = x2 + y2 + z2, and r x i y j z k , then find div (ur ) in terms of u.
(A.M.I.E.T.E., Summer 2004)
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400
Vectors
j
k .[( x 2 y 2 z 2 ) ( x i y j z k )]
div (u r ) = i
y
z
x
j
k .[( x 2 y 2 z 2 ) x i ( x 2 y 2 z 2 ) y j ( x 2 y 2 z 2 ) z k ]
= i
y
z
x
Solution.
3
( x xy 2 xz 2 ) ( x 2 y y 3 yz 2 ) ( x 2 z y 2 z z 3 )
x
y
z
= (3x2 + y2 + z2) + (x2 + 3y2 + z2) + (x2 + y2 + 3z2) = 5 (x2 + y2 + z2) = 5 u
=
Ans.
Example 36. Find the value of n for which the vector r n r is solenoidal, wheree
r xi y j zk.
n
2
2
2 n/2
F = . F .r r .( x y z ) ( x i y j z k )
Solution. Divergence
2
2
2 n/2
2
2
2 n/ 2
2
2
2 n/2
= i j k .[( x y z ) x i ( x y z ) y j ( x y z ) z k ]
y
z
x
n
n 2
= (x2 + y2 + z2)n/2 – 1 (2x2) + (x2 + y2 + z2)n/2 +
(x + y2 + z2)n/2 – 1 (2y2)
2
2
n 2
+ (x2 + y2 + z2)n/2 +
(x + y2 + z2)n/2 – 1 (2z2) + (x2 + y2 + z2)n/2
2
= n(x2 + y2 + z2)n/2 – 1 (x2 + y2 + z2) + 3 (x2 + y2 + z2)n/2
= n(x2 + y2 + z2)n/2 + 3(x2 + y2 + z2)n/2 = (n + 3) (x2 + y2 + z2)n/2
If r n r is solenoidal, then (n + 3) (x2 + y2 + z2)n/2 = 0 or n + 3 = 0 or n = –3.
Ans.
Example 37. Show that ( a . r ) a n ( a . r ) r .
(M.U. 2005)
r n r n
rn 2
Solution. We have,
a. r
rn
=
(a1 i a2 j a3 k ).( x i y j z k )
r
n
=
a1 x a2 y a3 z
rn
Let
=
=
x
a . r a1 x a2 y a3 z
rn
rn
n
r .a1 (a1 x a2 y a3 z ) n r n 1(r / x)
But r2 = x2 + y2 + z2
r 2n
r
2r
= 2x
x
r x
x r
a1r n (a1 x a2 y a3 z ).n r n 2 .x
n (a1 x a2 y a3 z ) x
a
=
= 1n
x
r 2n
r
rn 2
i
j
k
=
x
y
z
1
n
= n (a1 i a2 j a3 k ) n 2 [(a1 x a2 y a3 z ) ( x i y j z k )]
r
r
a
n
= n n 2 ( a .r ) r
r
r
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Vectors
401
Example 38. Let r x i y j z k , r | r | and a is a constant vector. Find the value of
ar
div n
r
a = a1 i a2 j a3 k
Solution. Let
ar
= a1
x
a2
y
i
ar
| r |n
= (a1 i a2 j a3 k ) ( x i y j z k )
j
k
a3 = (a2 z a3 y ) i (a1 z a3 x ) j (a1 y a2 x) k
z
(a2 z a3 y ) i (a1 z a3 x ) j (a1 y a2 x) k
=
( x2 y2 z 2 )n / 2
a r
ar
= .
div
n
| r |n
|r|
(a2 z a3 y ) i (a1 z a3 x ) j (a1 y a2 x) k
j
k .
= i
y
z
( x2 y 2 z 2 ) n / 2
x
a2 z a3 y
a1 z a3 x
( a1 y a2 x)
=
2
2
2 n/ 2
2
2
2 n/2
2
x ( x y z )
y ( x y z )
z ( x y 2 z 2 )n / 2
n (a2 z a3 y ) 2 x
n (a1 z a3 x) 2 y
n (a1 y a2 x ) 2 z
=
n2
n2
n2
2 2
2 2
2 2
(x y2 z2 ) 2
(x y2 z2 ) 2
(x y2 z2 ) 2
n
=
[(a2 z a3 y ) x (a1 z a3 x) y (a1 y a2 x ) z ]
n2
=
( x2 y 2 z 2 )
n
2
2
2
2
n2
) 2
[a2 zx a3 xy a1 yz a3 xy a1 yz a2 zx] = 0
Ans.
(x y z
Example 39. Find the directional derivative of div ( u ) at the point (1, 2, 2) in the direction
of the outer normal of the sphere x2 + y2 + z2 = 9 for u x 4 i y 4 j z 4 k .
Solution. div ( u ) = . u
4
j
k .( x i y 4 j z 4 k ) 4 x 3 4 y 3 4 z 3
= i
y
z
x
Outer normal of the sphere = (x2 + y2 + z2 – 9)
2
j
k ( x y 2 z 2 9) 2 x i 2 y j 2 z k
= i
y
z
x
Outer normal of the sphere at (1, 2, 2) = 2 i 4 j 4 k
...(1)
Directional derivative = (4 x3 4 y 3 4 z 3 )
j
k (4 x3 4 y 3 4 z 3 ) 12 x 2 i 12 y 2 j 12 z 2 k
= i
y
z
x
Directional derivative at (1, 2, 2) = 12 i 48 j 48 k
...(2)
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402
Vectors
Directional derivative along the outer normal = (12 i 48 j 48 k ).
24 192 192
= 68
6
n
n–2
Example 40. Show that div (grad r ) = n (n + 1)r
, where
2 i 4 j 4k
4 16 16
[From (1), (2)]
=
x2 y 2 z 2
r =
1
Hence, show that 2 = 0.
r
Solution.
(U.P. I Semester, Dec. 2004, Winter 2002)
n n n
r j
r k r by definition
x
y
z
r
r
r
r
n 1 r
n 1 r
j n r n 1
k .n r n 1
i
j
k
= i nr
= nr
x
y
z
y
z
x
x y z
= n r n 1 i j k nr n 2 ( x i y j z k ) nr n 2 r .
r
r
r
r
r x
2
2
2
2
r x y z 2r x 2 x x r etc.
grad (rn) = i
Thus, grad (rn) = n r n 2 x i n r n 2 y j n r n 2 z k
Ans.
...(1)
div grad rn = div [ n r n 2 x i n r n 2 y j n r n 2 z k ]
j
k .( nr n 2 x i nr n 2 y j nr n 2 z k )
= i
[From (1)]
y
z
x
( n r n 2 x)
(n r n 2 y ) (n r n 2 z )
=
(By definition)
x
y
z
r n 2
r
ny (n 2) r n 3
= n r n 2 nx (n 2) r n 3
n r
x
y
= 3n r n 2 n (n 2)r n 3
r
n r n 2 nz (n 2) r n 3
z
r
r
r
z
x y
y
z
x
x
y
z
n2
n ( n 2)r n 3 x y z
= 3n r
r
r
r
r
r x
2
2
2
2
r x y z 2r x 2 x x r etc.
= 3nrn – 2 + n (n – 2)rn – 4 [x2 + y2 + z2]
= 3nrn – 2 + n (n – 2) rn – 4.r2
( r2 = x2 + y2 + z2)
n–2
2
n–2
2
= r
[3n + n – 2n] = r
(n + n) = n(n + 1) rn – 2
If we put n = –1
div grad (r– 1) = –1 (–1 + 1) r– 1 – 2
1
2 = 0
r
r
Ques. If r x i y j z k , and r = |r| find div 2 . (U.P. I Sem., Dec. 2006)
r
Ans.
1
r2
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Vectors
403
EXERCISE 5.8
r
1. If r = x i y j z k and r = | r | , show that (i) div 3 = 0,
| r |
(ii) div (grad rn) = n (n + 1) rn – 2
(AMIETE, June 2010) (iii) div (r ) = 3 + r grad .
2. Show that the vector V = ( x 3 y ) i ( y 3 z ) j ( x 2 z ) k is solenoidal.
(R.G.P.V., Bhopal, Dec. 2003)
3. Show that .( A) = .A + (.A)
4. If , , z are cylindrical coordinates, show that grad (log ) and grad are solenoidal vectors.
5. Obtain the expression for 2f in spherical coordinates from their corresponding expression in
orthogonal curvilinear coordinates.
Prove the following:
6. .( F ) ( ). F ( . F )
(b) ( A R ) (2 n ) A n ( A . R ) R , r | R |
n
n
n2
r
r
r
8. div ( f g) – div (g f) = f 2g – g 2 f
7. (a) .() = 2
5.31
CURL
(U.P., I semester, Dec. 2006)
The curl of a vector point function F is defined as below
curl F = F
( F F1 i F2 j F3 k )
j
k ( F1 i F2 j F3 k )
= i
y
z
x
i
j
k
F
F F F F F
i 3 2 j 3 1 k 2 1
=
x y z
z
z
y
x
y
x
F1 F2 F3
Curl F is a vector quantity..
5.32 PHYSICAL MEANING OF CURL
(M.D.U., Dec. 2009, U.P. I Semester, Winter 2009, 2000)
We know that V r , where is the angular velocity, V is the linear velocity and r
is the position vector of a point on the rotating body.
1 i 2 j 3 k
r x i y j z k
Curl V = V
= ( r ) = [( 1 i 2 j 3 k ) ( x i y j z k )]
i
j
k
= 1 2 3
x
y
z
= [(2 z 3 y ) i (1 z 3 x ) j (1 y 2 x) k ]
j
k [( 2 z 3 y ) i ( 1 z 3 x ) j (1 y 2 x ) k ]
= i
y
z
x
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404
Vectors
j
k
x
y
z
i
=
2 z 3 y 3 x 1 z 1 y 2 x
= (1 2 ) i (2 2 ) j (3 3 ) k = 2(1 i 2 j 3 k ) 2
Curl V = 2 which shows that curl of a vector field is connected with rotational properties
of the vector field and justifies the name rotation used for curl.
If Curl F = 0, the field F is termed as irrotational.
Example 41. Find the divergence and curl of v ( x y z ) i (3 x 2 y ) j ( xz 2 y 2 z ) k at
(2, –1, 1)
(Nagpur University, Summer 2003)
Solution. Here, we have
2
2
2
v = ( x y z ) i (3 x y ) j ( xz y z ) k
Div.
v =
( x y z)
(3x 2 y )
( xz 2 y 2 z )
x
y
z
= yz + 3x2 + 2x z – y2
= –1 + 12 + 4 – 1 = 14 at (2, –1, 1)
Div v =
Curl v =
i
x
j
y
xyz 3 x 2 y
k
z
xz 2 y 2 z
Curl at (2, –1, 1)
= 2 yz i ( z 2 xy ) j (6 xy xz ) k
= 2 yz i ( xy z 2 ) j (6 xy xz ) k
= 2(1)(1) i {(2) (1) 1} j {6(2)(1) 2(1)} k
= 2 i 3 j 14 k
Ans.
Example 42. If V x i y j z k , find the value of curl V .
x2 y 2 z 2
(U.P., I Semester, Winter 2000)
Solution.
Curl V
= V
x i y j zk
j
k
= i
y
z ( x 2 y 2 z 2 )1/ 2
x
=
i
x
x
j
y
y
k
z
z
( x 2 y 2 z 2 )1/ 2
( x 2 y 2 z 2 )1/ 2
( x 2 y 2 z 2 )1/ 2
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Vectors
405
z
y
z
= i 2
2
j 2
2
2 1/ 2
2
2 1/ 2
2
2 1/ 2
y
z
x
(
x
y
z
)
(
x
y
z
)
(
x
y
z
)
x
y
x
k 2
2
2
2
2 1/ 2
2
2 1/ 2
2
2 1/ 2
z ( x y z )
x ( x y z ) y ( x y z )
yz
y.z
zx
zx
2
j 2
2
= i 2
2
2 3/ 2
2
2 3/ 2
2
2 3/ 2
2
2 3/ 2
(x y z )
(x y z )
(x y z )
(x y z )
xy
xy
k 2
2
0
2
2 3/ 2
2
2 3/ 2
(x y z )
(x y z )
Ans.
Example 43. Prove that ( y 2 – z 2 3 yz – 2 x ) i (3xz 2 xy ) j (3xy – 2 xz 2 z ) k is both
solenoidal and irrotational.
Solution. Let
(U.P., I Sem, Dec. 2008)
2
2
F = ( y – z 3 yz – 2 x ) i (3xz 2 xy ) j (3xy – 2 xz 2 z ) k
For solenoidal, we have to prove . F = 0.
2
k ( y – z 2 3 yz – 2 x) i (3xz 2 xy ) j (3xy – 2 xz 2 z ) k
Now, . F = i j
y
z
x
= – 2 + 2x – 2x + 2 = 0
Thus, F is solenoidal. For irrotational, we have to prove Curl F = 0.
j
k
x
y
z
i
Now,
Curl F =
y 2 – z 2 3 yz – 2 x 3xz 2 xy 3xy – 2 xz 2 z
= (3z 2 y – 2 y 3z ) i – (– 2 z 3 y – 3 y 2 z ) j
(3 z 2 y – 2 y – 3z ) k
= 0 i 0 j 0k = 0
Thus, F is irrotational.
Hence, F is both solenoidal and irrotational.
Proved.
Example 44. Determine the constants a and b such that the curl of vector
A = (2 xy 3 yz ) i ( x 2 axz – 4 z 2 ) j – (3 xy byz ) k is zero.
o.
(U.P. I Semester, Dec 2008)
k [(2 xy 3 yz ) i ( x 2 axz – 4 z 2 ) j
Solution.
Curl A = i j
y
z
x
=
(3xy byz ) k ]
i
x
j
y
k
z
2 xy 3 yz
x 2 axz – 4 z 2
– 3 xy – byz
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406
Vectors
= [– 3 x – bz – ax 8 z ] i – [– 3 y – 3 y ] j [2 x az – 2 x – 3 z ] k
= [– x(3 a ) z ( 8 – b )] i 6 y j z (– 3 a ) k
=0
i.e., 3 + a = 0 and 8 – b = 0,
–3+a=0
a = – 3, 3
b =8
a=3
Example 45. If a vector field is given by
(given)
Ans.
F ( x 2 – y 2 x) i – (2 xy y ) j . Is this field irrotational ? If so, find its scalar potential.
(U.P. I Semester, Dec 2009)
Solution. Here, we have
F = ( x 2 – y 2 x ) i – (2 xy y ) j
Curl F = F
j
k ( x 2 – y 2 x ) i – (2 xy y ) j
= i
y
z
x
j
k
x
y
= i (0 – 0) – j (0 – 0) k (– 2 y 2 y ) = 0
z
i
=
x 2 – y 2 x – 2 xy – y 0
Hence, vector field F is irrotational.
To find the scalar potential function
F =
d =
j
k
( i dx j dy k dz )
dx
dy
dz = i
x
y
z
x
y
z
= i x j y k z ( d r ) = d r = F d r
= [( x 2 – y 2 x )i – (2 xy y ) j ] ( i dx j dy k dz )
= (x2 – y2 + x)dx – (2xy + y)dy.
= [( x 2 – y 2 x ) dx – (2 xy y ) dy ] c
3
2
2
x 2 d x x dx y dy y 2 dx 2 xy dy c = x x – y – xy 2 c
3
2
2
3
2
2
x
x
y
Hence, the scalar potential is
Ans.
–
– xy 2 c
3
2
2
=
Example 46. Find the scalar potential function f for A y 2 i 2 xy j z 2 k .
(Gujarat, I Semester, Jan. 2009)
Solution. We have,
2
2
A = y i 2 xy j z k
j
k ( y 2 i 2 xy j z 2 k )
Curl A = A = i
y
z
x
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Vectors
407
i
=
x
j
y
y2
k
z
= i (0) j (0) k (2 y 2 y ) = 0
2 xy z 2
Hence, A is irrotational. To find the scalar potential function f.
A =f
f
f
f
f f f
dx
dy
dz
j
k .( i dx j dy k dz )
df =
= i
x
y
z
y
z
x
j
k f .dr = f .d r
= i
y
z
x
= A .dr
(A = f)
2
2
= ( y i 2 xy j z k ).( i dx j dy k dz )
2
= y dx + 2xy dy – z2 dz = d (xy2) – z2 dz
f=
= xy 2
2
2
d ( xy ) z dz
z3
C
3
Ans.
Example 47. A vector field is given by A = (x2 + xy2) i + (y2 + x2y) j . Show that the field
is irrotational and find the scalar potential.(Nagpur Univeristy, Summer 2003, Winter 2002)
Solution. A is irrotational if curl A = 0
i
j
x
y
x 2 xy 2
y2 x2 y
0
k
z
Curl A = A
= i (0 0) j (0 0) k (2 xy 2 xy ) 0
Hence, A is irrotational. If is the scalar potential, then
A = grad
dx
dy
dz
[Total differential coefficient]
x
y
z
j
k .( i dx j dy k dz ) = grad . dr
= i
y
z
x
d =
2
2
2
2
= A . dr = [( x xy ) i ( y x y ) j ].( i dx j dy k dz )
2
2
2
2
= (x + xy ) dx + (y + x y) dy = x2 dx + y2 dy + (x dx)y2 + (x2) (y dy)
=
2
2
2
2
x dx y dy [( x dx) y ( x ) ( y dy)] =
x3 y 3 x 2 y 2
c Ans.
3
3
2
Example 48. Show that V ( x, y, z ) 2 x y z i ( x 2 z 2 y ) j x 2 y k is irrotational and find a
scalar function u(x, y, z) such that V = grad (u).
Solution.
2
2
V (x, y, z) = 2 x y z i ( x z 2 y ) j x y k
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408
Vectors
Curl V
j
k [2 x y z i ( x 2 z 2 y ) j x 2 y k ]
= i
y
z
x
i
x
j
y
k
z
2x y z
x2 z 2 y
x2 y
=
2
2
= ( x x ) i (2 xy 2 xy ) j (2 xz 2 xz ) k 0
Hence, V (x, y, z) is irrotational.
To find corresponding scalar function u, consider the following relations given
V
= grad (u)
V = (u )
u
u
u
du =
dx
dy
dz
x
y
z
or
...(1)
(Total differential coefficient)
u u u
j
k
= i
.( i dx j dy k dz )
y
z
x
= u .d r V . d r
=
=
=
=
u =
Integrating, we get
[From (1)]
[2 x y z i ( x 2 z 2 y ) j x 2 y k ].( i dx j dy k dz )
2 x y z dx + (x2z + 2y) dy + x2y dz
y(2x z dx + x2 dz) + (x2z) dy + 2y dy
[yd (x2z) + (x2z) dy] + 2y dy = d(x2yz) + 2y dy
x2yz + y2
Ans.
Example 49. A fluid motion is given by v ( y z ) i ( z x ) j ( x y ) k . Show that the
motion is irrotational and hence find the velocity potential.
(Uttarakhand, I Semester 2006; U.P., I Semester, Winter 2003)
Solution.
Curl v = v
j
k [( y z ) i ( z x) j ( x y ) k ]
= i
y
z
x
=
i
x
yz
j
y
zx
k
= (1 1) i (1 1) j (1 1) k 0
z
x y
Hence, v is irrotational.
To find the corresponding velocity potential , consider the following relation.
v =
d =
dx
dy
dz
[Total Differential coefficient]
x
y
z
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Vectors
409
= i j k .( i dx j dy k dz ) = i j k . d r . d r = v . d r
y
z
y
z
x
x
= [( y z ) i ( z x ) j ( x y ) k ].( i dx j dy k dz )
= (y + z) dx + (z + x) dy + (x + y) dz
= y dx + z dx + z dy + x dy + x dz + y dz
( y dx x dy) ( z dy y dz ) ( z dx x dz )
=
= xy + yz + zx + c
Velocity potential = xy + yz + zx + c
Example 50. A fluid motion is given by
Ans.
2
v = (y sin z – sin x) i + (x sin z + 2yz) j + (xy cos z + y ) k
is the motion irrotational? If so, find the velocity potential.
Curl v = v
j
k (y sin z sin x) i + (x sin z + 2yz ) j + (xy cos z + y 2 ) k
= i
y
z
x
Solution.
j
k
x
y
z
i
=
xy cos z y 2
y sin z sin x x sin z 2 yz
= (x cos z + 2y – x cos z – 2y) i – [y cos z – y cos z] j + (sin z – sin z) k = 0
Hence, the motion is irrotational.
So, v = where is called velocity potential.
dx
dy
dz
[Total differential coefficient]
x
y
z
j
k .( i dx j dy k dz ) = .d r = v . d r
= i
y
z
x
= [(y sin z – sin x) i + (x sin z + 2yz) j + (xy cos z + y2) k ]. [ i dx j dy k dz ]
= (y sin z – sin x) dx + (x sin z + 2 y z) dy + (x y cos z + y2) dz
= (y sin z dx + x dy sin z + x y cos z dz) – sin x dx + (2 y z dy + y2 dz)
= d (x y sin z) + d (cos x) + d (y2 z)
d =
= d ( xy sin z ) d (cos x) d ( y 2 z )
= xy sin z + cos x + y2z + c
Hence,
Velocity potential = xy sin z + cos x + y2z + c.
Ans.
Example 51. Prove that F r 2 r is conservative and find the scalar potential such that
F = .
j
k
x
y
z
i
(Nagpur University, Summer 2004)
2
2
2
2
F = r r = r (x i y j z k) = r x i r y j r z k
Solution. Given
Consider F =
2
r 2 x r 2 y r2 z
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410
Vectors
2 2
2 2
2
2
= i r z r y j r z r x k r y r x
z
x
z
y
y
x
r
r
r
r
r
r
2ry j 2rz
2rx k 2ry 2rx
= i 2rz
y
z
x
z
x
y
x r y r z
2
2
2
2 r
But r x y z , x r , y r , z r
y
z
x
z
x
y
= i 2 rz 2ry j 2 rz 2rx k 2 ry 2 rx
r
r
r
r
r
r
= i (2 yz 2 yz ) j (2 zx 2 zx) k (2 xy 2 xy ) = 0 i 0 j 0 k 0
F = 0
F is irrotational
F is conservative.
Consider scalar potential such that F = .
d =
dx
dy
dz
x
y
z
[Total differential coefficient]
j
k .( i dx j dy k dz )
= i
y
z
x
j
k .( i dx j dy k dz ) = .( i dx j dy k dz )
= i
y
z
x
2
= r r .( i dx j dy k dz )
= F .( i dx j dy k dz )
( = F )
= ( x 2 y 2 z 2 ) ( i x j y k z ).( i dx j dy k dz )
= (x2 + y2 + z2) (x dx + y dy + z dz)
= x3 dx + y3 dy + z3 dz + (x dx) y2 + (x2) (y dy)
+ (x dx)z2 + z2 (y dy) + x2 (z dz) + y2 (z dz)
= x3 dx y 3 dy z 3 dz [( x dx ) y 2 ( y dy ) x 2 ]
[( x dx) z 2 ( z dz ) x 2 ] [( y dy ) z 2 ( z dz ) y 2 ]
x4 y 4 z 4 1 2 2 1 2 2 1 2 2
x y x z y z c
4
4
4 2
2
2
1 4
=
(x + y4 + z4 + 2x2y2 + 2x2z2 + 2y2 z2) + c
4
=
Ans.
r
Example 52. Show that the vector field F
3
is irrotational as well as solenoidal. Find
|r|
the scalar potential.
(Nagpur University, Summer 2008, 2001, U.P. I Semester Dec. 2005, 2001)
Solution.
F =
r
| r |3
xi y j zk
( x 2 y 2 z 2 )3/2
x i y j zk
j
k
Curl F = F = i
y
z ( x 2 y 2 z 2 )3/ 2
x
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Vectors
411
i
x
x
j
y
y
k
z
z
( x 2 y 2 z 2 )3/ 2
( x 2 y 2 z 2 )3/ 2
( x 2 y 2 z 2 )3/ 2
=
3
2 yz
3
2 yz
= i
2
2
2 5/2
2
2
2 5/2
2
2
(x y z )
(x y z )
3
2 xz
2 xz
3
j
2
2
2
2 5/2
2
2 5/2
2
2
(x y z )
(x y z )
3
2 xy
2 xy
3
k
2
2
2
2 5/2
2
2 5/2
2 (x y z )
2 (x y z )
= 0
Hence, F is irrotational.
F = , where is called scalar potential
dx
dy
dz
d =
[Total differential coefficient]
x
y
z
j
k .( i dx j dy k dz ) = .d r F .d r
= i
y
z
x
=
xi y j zk
2
2
2
.( i dx j dy k dz )
3/ 2
(x y z )
1 2 x dx 2 y dy 2 z dz
=
2
( x 2 y 2 z 2 )3/ 2
1
1 2 2
2
2
= (x y z ) 2
2 1
Now,
=
x dx y dy z dz
( x 2 y 2 z 2 )3/ 2
1
(x2 y2
1
z2 ) 2
1
|r |
Ans.
Div F = . F
x i y j z k
= i
j
k . 2
y
z ( x y 2 z 2 )3/ 2
x
x
y
z
=
x ( x 2 y 2 z 2 )3/ 2 y ( x 2 y 2 z 2 )3/ 2 z ( x 2 y 2 z 2 )3/ 2
3
( x 2 y 2 z 2 )3/ 2 (1) x ( x 2 y 2 z 2 )1/ 2 (2 x )
2
=
( x 2 y 2 z 2 )3
3
( x 2 y 2 z 2 )3/ 2 (1) y ( x 2 y 2 z 2 )1/ 2 (2 y )
2
2
( x y 2 z 2 )3
3
( x 2 y 2 z 2 )3/ 2 (1) z ( x 2 y 2 z 2 )1/ 2 (2 z )
2
2
( x y 2 z 2 )3
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412
Vectors
=
( x 2 y 2 z 2 )1/ 2
[x2 + y2 + z2 – 3x2 + x2 + y2 + z2 – 3y2 + x2 + y2 + z2 – 3z2]
( x 2 y 2 z 2 )3
= 0
Hence, F is solenoidal.
Proved.
Example 53. Given the vector field V ( x 2 y 2 2 xz ) i ( xz xy yz ) j ( z 2 x 2 ) k find
curl V. Show that the vectors given by curl V at P0 (1, 2, –3) and P1 (2, 3, 12) are orthogonal.
Curl V = V
j
k [( x 2 y 2 2 xz ) i ( xz xy yz ) j ( z 2 x 2 ) k ]
= i
y
z
x
Solution.
j
k
x
y
z
xz xy yz
z 2 x2
i
curl V =
x 2 y 2 2 xz
= ( x y ) i (2 x 2 x) j ( z y 2 y ) k = ( x y ) i ( y z ) k
curl V at P0 (1, 2, –3) = (1 2) i (2 3) k 3 i k
curl V at P1 (2, 3, 12) = (2 3) i (3 12) k 5 i 15 k
The curl V at (1, 2, –3) and (2, 3, 12) are perpendicular since
( 3 i k ).( 5 i 15 k ) = +15 – 15 = 0
Example 54. Find the constants a, b, c, so that
Proved.
F = ( x 2 y az ) i (bx 3 y z ) j (4 x cy 2 z ) k
...(1)
is irrotational and hence find function such that F = .
(Nagpur University, Summer 2005, Winter 2000; R.G.P.V., Bhopal 2009)
Solution. We have,
i
j
k
F =
x
y
z
( x 2 y az ) (bx 3 y z ) (4 x cy 2 z )
= (c 1) i (4 a ) j (b 2) k
As F is irrotational, F 0
i.e., (c 1) i (4 a ) j (b 2) k 0 i 0 j 0 k
c + 1 = 0,
4–a=0
and
i.e.,
a = 4,
b = 2,
c = –1
Putting the values of a, b, c in (1), we get
b–2=0
F = ( x 2 y 4 z ) i (2 x 3 y z ) j (4 x y 2 z ) k
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Vectors
413
Now we have to find such that F =
We know that
dx
dy
dz
d =
[Total differential coefficient]
x
y
z
j
k .( i dx j dy k dz )
= i
y
z
x
j
k .( i dx j dy k dz ) = .( i dx j dy k dz )
= i
y
z
x
= F .( i dx j dy k dz )
= [( x 2 y 4 z ) i (2 x 3 y z ) j (4 x y 2 z ) k )].( i dx j dy k dz )
= (x + 2y + 4z) dx + (2x – 3y – z) dy + (4x – y + 2z) dz
= x dx – 3y dy + 2z dz + (2y dx + 2x dy) + (4z dx + 4x dz) + (–z dy – y dz)
= x dx 3 y dy 2 z dz (2 y dx 2 x dy ) (4 z dx 4 x dz ) ( z dy y dz )
=
x2 3 y 2
+ z2 + 2xy + 4zx – yz + c
2
2
Ans.
Example 55. Let V (x, y, z) be a differentiable vector function and (x, y, z) be a scalar
function. Derive an expression for div ( V ) in terms of . V , div V and .
(U.P. I Semester, Winter 2003)
Solution. Let V = V1 i V2 j V3 k
div ( V ) = .( F )
j
k .[V1 i V2 j V3 k ] =
(V1 ) (V2 ) ( V3 )
= i
y
z
x
y
z
x
V1 V2 V3
V1
V2
V3
=
y z
z
x x y
V1 V2 V3
=
x V1 y V2 z V3
x
y
z
= i x j y k z .(V1 i V2 j V3 k ) i x j y k z .(V1 i V2 j V3 k )
= (.V ) ( ).V (div V ) (grad ). V
Ans.
Example 56. If A is a constant vector and R = x iˆ + y ĵ + z k̂ , then prove that
Curl A . R A A R
(K. University, Dec. 2009)
Solution. Let A = A1 iˆ + A2 ĵ + A3 k̂ ,
R = x iˆ + y ĵ + z k̂
A . R (A1 iˆ + A2 ĵ + A3 k̂ ) . (x iˆ + y ĵ + z k̂ ) = A1 x + A2 y + A3 z
[ A . R ] R = (A1 x + A2y + A3 z) (x iˆ + y ĵ + z k̂ )
= (A1 x2 + A2 xy + A3 zx) iˆ + (A1 xy + A2 y2 + A3 yz) ĵ + (A1 xz + A2 yz + A3z2) k̂
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414
Vectors
iˆ
Curl ( A . R ) R =
x
A1 x 2 A2 xy A3 zx
ˆj
y
kˆ
z
A2 xy A2 y 2 A3 yz
A1 xz A2 yz A3 z 2
= (A2 z – A3 y) iˆ – [A1 z – A3 x) ĵ [A1 y – A2 x] k̂
... (1)
L.H.S. = A R
= (A1 iˆ + A2
ˆj
iˆ
= A1 A 2
x
y
ĵ + A3 k) ×(x iˆ + y ĵ + z k̂ )
k
A3
z
= (A2 z – A3 y) iˆ – (A1 z – A3 x) ĵ + (A1 y – A2 x) k̂
= R.H.S.
[From (1)]
Example 57. Suppose that U , V and f are continuously differentiable fields then
Prove that, div (U V ) V .curl U U .curl V .
(M.U. 2003, 2005)
U = u1 i u2 j u3 k , V v1 i v2 j v3 k
Solution. Let
i
U V
=
j
k
u1 u2
v1 v2
u3
v3
= (u2v3 u3v2 ) i (u1v3 u3v1 ) j + (u1v2 u2v1 ) k
j k .[(u2 v3 u3v2 ) i (u1v3 u3v1 ) j + (u1v2 u2 v1 ) k ]
div (U V ) = i
y
z
x
= x (u2 v3 u3 v2 ) y (u1v3 u3 v1 ) z (u1v2 u2 v1 )
u
v
u
u
v
u
v
v3
v3 2 u3 2 v2 3 u1 3 v3 1 u3 1 v1 3
= u2
x
x
x
y
y
y
y
x
u1
v1
u
v2
u1
v2
u2
v1 2
z
z
z
z
u
u
u
u
u
u
3
2 v2 3 1 v3 2 1
= v1
y
z
x
z
x
y
v3 v2
v3 v1
v1 v2
u1
u 2 x z u3 y x
y
z
u
u
u
u
u
u
= (v1 i v2 j v3 k ) . i 3 2 j 1 3 k 2 1
y
z
z
x
x
y
v
v v
v v
v
(u1 i u2 j u3 k ). i 2 3 j 3 1 k 1 2
y
x
z
x
y
z
= V .( U ) U .( V ) V .curl U U .curl V
Proved.
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Vectors
415
Example 58. Prove that
Solution.
( F G ) = F ( . G ) G ( . F ) (G . ) F ( F . ) G
(M.U. 2004, 2005)
( F G) = i ( F G )
x
F
G
F
G
= i
G F
G i F
i
x
x
x
x
F F
G G
= ( i . G )
i
G i
F (i . F )
x x
x
x
F
F
G
G
= (G . i )
G i . F i .
( F . i )
x
x
x
x
G
F
F
G
( F . i )
= F i
G i.
(G . i )
x
x
x
x
= F ( G ) G ( . F ) (G . ) F ( F . ) G
Questions for practice:
Prove that
Proved.
( F . G ) = (G . ) F ( F . ) G G ( F ) F ( G )
Example 59. Prove that, for every field V ; div curl V = 0.
(Nagpur University, Summer 2004; AMIETE, Sem II, June 2010)
Solution. Let
V = V1 i V2 j V3 k
div (curl V ) = .( V )
i
= .
x
V1
j
k
y
V2
z
V3
V3 V2 V3 V1 V2 V1
j
k . i
= i
j
k
y
z y
z
z
y
x
x
x
V3 V2 V3 V1 V2 V1
=
x y
z y x
z z x
y
=
2 V3 2 V2 2 V3 2 V1 2 V2 2 V1
x y x z y x y z z x z y
2 V1 2 V1 2 V2 2 V2 2 V3 2 V3
=
y z z y z x x z x y y x
=0
Ans.
Example 60. If a is a constant vector, show that
a ( r ) = ( a . r ) ( a . ) r .
Solution.
(U.P., Ist Semester, Dec. 2007)
a = a1 i a2 j a3 k , r r1 i r2 j r3 k
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416
Vectors
j
k
x
y
z
r2
r3
i
r =
r1
j
k
a1
a2
a3
i
a ( r ) =
r3 r2 r3 r1 r2 r1
i
=
j x y k
z
x z
y
r3 r2
y z
r3 r1
x z
r2 r1
x y
r
r
r
r r
r
r
r
= a2 2 a2 1 a3 3 a3 1 i a1 2 a1 1 a3 3 a3 2 j
y
x
z x
y
y
z
x
r
r
r
r
a1 3 a1 1 a2 3 a2 2 k
x
z
y
z
r1
r2
r3
r1
r2
r3
= a1 i x a2 i x a3 i x a1 j y a2 j y a3 j y
r
r
r
r
r
r
a1 k 1 a2 k 2 a3 k 3 a1 i 1 a1 j 2 a1 k 3
z
z
z
x
x
x
r
r
r
r
r
r
a2 i 1 a2 j 2 a2 k 3 a3 i 1 a3 j 2 a3 k 3
y
y
y
z
z
z
= i j k (a1r1 a2 r2 a3 r3 ) a1 a2 a3 ( r1 i r2 j r3 k )
y
z
y
z
x
x
= ( a . r ) ( a .) r
Proved.
Example 61. If r is the distance of a point (x, y, z) from the origin, prove that
1
1
Curl k grad grad k .grad = 0, where k is the unit vector in the direction OZ.
r
r
(U.P., I Semester, Winter 2000)
2
2
2
Solution.
r = (x – 0) + (y – 0) + (z – 0)2 = x2 + y2 + z2
1
= (x2 + y2 + z2)– 1/2
r
1
1
2
j
k ( x y 2 z 2 )1/ 2
grad
= = i
r
r
x
y
z
1
= ( x 2 y 2 z 2 )3/ 2 (2 x i 2 y j 2 z k )
2
= – ( x 2 y 2 z 2 )3/ 2 ( x i y j z k )
k × grad
1
= k [( x 2 y 2 z 2 ) 3/ 2 ( x i y j z k )]
r
= ( x 2 y 2 z 2 )3/ 2 ( x j y i )
1
1
curl k grad = k grad
r
r
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Vectors
417
j
k × [–(x2 + y2 + z2)–3/2 ( x j y i ) ]
= i
y
z
x
i
x
y
j
y
x
( x 2 y 2 z 2 )3/ 2
( x 2 y 2 z 2 )3/ 2
=
k
z
0
3
( x) (2 z )
3
y (2 z )
( x)(2 x )
3
= 2
i
j
2
2 5/2
2
2
2 5/2
2
2
2 5/ 2
2 (x y z )
2 (x y z )
2 (x y z )
1
(3 / 2) ( y ) (2 y )
1
2
2
2
k
2
2 3/ 2
2
2 5/ 2
2
2 3/ 2
(x y z )
(x y z )
(x y z )
=
3xz
3 yz
(3x 2 x 2 y 2 z 2 3 y 2 x 2 y 2 z 2 )
i
j
k
( x 2 y 2 z 2 )5 / 2
( x 2 y 2 z 2 )5 / 2
( x 2 y 2 z 2 )5 / 2
=
3xz i 3 yz j ( x 2 y 2 2 z 2 ) k
( x 2 y 2 z 2 )5 / 2
1
z
k . grad
= k .[ ( x 2 y 2 z 2 )3/ 2 ( x i y j z k )] 2
r
( x y 2 z 2 )3/ 2
z
1
j
k 2
grad k .grad = i
y
z ( x y 2 z 2 )3/ 2
r
x
...(1)
3
i ( z )(2 x)
3
j ( z )(2 y )
=
2
2
2
5
/
2
2
2 (x y z )
2 ( x y 2 z 2 )5 / 2
( z )(2 z )
1
3
2
k
2
2
2 5/2
2
2 3/ 2
(x y z )
2 (x y z )
2
2
2
2
2
2
2
= 3xz i 3 yz j (3z x y z ) k 3xz i 3 yz j ( x y 2 z ) k
...(2)
2
2
2 5/ 2
2
2
2 5/2
(x y z )
(x y z )
Adding (1) and (2), we get
1
1
Curl k grad grad k .grad = 0
Proved.
r
r
a r (2 n ) a n ( a . r ) r
Example 62. Prove that
.
r n
rn
rn 2
(M.U. 2009, 2005, 2003, 2002; AMIETE, II Sem. June 2010)
Solution. We have,
ar
rn
=
=
1
rn
1
r
n
j
k
a1
x
a2
y
a3
z
i
(a2 z a3 y ) i
1
r
n
(a3 x a1 z ) j
1
r
n
(a1 y a2 x) k
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418
Vectors
i
(a r)
=
x
n
r
a2 z a3 y
j
y
a3 x a1 z
k
z
a1 y a2 x
rn
rn
rn
a y a x
a3 x a1 z a1 y a2 x a2 z a3 y
= i 1 n 2
j x
r
rn
rn
rn
z
z
y
a xa z
a
z
a
y
k 3 n 1 2 n 3
r
r
y
x
r
r x
Now,
r2 = x2 + y2 + z2 2r
= 2x
x
x r
y
r
r z
,
Similarly,
=
r
y
z r
1
ar
n 1 y
. (a1 y a2 x) n a1
n = i nr
r
r
r
1
z
nr n 1 (a3 x a1 z ) n (a1 ) + two similar terms
r
r
a
a
n
n
2
2
= i n 2 (a1 y a2 xy ) n1 n 2 (a3 xz a1 z ) n1
r
r
r
r
+ two similar terms
2a
n
n
2
2
1
= i n n 2 a1 ( y z ) n 2 (a2 xy a3 xz ) + two similar terms
r
r
r
n
2
Adding and subtracting n 2 a1 x to third and from second term, we get
r
a r
2a
na1
n
n = i 1
( x 2 y 2 z 2 ) n 2 (a1 x 2 a2 xy a3 xz )
n
n2
r
r
r
r
+ two similar terms
2a
na1 2
n
1
= i n n 2 r n 2 x (a1 x a2 y a3 z ) + two similar terms
r
r
r
2a
2a
na
n
na
n
j n2 n2 n 2 y (a2 y a3 z a1 x)
= i n1 n1 n 2 x(a1 x a2 y a3 z )
r
r
r
r
r
r
2a
na
n
k n3 n3 n 2 z ( a3 z a1 x a2 y )
r
r
r
n
2
n
= n (a1 i a2 j a3 k ) n (a1 i a2 j a3 k ) n 2 (a1 x a2 y a3 z ) ( x i y j z k )
r
r
r
2n
n
(a1 i a2 j a3 k ) n 2 (a1 x a2 y a3 z ) ( x i y j z k )
=
n
r
r
2n
n
a n 2 (a . r ) r
=
Proved.
rn
r
Example 63. If f and g are two scalar point functions, prove that
div (f g) = f 2g + f g. (U.P., I Semester, compartment, Winter 2001)
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Vectors
419
g g g
i
j
k
x
y
z
g
g
g
f g = f
i f
j f
k
x
y
z
g g g
(f g) =
f
f
f
x x y y z z
2 g 2 g 2 g f g f g f g
f 2 2 2
y
z x x y y z z
x
2
f f f g g g
2
2
f 2 2 2 g
i
j
k .
i
j
k
y
z x
y
z
y
z
x
2x
f g + f.g
Proved.
Solution. We have,
div
=
=
=
g =
Example 64. For a solenoidol vector F , show that curl curl curl curl F = 4 F .
(M.D.U., Dec. 2009)
Solution.
Since vector F is solenoidal, so div F = 0
... (1)
We know that curl curl F = grad div ( F – 2 F )
... (2)
Using (1) in (2), grad div F = grad (0) = 0
On putting the value of grad div F in (2), we get
... (3)
curl curl F = – 2 F
... (4)
Now, curl curl curl curl F = curl curl (– 2 F )
[Using (4)]
= – curl curl ( 2 F ) = – [grad div ( 2 F ) – 2 ( 2 F ) ]
[Using (2)]
[ . F
= 0]
= – grad ( . 2 F ) + 2 ( 2 F ) = – grad ( 2 . F ) + 4 F
= 0 + 4 F = 4 F [Using (1)]
EXERCISE 5.9
Proved.
1. Find the divergence and curl of the vector field V = (x2 – y2) i + 2xy j + (y2 – xy) k .
Ans. Divergence = 4x, Curl = (2y – x) i + y j + 4y k
2. If a is constant vector and r is the radius vector, prove that
(ii) div ( r a ) 0
(i) ( a . r ) a
(iii) curl ( r a ) 2 a
where r = x i y j z k and a a1 i a2 j a3 k .
3. Prove that:
(i) .(A) = .A + (.A)
(ii) (A.B) = (A.)B + (B.)A + A × ( × B) + B × ( × A)
(iii) × (A × B) = (B.)A – B(.A) – (A.)B + A(.B)
(R.G.P.V. Bhopal, June 2004)
4. If F = (x + y + 1) i + j – (x + y) k , show that F.curl F = 0.
(R.G.P.V. Bhopal, Feb. 2006, June 2004)
Prove that
5. ( F ) ( ) F ( F )
7. Evaluate div ( A r ) if curl A = 0.
6. .( F G ) G .( F ) F .( G )
8. Prove that curl ( a r ) = 2a
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420
Vectors
9. Find div F and curl F where F = grad (x3 + y3 + z3 – 3xyz).
(R.G.P.V. Bhopal Dec. 2003)
Ans. div F = 6(x + y + z), curl F = 0
= (x + y + az) i + (bx + 3y – z) j + (3x + cy + z) k
10. Find out values of a, b, c for which v
is irrotational.
Ans. a = 3, b = 1, c = –1
11. Determine the constants a, b, c, so that F = (x + 2y + az) i + (bx – 3y – z) j + (4x + cy + 2z) k is
irrotational. Hence find the scalar potential such that F = grad .
(R.G.P.V. Bhopal, Feb. 2005) Ans. a = 4, b = 2, c = 1
x2 3 y 2
z 2 2 xy yz 4 zx
Potential =
2
2
Choose the correct alternative:
12. The magnitude of the vector drawn in a direction perpendicular to the surface
x2 + 2y2 + z2 = 7 at the point (1, –1, 2) is
2
3
(i)
(ii)
(iii) 3
(iv) 6
(A.M.I.E.T.E., Summer 2000) Ans. (iv)
3
2
13.If u = x2 – y2 + z2 and V xi y j zk then (uV ) is equal to
(i) 5u
(ii) 5 | V |
(iii) 5(u | V |) (iv) 5(u | V |)
14.A unit normal to x2 + y2 + z2 = 5 at (0, 1, 2) is equal to
1
1
1
( i j k ) (ii)
( j 2 k ) (iv)
(i)
( i j k ) (iii)
5
5
5
(A.M.I.E.T.E., June 2007)
1
( i j k)
5
(A.M.I.E.T.E., Dec. 2008)
15.The directional derivative of = x y z at the point (1, 1, 1) in the direction i is:
1
1
(i) –1
(ii)
(iii) 1
(iv)
Ans. (iii)
3
3
(R.G.P.V. Bhopal, II Sem., June 2007)
16.If r x i y j z k and r = | r | then (r) is:
(i) (r) r
( r ) r
r
(ii)
(iii)
( r ) r
r
(iv) None of these
(R.G.P.V. Bhopal, II Semester, Feb. 2006)
17. If r = x i y j z k is position vector, then value of (log r) is
r
(i)
r
r
2
(iii) –
r
(iv) none of the above.
r3
(iv) –2
Ans. (ii)
(R.G.P.V. Bhopal, II Semester, Feb. 2006)
19. If V xy 2 i 2 yx 2 z j 3 yz 2 k then curl V at point (1, –1, 1) is
(i) ( j 2 k )
20. If A is such that A = 0 then A is called
(i) Irrotational
(ii) Solenoidal
(iii) Rotational
(iii) ( i 2 k )
(iv) ( i 2 j k )
(R.G.P.V. Bhopal, II Semester, Feb 2006)
Ans. (iii)
(ii) ( i 3 k )
Ans. (ii)
18. If r x i y j z k and | r | = r, then div r is:
(i) 2
(ii) 3
(iii) –3
(U.P., I Sem, Dec 2008)
r
(ii)
Ans. (iii)
(iv) None of these
(A.M.I.E.T.E., Dec. 2008)
21. If F is a conservative force field, then the value of curl F is
(i) 0
(ii) 1
(iii) F
(iv) –1
(A.M.I.E.T.E., June 2007)
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421
22.If 2 [(1 – x) (1 – 2x)] is equal to
(i) 2
(ii) 3
(iii) 4
(iv) 6
(A.M.I.E.T.E., Dec. 2009) Ans. (iii)
23.If R = xi + yj + zk and A is a constant vector, curl ( A R ) is equal to
(ii) 2 R
(i) R
(iii) A
(iv) 2 A (A.M.I.E.T.E., Dec. 2009) Ans. (iv)
1
24. If r is the distance of a point (x, y, z) from the origin, the value of the expression ˆj grad
2
equals
(i) ( x 2 y 2 z 2 )
3
2
( ˆj z kˆ x )
(ii) ( x 2 y 2 z 2 )
(iii) zero
(iv)
(x2 y2
3
2
3
z2 ) 2
( ˆj z iˆ z )
( ˆj y kˆ x )
(AMIETE, Dec. 2010) Ans. (ii)
5.33 LINE INTEGRAL
Let
F ( x, y , z)
be a vector function and a curve AB.
Line integral of a vector function F along the curve AB is defined as integral of the component
of F along the tangent to the curve AB.
Component of F along a tangent PT at P
= Dot product of F and unit vector along PT
= F dr dr is a unit vector along tangent PT
ds ds
dr
Line integral = F
from A to B along the curve
ds
F dr ds
Line integral = c
= c F d r
ds
Note (1) Work. If F represents the variable force acting on a particle along arc AB, then the
total work done =
B
A F dr
(2) Circulation. If
V
represents the velocity of a liquid then
c V dr
is called the circulation
of V round the closed curve c.
If the circulation of V round every closed curve is zero then V is said to be irrotational there.
(3) When the path of integration is a closed curve then notation of integration is in place
of
.
Example 65. If a force F 2 x 2 yiˆ 3 xyjˆ displaces a particle in the xy-plane from (0, 0) to
(1, 4) along a curve y = 4 x2. Find the work done.
Solution. Work done =
=
=
c F . dr
c (2 x y iˆ 3 xy ˆj) . (dx iˆ dy ˆj)
2
c (2 x y dx 3 xy dy)
2
ˆ
ˆ
r xi yj
dr dxiˆ dy
ˆj
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Vectors
y 4 x2
dy 8 x dx
Putting the values of y and dy, we get
1
0 [2 x
=
2
(4 x 2 ) dx 3 x (4 x 2 ) 8 x dx]
1
x5
1 4
104
= 104 0 x dx 104
5
5
0
Ans.
Example 66. Evaluate F . dr where F x 2iˆ xyjˆ and C is the boundary of the square in the
C
plane z = 0 and bounded by the lines x = 0, y = 0, x = a and y = a.
(Nagpur University, Summer 2001)
Solution.
C F . d r OA F . dr AB F . dr BC F . dr CO F . dr
Here
r xiˆ yjˆ, d r dxiˆ dyjˆ , F x 2iˆ xy ˆj
Y
F . dr = x2dx + xydy
On OA, y = 0
...(1)
F . dr x dx
OA F . dr
On AB, x = a
(1) becomes
=
a 2
x dx
0
a
x3
a3
3 0 3
...(2)
dx = 0
O
F . dr = aydy
A
On BC, y = a
X
a
y2
a3
aydy a
F
.
dr
=
0
Ab
2
2 0
dy = 0
a
B
C
2
...(3)
(1) becomes
F . dr x 2 dx
BC F . dr
=
0 2
x dx
a
0
x3
– a3
3
3 a
...(4)
On CO, x = 0,
(1) becomes
F . dr 0
CO F . dr
=0
...(5)
On adding (2), (3), (4) and (5), we get
C F . dr
Example 67. A vector field is given by
F = (2 y 3) iˆ xzjˆ ( yz – x) kˆ. Evaluate
y = t, z = t3 from t = 0 to t = 1.
Solution.
C F . dr
=
=
a3 a3 a3
a3
–
0
3
2
3
2
Ans.
C F . dr
along the path c is x = 2t,
(Nagpur University, Winter 2003)
C (2 y 3) dx ( xz) dy ( yz – x) dz
Since x 2t
dx 2
dt
yt
z t3
dy
dz
1
3t 2
dt
dt
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423
=
1
0 (2t 3) (2 dt ) (2t ) (t
3
) dt (t 4 – 2t ) (3t 2 dt ) =
1
0 (4t 6 2t
4
3t 6 – 6t 3 ) dt
1
1
t2
2 5 3 7 6 4
2 5 3 7 3 4
2
= 4 2 6t 5 t 7 t – 4 t = 2t 6t t t – t
5
7
2 0
0
2 3 3
– = 7.32857.
5 7 2
Example 68. The acceleration of a particle at time t is given by
= 26
Ans.
a = 18 cos 3t iˆ 8 sin 2t ˆj 6t kˆ.
If the velocity v and displacement r be zero at t = 0, find v and r at any point t.
d2 r
Solution. Here, a =
= 18 cos 3t iˆ 8 sin 2t ˆj 6t kˆ.
dt 2
On integrating, we have
= dr
iˆ 18 cos 3t dt ˆj 8 sin 2t dt kˆ 6t dt
v
dt
v = 6 sin 3t iˆ 4 cos 2t ˆj 3t 2 kˆ c
At
...(1)
t = 0, v = 0
Putting t = 0 and v = 0 in (1), we get
0 = 4 ˆj c c 4 ˆj
dr
6 sin 3t iˆ 4(cos 2t 1) ˆj 3t 2 kˆ
v =
dt
Again integrating, we have
= iˆ 6 sin 3t dt ˆj 4 (cos 2t 1) dt kˆ 3t 2 dt
r
r = 2 cos 3t iˆ (2 sin 2t 4t ) ˆj t 3 kˆ c1
...(2)
t = 0, r = 0
At,
Putting t = 0 and r = 0 in (2), we get
0 = 2iˆ C1
C1 2iˆ
3
r = 2 (1 cos 3t ) iˆ 2 (sin 2t 2t ) ˆj t kˆ
Hence,
Ans.
Example 69. If A (3x 2 6 y ) iˆ – 14 yzjˆ 20 xz 2 kˆ, evaluate the line integral
om
A. dr from
(0, 0, 0) to (1, 1, 1) along the curve C.
x = t, y = t2, z = t3.
(Uttarakhand, I Semester, Dec. 2006)
Solution. We have,
C A . dr
=
C [ (3x
2
6 y ) iˆ – 14 yzjˆ 20 xz 2 kˆ ] . [iˆ dx ˆj dy kˆ dz ]
=
C [ (3x
2
6 y ) dx – 14 yzdy 20 xz 2 dz ]
If x = t, y = t2, z = t3, then points (0, 0, 0) and (1, 1, 1) correspond to t = 0 and t = 1 respectively.
Now,
C A . dr =
=
t 1
t 0 [ (3 t
t 1
t 0 [ 9t
2
2
6 t 2 ) d (t ) – 14 t 2 t 3 d (t 2 ) 20 t (t 3 ) 2 d (t 3 )]
dt – 14 t 5 . 2 t d t 20 t 7 . 3 t 2 dt ] =
1
0 (9 t
2
– 28 t 6 60 t 9 ) dt
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Vectors
t3
t7
= 9 – 28
3
7
Example 70. Evaluate
1
t10
60
10 0
=3–4+6=5
S A . nˆ ds where A ( x y
2
Ans.
) iˆ – 2 xjˆ 2 yzkˆ and S is the surface of
the plane 2x + y + 2z = 6 in the first octant.
(Nagpur University, Summer 2000)
Solution. A vector normal to the surface “S” is given by
ˆ
ˆj
kˆ
(2 x y 2 z ) = iˆ
(2 x y 2 z ) 2iˆ ˆj 2k
x
y
z
And n̂ = a unit vector normal to surface S
Z
N
2 iˆ ˆj 2 kˆ 2 ˆ 1 ˆ 2 ˆ
i j k
=
3
3
3
4 1 4
1
2
2
kˆ . nˆ = kˆ . iˆ ˆj kˆ
3
3
3
dx dy
S A . nˆ ds = R A . nˆ kˆ . n
Where R is the projection of S.
1
2
2
2
Now, A . nˆ = [ ( x y ) iˆ – 2 xjˆ 2 yzkˆ] . iˆ ˆj
3
3
3
2
2
4
2 2 4
2
= ( x y ) – x yz y yz
3
3
3
3
3
Putting the value of z in (1), we get
–
n
K
2
3
M
O
3
R
Y
L
X
kˆ
...(1)
on the plane 2 x y 2 z 6,
2
4
6
2
x
y
2
A . nˆ = y y
z (6 2 x y )
3
3
2
2
2
4
...(2)
A . nˆ = y ( y 6 – 2 x – y ) y (3 – x)
3
3
M
dx dy
...(3)
S A . nˆ ds = R A . n | kˆ . n |
Hence,
+
2x
=
3y
6 – 2x
y2
2
(3
–
x
)
= 0
2 0
3
=
6
Putting the value of A . nˆ from (2) in (3), we get
3
6 2 x
4
3
S A . nˆ ds = R 3 y (3 – x) . 2 dx dy 0 0 2 y (3 x) dydx
3
0 (3 – x) (6 – 2 x)
2
O
dx
X
L
3
dx 4 (3 – x)3 dx
0
3
4
= 4. (3 – x ) – (0 – 81) 81
4 (– 1) 0
Example 71. Compute
c
F . dr , where F
ˆ ˆjx
iy
x2 y 2
Ans.
and c is the circle x2 + y2 = 1 traversed
counter clockwise.
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425
r = iˆ x ˆj y kˆ z, d r iˆ dx ˆj dy kˆ dz
ˆ ˆjx
iy
ˆ )
ˆ ˆjdy kdz
c F . d r = c x 2 y 2 (idx
ydx xdy
( ydx xdy )
= c 2
...(1) [ x2 + y2 = 1]
c
x y2
Parametric equation of the circle are x = cos , y = sin .
Putting x = cos , y = sin , dx = – sin d , dy = cos d in (1), we get
Solution.
C F d r
=
2
0
sin ( sin d ) cos (cos d )
2
2
2
2
2
= 0 (sin cos ) d 0 d = 0 2
Ans.
2
3
2
2 2
Example 72. Show that the vector field F 2 x( y z )iˆ 2 x yjˆ 3 x z kˆ is conservative.
Find its scalar potential and the work done in moving a particle from (–1, 2, 1) to (2, 3, 4).
(A.M.I.E.T.E. June 2010, 2009)
Solution. Here, we have
F 2 x( y 2 z 3 ) iˆ 2 x 2 y ˆj 3x 2 z 2 kˆ
Curl F F
iˆ
x
ˆj
y
kˆ
z
2 x( y 2 z3 )
2 x2 y
3x2 z 2
(0 0)i (6 xz 2 6 xz 2 ) ˆj (4 xy 4 xy )kˆ = 0
Hence, vector field F is irrotational.
To find the scalar potential function
F
ˆ
ˆ
d
dx dy dz iˆ ˆj kˆ . idx
ˆjdy kdz
x
y
z
y
z
x
iˆ ˆj kˆ . d r .d r F . d r
y
z
x
2
3
ˆ )
ˆ ˆjdy kdz
2 x ( y z )iˆ 2 x 2 yjˆ 3x2 z 2 kˆ (idx
2 x ( y 2 z 3 ) dx 2 x 2 y dy 3 x 2 z 2 dz
=
2 x( y
(2 xy
2
2
z 3 )dx 2 x 2 ydy 3x 2 z 2 dz C
dx 2 x 2 ydy ) (2 xz 3dx 3 x 2 z 2 dz ) + C = x2y2 + x2z3 + C
Hence, the scalar potential is x2y2 + x2z3 + C
Now, for conservative field
Work done =
(2, 3, 4)
F. d r
( 1, 2,1)
(2, 3, 4)
d
(2,3,4)
(1,2,1)
(2,3,4)
x2 y 2 x2 z3 c
( 1,2,1)
( 1, 2,1)
= (36 + 256) – (2 – 1) = 291
Ans.
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426
Vectors
Example 73. A vector field is given by F (sin y ) iˆ x (1 cos y ) ˆj. Evaluate the line integral
over a circular path x2 + y2 = a2, z = 0.
Solution. We have,
`(Nagpur University, Winter 2001)
Work done
=
=
C F . d r
C F . dr
C [ (sin y ) iˆ x (1 cos y ) ˆj ] . [dxiˆ dyjˆ]
( z = 0 hence dz = 0)
=
C sin y dx x (1 cos y) dy C (sin y dx x cos y dy x dy)
=
C d ( x sin y) C x dy
(where d is differential operator).
The parametric equations of given path
x2 + y2 = a2 are x = a cos , y = a sin ,
Where varies form 0 to 2
C F . d r
2
=
0
=
0
2
d [ a cos sin ( a sin ) ]
d [ a cos sin ( a sin ) ]
2 1
0
2
0
2 2
a
0
2
2
= [a cos sin (a sin ) ] 0
= 0 a2
2
0
a cos . a cos d
a 2 cos 2 . d
cos 2 d
2
cos 2
a
sin 2
d
2
2
2 0
2
a
. 2 a 2
=
Ans.
2
Example 74. Determine whether the line integral
2
2 2
2
(2 x y z ) dx ( x z z cos y z ) dy (2 x yz y cos yz) dz is independent of the path of
integration ? If so, then evaluate it from (1, 0, 1) to 0, , 1 .
2
2
2 2
2
(2
xy
z
)
dx
(
x
z
z
cos
y
z
)
dy
(2
x
yz y cos yz ) dz
Solution. c
2
2 2
2
ˆ ˆ ˆjdy kdz
ˆ )
= c [(2 xy z iˆ) ( x z z cos y z ) ˆj (2 x yz y cos yz ) k ].(idx
= c F dr
This integral is independent of path of integration if
F = F 0
iˆ
F =
x
2 x yz 2
ˆj
y
kˆ
z
x 2 z 2 z cos y z 2 x 2 y z y cos y z
= (2x2z + cos yz – yz sin yz – 2x2z – cos yz + yz sin yz) = iˆ – (4 xyz – 4 x yz ) ˆj (2 xz 2 – 2 xz 2 ) kˆ
=0
Hence, the line integral is independent of path.
dx
dy
dz
d =
(Total differentiation)
x
y
z
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427
ˆ ˆ ˆ
ˆ
j
k
(idx ˆjdy kdz ) = dr F d r
= iˆ
y
z
x
2 ˆ
2 2
ˆ )
ˆ ˆjdy kdz
= [(2 xyz ) i ( x z z cos y z ) ˆj (2 x 2 yz y cos yz ) kˆ]. (idx
= 2xyz2 dx + (x2z2 + z cos y z) dy + (2x2yz + y cos yz) dz
= [(2x dx) yz2 + x2 (dy) z2 + x2y (2z dz)] + [(cos yz dy) z + (cos yz dz) y]
= d (x2yz2) + d (sin yz)
=
BA
d (x
2
yz 2 ) d (sin yz ) x 2 yz 2 sin yz
= (B) – (A)
[ x 2 yz 2 sin yz ](1, 0,1) = 0 sin ( 1) [0 0]
2
=1
Ans.
Example 75. Evaluate A . nˆ d S , where A 18 ziˆ – 12 ˆj 3 y kˆ and S is the part of the
S
plane 2x + 3y + 6z = 12 included in the first octant. (Uttarakhand, I semester, Dec. 2006)
Solution.
Here, A = 18 ziˆ – 12 ˆj 3 ykˆ
Given surface f (x, y, z) = 2x + 3y + 6z – 12
ˆj
kˆ
(2 x 3 y 6 z – 12) = 2 iˆ 3 ˆj 6 kˆ
Normal vector = f = iˆ
x
y
z
n̂ = unit normal vector at any point (x, y, z) of 2x + 3y + 6z = 12
2 iˆ 3 ˆj 6 kˆ 1 ˆ
(2 i 3 ˆj 6 kˆ)
=
7
4 9 36
2 2
= [ x yz sin yz ]
(0, ,1)
2
dx dy
dx dy
dxdy 7
dx dy
6
6
nˆ . kˆ 1 (2 iˆ 3 ˆj 6 kˆ) . kˆ
7
7
1
7
Now, A . nˆ dS = (18 z iˆ – 12 ˆj 3 y kˆ) . (2 iˆ 3 ˆj 6 kˆ) dx dy
7
6
dx dy
= (36 z – 36 18 y )
= (6 z – 6 3 y ) dx dy
6
Putting the value of 6z = 12 – 2x – 3y, we get
Y
dS =
6
=
0
=
6
=
0
6
0
6
1
(12 – 2 x )
3
(12
0
1
(12 – 2 x )
3
(6
0
– 2 x – 3 y – 6 3 y ) dx dy
2x
+
– 2 x ) dx dy
1
(12 – 2 x )
3
dy
0
1
(12 – 2 x )
( y)3
0
3y
=
12
(6 – 2 x) dx
=
0
=
0 (6 – 2 x) 3 (12 – 2 x) dx
6
B
(6 – 2 x ) dx
1
=
O
A
X
1 6
(4 x 2 – 36 x 72) dx
3 0
6
1 4 x3
– 18 x 2 72 x = 1 [4 36 2 – 18 36 72 6] = 72 [4 – 9 6] 24 Ans.
3 3
3
0 3
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Vectors
EXERCISE 5.10
1.
2.
3.
Find the work done by a force yiˆ xjˆ which displaces a particle from origin to a point (iˆ ˆj ). Ans. 1
Find the work done when a force F ( x2 – y 2 x) iˆ (2 xy y) ˆj moves a particle from origin to
2
(1, 1) along a parabola y2 = x.
Ans.
3
Show that V (2 xy z 3 ) iˆ x 2 ˆj 3 xz 2 kˆ is a conservative field. Find its scalar potential such that
V = grad . Find the work done by the force V in moving a particle from (1, – 2, 1) to (3, 1, 4).
2
3
Ans. x y + xz , 202
4.
Show that the line integral
c
(2 xy 3) dx ( x 2 4 z ) dy 4 y dz
where c is any path joining (0, 0, 0) to (1, – 1, 3) does not depend on the path c and evaluate the line
integral.
Ans. 14
5.
6.
7.
x2 y 2
1 , z = 0, under the field of
25 16
force given by F = (2x – y + z) iˆ + (x + y – z2) ĵ + (3x – 2y + 4z) kˆ. Is the field of force conservative?
(A.M.I.E.T.E., Winter 2000) Ans. 40
z4
x y 2 x2 y z3
If = (y2 – 2xyz3) iˆ + (3 + 2xy – x2z3) ĵ + (z3 – 3x2yz2) kˆ, find . Ans. 3 y
4
Find the work done in moving a particle once round the ellipse
R . dR
C
is independent of the path joining any two point if it is.
(i) irrotational field
(ii) solenoidal field
(iii) rotational field
(A.M.I.E.T.E., June 2010)
Ans. (i)
(iv) vector field.
5.34 SURFACE INTEGRAL
A surface r = f(u, v) is called smooth if f (u, v) posses continous
first order partial derivative.
Let F be a vector function and S be the given surface.
Surface integral of a vector function F over the surface S is defined
as the integral of the components of F along the normal to the
surface.
Component of F along the normal
= F . n̂ , where n is the unit normal vector to an element ds and
grad f
dx dy
ds =
n̂ = | grad f |
( nˆ kˆ )
Surface integral of F over S
= F nˆ
Note. (1) Flux =
S ( F nˆ ) ds
S ( F nˆ ) ds
S ( F nˆ ) d s where, F represents the velocity of a liquid.
If
=
= 0, then F is said to be a solenoidal vector point function.
Example 76. Evaluate
S ( yziˆ zxjˆ xykˆ) ds
where S is the surface of the spheree
x2 + y2 + z2 = a2 in the first octant.
Solution. Here, = x2 + y2 + z2 – a2
(U.P., I Semester, Dec. 2004)
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Vectors
429
ˆ ˆ
j
k
Vector normal to the surface = = iˆ
x
y
z
ˆj
kˆ ( x 2 y 2 z 2 a 2 ) 2 xiˆ 2 yjˆ 2 zkˆ
= iˆ
x
y
z
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
n̂ = 2 x i 2 y j 2 z k xi yj zk
| |
4 x2 4 y 2 4 z 2
x2 y 2 z 2
xiˆ yjˆ zkˆ
=
[ x2 + y2 + z2 = a2]
a
ˆ
ˆ
ˆ
Here,
F = yz i zx j xy k
ˆ
ˆ
ˆ
ˆ xi yj zk 3xyz
ˆ
ˆ
F nˆ = ( yz i zx j xy k )
a
a
2
2
a
a x 3 xyz dx dy
dx dy
Now, S F nˆ ds = S ( F nˆ ) ˆ
0
0
z
| k . nˆ |
a
a
a 2 x2
y2
xy dy dx 3 x
dx
= 3
0 0
0
2 0
a
3 a
3 a 2 x2 x4
3 a 4 a 4 3a 4
2
2
x
(
a
x
)
dx
.
= 0
2
2 2
4 0 2 2
4
8
a
a 2 x2
3
Example 77. Show that
a
S F nˆ ds 2 ,
where F = 4 xz iˆ – y2 ĵ + yz k̂
and S is the surface of the cube bounded by the planes,
x= 0, x = 1, y = 0, y = 1, z = 0, z = 1.
S.No. Surface Outward
normal
Solution. S F nˆ ds = OABC F nˆ ds
1
OABC
–k
2
DEFG
k
F nˆ ds
F nˆ ds
DEFG
OAGF
3
OAGF
–j
4
BCED
j
F nˆ ds
F nˆ ds
BCED
ABDG
5
ABDG
i
6
OCEF
–
i
F nˆ ds
...(1)
OCEF
Now,
Ans.
ds
dx dy
dx dy
dx dz
dx dz
dy dz
dy dz
z=
z=
y=
y=
x=
x=
0
1
0
1
1
0
1 1
OABC F n ds = OABC (4 xziˆ y 2 ˆj yz kˆ) ( k ) dx dy = 0 0 yz dx dy 0 (as z = 0)
DEFG (4 xziˆ y
2
ˆj yz kˆ ) kˆ dx dy
=
1 1
DEFG yz dx dy 0 0 y (1) dx dy
OAGF
1
y2
1
1 1
= 0 dx [ x ]0
2
2
2
0
2
(4 xz iˆ y 2 ˆj yz kˆ) ( j ) dx dz = OAGF y dx dz 0
1
(as y = 0)
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430
Vectors
BCED (4 xz iˆ y
ˆj yz kˆ) ˆj dx dz =
2
1
BCED ( y
2
) dx dz
1
1
1
= 0 dx 0 dz ( x )0 ( z ) 0 1
ABDG (4 xziˆ y
2
ˆj yzkˆ) iˆ dy dz =
= 4
OCEF (4 xz iˆ y
2
( y )10
z2
2
1
(as y = 1)
1 1
4 xz dy dz 0 0 4 (1) z dy dz
1
4 (1) 2
2
0
ˆj yz kˆ ) ( iˆ ) dy dz =
1 1
0 0 4 xz dy dz 0
(as x = 0)
On putting these values in (1), we get
1
3
S F nˆ ds = 0 2 0 1 2 0 = 2
EXERCISE 5.11
1.
2.
Proved.
where A
= ( x y 2 ) iˆ 2 xjˆ 2 yzkˆ and S is the surface of the plane
2x + y + 2z = 6 in the first octant.
Ans. 81
Evaluate
Evaluate
S A . nˆ ds,
S A . nˆ ds,
where A = ziˆ xjˆ 3 y 2 zkˆ and S is the surface of the cylinder x2 + y2 = 16
included in the first octant between z = 0 and z = 5.
Ans. 90
2
3.
2
If r = tiˆ t ˆj (t 1) kˆ and S = 2t 2iˆ 6tkˆ, evaluate
4.
Evaluate
S F nˆ dS ,
0 r S dt.
Ans. 12
where, F = 18 z iˆ 12 ˆj 3 ykˆ and S is the surface of the plane 2x + 3y + 6z = 12
in the first octant.
5.
Evaluate
Ans. 24
S F nˆ ds, where, F =
2 yx iˆ yzjˆ x 2 kˆ over the surface S of the cube bounded by the
coordinate planes and planes x = a, y = a and z = a.
6.
Ans.
1 4
a
2
If F 2 yiˆ 3 ˆj x 2 kˆ and S is the surface of the parabolic cylinder y2 = 8x in the first octant bounded
by the planes y = 4, and z = 6, then evaluate
S F nˆ dS.
Ans. 132
5.35 VOLUME INTEGRAL
Let F be a vector point function and volume V enclosed by a closed surface.
The volume integral = V
F dv
Example 78. If F = 2 z iˆ – x ĵ + y k̂ , evaluate
V F dv
where, v is the region bounded by
the surfaces
x = 0, y = 0, x = 2, y = 4, z = x2, z = 2.
(2 z iˆ xjˆ ykˆ ) dx dy dz
Solution.
F dv =
V
=
=
2
4
2
4
2
0 dx 0 dy x2 (2 ziˆ xjˆ ykˆ) dz
0 dx 0 dy [4 iˆ 2 xjˆ 2 ykˆ x
=
2
4
0 dx 0 dy [ z
2
2
iˆ xzjˆ yzkˆ ] 2
x
i x3 ˆj x 2 ykˆ ]
4ˆ
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Vectors
431
4
x2 y 2 ˆ
2ˆ
4
3
k
= 0 dx 4 yiˆ 2 xyjˆ y k x yiˆ x yjˆ
2
0
2
=
2
0
(16 iˆ 8 xjˆ 16 kˆ 4 x 4 iˆ 4 x3 ˆj 8 x 2 kˆ ) dx
2
4 x5 ˆ
8 x3
i x 4 ˆj
= 16 xiˆ 4 x 2 ˆj 16 xkˆ
5
3
kˆ
0
128 ˆ
64 ˆ
32 ˆ
32 iˆ 32 kˆ
i 16 ˆj
k =
(3 i 5 kˆ)
= 32 iˆ 16 ˆj 32 kˆ
=
5
3
15
5
3
EXERCISE 5.12
1.
If F
= (2 x2 3z ) iˆ 2 xy ˆj 4 x kˆ, then evaluate
V F dV ,
Ans.
where V is bounded by the plane
Ans. 8
3
x = 0, y = 0, z = 0 and 2x + 2y + z = 4.
2.
3.
Evaluate V dV , where = 45 x2y and V is the closed region bounded by the planes
4x + 2y + z = 8, x = 0, y = 0, z = 0
If F = (2x2 – 3z) iˆ 2 xy ˆj 4 xkˆ, then evaluate
V F dV ,
where V is the closed region bounded
by the planes x = 0, y = 0, z = 0 and 2x + 2y + z = 4.
4.
Evaluate
V (2 x y) dV ,
Ans.
8 ˆ ˆ
( j k)
3
where V is closed region bounded by the cylinder z = 4 – x2 and the planes
x = 0, y = 0, y = 2 and z = 0.
5.
Ans. 128
Ans.
80
3
2
If F = 2 xz iˆ xjˆ y kˆ, evaluate F d V over the region bounded by the surfaces x = 0, y = 0,
y = 6 and z = x2, z = 4.
Ans. (16iˆ 3 ˆj 48 kˆ )
5.36 GREEN’S THEOREM (For a plane)
Statement. If (x, y), (x, y),
and
be continuous functions over a region R bounded
y
x
by simple closed curve C in x – y plane, then
C ( dx dy) = R x y dx dy (AMIETE, June 2010, U.P., I Semester, Dec. 2007)
Proof. Let the curve C be divided into two curves C1 (ABC) and C1 (CDA).
Let the equation of the curve C1 (ABC) be y = y1 (x) and equation of the curve C2 (CDA) be
y = y2 (x).
Let us see the value of
x c y y2 ( x )
c
y y ( x)
R y dx dy = x a y y1 ( x) y dy dx = a ( x, y ) y y12 ( x ) dx
=
c
a ( x, y2 ) ( x, y1 ) dx
a
a
c
= c ( x, y2 ) dx a ( x, y1 ) dx
c
= ( x, y2 ) dx ( x, y1 ) dx
a
c
= ( x, y) dx ( x, y ) dx = –
c1
c2
c ( x, y) dx
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432
Vectors
c dx
Thus,
=
dx dy
y
R
...(1)
Similarly, it can be shown that
c dy
=
x
dx dy
...(2)
On adding (1) and (2), we get
( dx dy) = R x y dx dy Proved.
Note. Green’s Theorem in vector form
c F d r
= R (
F ) kˆ d R
where, F iˆ ˆj , r xiˆ yjˆ, kˆ is a unit vector along z-axis and dR = dx dy.
Example 79. A vector field F is given by F sin yiˆ x (1 cos y ) ˆj.
Evaluate the line integral
C F dr
where C is the circular path given by x2 + y2 = a2.
Solution. F sin yiˆ x (1 cos y ) ˆj
ˆ ˆjdy ) =
= C [sin yiˆ x (1 cos y ) ˆj ] (idx
On applying Green’s Theorem, we have
C F dr
c ( dx dy)
dx dy
y
=
s x
=
s [(1 cos y) cos y] dx dy
C sin y dx x (1 cos y) dy
where s is the circular plane surface of radius a.
=
s dx dy
= Area of circle = a2. Ans.
Example 80. Using Green’s Theorem, evaluate
c (x
2
ydx x 2 dy ), where c is the boundaryy
described counter clockwise of the triangle with vertices (0, 0), (1, 0), (1, 1).
(U.P., I Semester, Winter 2003)
Solution. By Green’s Theorem, we have
Y
A
(1, 1)
(
dx
dy
)
dx
dy
= R
c
x
y
x
=
c (x
=
1
2
y dx x 2 dy ) =
0 (2 x x
2
) dx
x
0 dy
=
R (2 x x
1
0 (2 x x
2
2
y
) dx dy
) dx [ y ]0x
1
2
= 0 (2 x x ) ( x ) dx =
O (0, 0)
2 x3 x 4
2
3
0 (2 x x ) dx = 3 4
1
5
2 1
= =
12
3
4
Example 81. State and verify Green’s Theorem in the plane for
X
(1, 0)
1
0
Ans.
(3x
2
– 8 y 2 ) dx (4 y – 6 xy )
dy where C is the boundary of the region bounded by x 0, y 0 and 2x – 3y = 6.
(Uttarakhand, I Semester, Dec. 2006)
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Vectors
433
Solution. Statement: See Article 24.4 on page 576.
Here the closed curve C consists of straight lines OB, BA and AO, where coordinates of A and
B are (3, 0) and (0, – 2) respectively. Let R be the region bounded by C.
Then by Green’s Theorem in plane, we have
[ (3x
2
– 8 y 2 ) dx (4 y – 6 xy ) dy ]
– 8 y 2 ) dx dy
10 y dx dy
=
R x (4 y – 6 xy) – y (3x
=
R (– 6 y 16 y) dx dy R
3
0
1
(2 x – 6)
3
= 10 dx
0
2
0
y2
5
y dy 10 dx
= –
0
9
2 1 (2 x – 6)
3
...(1)
3
0 dx (2 x – 6)
2
3
3
5
5 (2 x – 6)3
5
(216) – 20 ...(2)
(0 6)3
= –
–
54
9 3 2 0
54
Now we evaluate L.H.S. of (1) along OB, BA and AO.
Along OB, x = 0, dx = 0 and y varies form 0 to –2.
1
3
Along BA, x = (6 3 y ), dx dy and y varies from –2 to 0.
2
2
and along AO, y = 0, dy = 0 and x varies from 3 to 0.
= –
L.H.S. of (1) =
=
OB [ (3x
[ (3x
2
2
– 8 y 2 ) dx (4 y – 6 xy ) dy ]
– 8 y 2 ) dx (4 y – 6 xy ) dy ]
2
2
BA [ (3x – 8 y ) dx (4 x – 6 xy) dy ]
[ (3 x 2 – 8 y 2 ) dx (4 y – 6 xy ) dy ]
AO
0
3
(6 3 y )2 – 8 y 2 dy [4 y – 3 (6 3 y ) y ] dy 3 x 2 dx
3
2
0
9
= [ 2 y 2 ]0– 2
(6 3 y )2 – 12 y 2 4 y – 18 y – 9 y 2 dy ( x 3 ) 03
–2
8
0 9
2
2
= 2 [4] (6 3 y ) – 21y – 14 y dy (0 – 27)
–2 8
0
9 (6 3 y )3
216
3
2
3
2
= 8 8 3 3 – 7 y – 7 y – 27 – 19 8 7 (– 2) 7 (– 2)
–2
=
–2
0
4 y dy
0
3
–2
4
= – 19 + 27 – 56 + 28 = – 20
...(3)
With the help of (2) and (3), we find that (1) is true and so Green’s Theorem is verified.
Example 82. Apply Green’s Theorem to evaluate
C [(2 x
2
y 2 ) dx ( x 2 y 2 ) dy ], where C
is the boundary of the area enclosed by the x-axis and the upper half of circle x2 + y2 = a2.
(M.D.U. Dec. 2009, U.P., I Sem., Dec. 2004)
2
2
2
2
[(2
x
y
)
dx
(
x
y
)
dy
]
Solution.
C
By Green’s Theorem, we’ve
( dx dy)
C
=
x y dx dy
S
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434
Vectors
=
=
a
a 2 x2
a
a 2 x2
a 0
a 0
(x2 y2 )
(2 x 2 y 2 ) dx dy
y
x
(2 x 2 y ) dx dy = 2
y2
= 2 a dx xy
2
0
a
a
2
a 2 x2
a
dx
a 2 x2
0
( x y ) dy
a
a 2 x2
2
2
dx
= 2 a x a x
2
a
2
a
2
a f ( x ) dx 2 a f ( x) dx, f is even
0
a
0,
f
is
odd
2
= 2 a x a x dx a ( a x ) dx
a
2
3 a3
a
x3
4a3
2
2
2
a
x
2
0
2
(
a
x
)
dx
a
=
=
=
Ans.
0
3 0
3
3
y
x
Example 83. Evaluate 2
dx 2
dy, where C C1 U C2 with C1 : x2 + y2 = 1
C x y2
x y2
and C2 : x = 2, y = 2.
(Gujarat, I Semester, Jan 2009)
y
x
Solution. 2
dx 2
dy
2
Y
C
x y
x y2
y=2
=
x
x
2
x y
2
y
dx dy
2
y x y 2
X
( x y ) 1 – 2 x ( x) ( x 2 y 2 ) 1 – 2 y ( y )
dx
dy
=
x=–2
( x 2 y 2 )2
( x 2 y 2 )2
x2 y 2 – 2 x 2 x 2 y 2 – 2 y 2
dx dy
=
2
2 2
( x 2 y 2 )2
(x y )
y 2 – x2
0
x2 – y2
dx dy 0
2
dx dy = 2
= 2
2 2
2 2
(
x
y 2 )2
(x y )
(x y )
5.37 AREA OF THE PLANE REGION BY GREEN’S THEOREM
Proof. We know that
N M
Mdx Ndy = A x – y dx dy
C
2
On putting
2
x 2+ y 2=1
X
x=2
O
y=–2
Y
Ans.
...(1)
N
M
N=x
1 and M = – y
1 in (1), we get
x
y
– y dx x dy = A [1 – (–1) ] dx dy = 2 dx dy = 2 A
C
Area =
1
( x dy – y dx)
2 C
Example 84. Using Green’s theorem, find the area of the region in the first quadrant bounded
by the curves
1
x
y = x, y = , y =
(U.P. I, Semester, Dec. 2008)
x
4
Solution. By Green’s Theorem Area A of the region bounded by a closed curve C is given by
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Vectors
435
A =
1
2
Y
C ( xdy – ydx)
)
y=
x
1
1,
x
1
B(
Here, C consists of the curves C1 : y = , C2 : y =
4
x
1—
and C3 : y = x So
y= x
1
1
1
1 )
C2
A (2, —
( I1 I 2 I3 )
2
A 2
C
C
C
C
1
2
3
2
2
C3 C1
x
1
x
Along
C1 : y = , dy dx, x : 0 to 2
y=—
4
4
4
x
1
I1 = C ( xdy – ydx) C x dx – dx 0
X
1
1
4
4
O (0, 0)
1
1
Along
C2 : y = , dy – 2 dx, x : 2 to 1
x
x
1
1
1
1
I2 = C2 ( xdy – ydx) 2 x – 2 dx – dx = [– 2log x ]2 2 log 2
2
x
Along
C3 : y = x, dy = dx ; x : 1 to 0 ;
I3 = ( xdy – ydx ) ( xdx – xdx ) 0
C3
A =
1.
Evaluate
c [(3x
2
1
1
( I1 I 2 I 3 ) (0 2 log 2 + 0) log 2
2
2
EXERCISE 5.13
Ans.
6 yz ) dx (2 y 3 xz ) dy (1 4 xyz 2 ) dz ] from (0, 0, 0) to (1, 1, 1) along the path c
given by the straight line from (0, 0, 0) to (0, 0, 1) then to (0, 1, 1) and then to (1, 1, 1).
2.
C (x
Verify Green’s Theorem in plane for
2
2 xy ) dx ( y 2 x 3 y ) dy , where c is a square with the
Ans.
vertices P (0, 0), Q (1, 0), R (1, 1) and S (0, 1).
3.
Verify Green’s Theorem for
y2 = 8 x and x = 2.
c ( x
2
1
2
2 xy ) dx ( x 2 y 3) dy around the boundary c of the region
c [(2 x
2
y 2 ) dx ( x 2 y 2 ) dy ] ,
4.
Use Green’s Theorem in a plane to evaluate the integral
5.
where c is the boundary in the xy-plane of the area enclosed by the x-axis and the semi-circle x2 + y2 =1
4
in the upper half xy-plane.
Ans.
3
[(
y
sin
x
)
dy
cos
x
dy
],
Apply Green’s Theoem to evaluate
where c is the plane triangle enclosed
6.
2x
and
y
.
by the lines y = 0, x =
2
Either directly or by Green’s Therorem, evaluate the line integral
c
Ans.
c e
x
where c is the rectangle with vertices (0, 0), (, 0,), , and 0, .
2
2
7.
Verify the Green’s Theorem to evaluate the line integral
of the closed region bounded by y = x and y = x2.
c (2 y
2
2 8
4
(cos y dx sin y dy ),
Ans. 2 (1 – e– )
(AMIETE II Sem June 2010)
dx 3 x dy ), where c is the boundary
(U.P., I Semester, Dec. 20005, AMIETE Summer 2004, Winter 2001) Ans.
27
4
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436
8.
Vectors
Evaluate
s F . nˆ.ds, where
F x y iˆ x 2 ˆj ( x z ) kˆ and s is the region of the plane 2x + 2y + z = 6
in the first octant.
9.
(A.M.I.E.T.E., Summer 2004, Winter 2001) Ans.
Verify Green’s Theorem for
C
27
4
( xy y 2 ) dx x 2 dy where C is the boundary by y = x and y = x2.
(AMIETE, June 2010)
5.38 STOKE’S THEOREM (Relation between Line Integral and Surface Integral)
(Uttarakhand, I Sem. 2008, U.P., Ist Semester, Dec. 2006)
Statement. Surface integral of the component of curl F along the normal to the surface S,
taken over the surface S bounded by curve C is equal to the line integral of the vector point function
F taken along the closed curve C.
Mathematically
F .d r
=
S
curl F nˆ ds
where n̂ = cos iˆ + cos ĵ + cos k̂ is a unit
external normal to any surface ds,
Proof. Let
r = xiˆ yjˆ zkˆ
d r = iˆ dx ˆj dy kˆ dz
ˆ
F = F1 iˆ F2 ˆj F3 k
On putting the values of F , d r in the statement of the theorem
c ( F1 iˆ F2 ˆj F3 kˆ) (iˆ dx ˆj dy kˆ dz)
k
ˆ
ˆ
ˆ
ˆ
ˆ
ˆ
y
z ( F1 i F2 j F 3 k ). (cos i cos j cos k ) ds
F3 F2
F2 F1
F1 F3
( F1 dx F2 dy F3 dz ) = S y z iˆ z x ˆj x y kˆ .
(iˆ cos ˆj cos kˆ cos ) ds
=
S i x
j
F3 F2
F2 F1
F1 F3
cos
cos x y cos ds
...(1)
y
z
z
x
Let us first prove
F1
F1
...(2)
c F1 dx = S z cos y cos ds
Let the equation of the surface S be z = g (x, y). The projection of the surface on x – y plane
is region R.
F1 ( x, y, z ) dx = F1 [ x, y, g ( x, y)] dx
=
S
c
c
F1 ( x, y , g ) dx dy
[By Green’s Theorem]
y
F1 F1 g
= R
...(3)
dx dy
z y
y
The direction consines of the normal to the surface z = g(x, y) are given by
cos
cos cos
=
g
g
1
x
y
=
R
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Vectors
437
And dx dy = projection of ds on the xy-plane = ds cos
Putting the values of ds in R.H.S. of (2)
F1
dx dy
F1
cos
y
cos
F1 g F1
F1 cos F1
dx dy
dx dy = R
= R
z cos y
z y y
F1 F1 g
dx dy
= R
z y
y
From (3) and (4), we get
S z
cos
F1
cos ds =
y
F1
R z
...(4)
F1
cos ds
y
F
F
Similarly,
c F2 dy = S x2 cos z2 cos ds
F
F
and
c F3 dz = S y3 cos x3 cos ds
On adding (5), (6) and (7), we get
c F1 dx
c ( F1 dx F2 dy F3 dz )
=
=
F1
cos
S z
F1
S z
...(5)
cos
cos
...(6)
...(7)
F1
F
F
cos 2 cos 2 cos
y
x
z
F3
F
cos 3 cos ds Proved.
y
x
5.39 ANOTHER METHOD OF PROVING STOKE’S THEOREM
The circulation of vector F around a closed curve C is equal to the flux of
the curve of the vector through the surface S bounded by the curve C.
= S curl F n d s S curl F d S
Proof : The projection of any curved surface over xy-plane can be treated as kernal of the
surface integral over actual surface
c F d r
S ( F ) kˆ d S
Now,
=
S ( F ) ( i
[ kˆ iˆ ˆj ]
j ) dx dy
=
S [( iˆ) ( F ˆj ) ( ˆj ) ( F iˆ)] dx dy = S x ( Fy ) y ( Fx ) dx dy
S [ Fx dx Fy dy] [By Green’s theorem]
=
S [iˆ Fx
=
ˆj Fy ] (iˆ dx ˆj dy ) =
S curl F nˆ dS
=
c F . dr
c F . d r.
where, F = Fx iˆ Fy ˆj Fz kˆ and dr dx iˆ dy ˆj dz kˆ
Example 85. Evaluate by Strokes theorem
x2 + y2 = 1, z = y2.
( yz dx zx dy xy dz ) where C is the curve
C
(M.D.U., Dec 2009)
Solution. Here we have
yz dx zx dy xy dz
ˆ ˆjdy kdz )
= ( yziˆ zxjˆ xykˆ ). (idx
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438
Vectors
=
iˆ
Curl F =
x
yz
F .dx
kˆ
z
xy
ˆj
y
zx
= (x – x) iˆ + (y – y) ĵ + (z – z) k̂
=0
=0
Example 86. Using Stoke’s theorem or otherwise, evaluate
2
2
[(2 x y ) dx yz dy y z dz ]
=
curl F . ds
Ans.
c
where c is the circle x2 + y2 = 1, corresponding to the surface of sphere of unit radius.
(U.P., I Semester, Winter 2001)
2
2
[(2
x
y
)
dx
yz
dy
y
z
dz
]
Solution.
c
=
ˆj y 2 z kˆ] (iˆ dx ˆj dy kˆ dz )
S Curl F n ds
2
F d r =
By Stoke’s theorem
c [(2 x y) iˆ yz
Curl F = F =
iˆ
x
ˆj
y
2 x y yz 2
...(1)
kˆ
z
1
O
y2 z
= (– 2 yz 2 yz ) iˆ – (0 – 0) ˆj (0 1) kˆ kˆ
Putting the value of curl F in (1), we get
=
kˆ nˆ ds
=
kˆ nˆ
dx dy
=
nˆ kˆ
Example 87. Evaluate
dx dy
dx dy
ds
(nˆ kˆ)
= Area of the circle =
C F . d r , where F ( x, y, z ) – y iˆ xjˆ z
2
2
kˆ and C is the curve of
intersection of the plane y + z = 2 and the cylinder x2 + y2 = 1. (Gujarat, I sem. Jan. 2009)
Solution.
C F . dr S
curl F . nˆ ds
S
curl (– y 2 iˆ x ˆj z 2 kˆ) nˆ ds
2
2
F (x, y, z) = y iˆ x ˆj z kˆ
ˆj
iˆ
kˆ
Curl F =
x y z
– y2
x
...(1)
(By Stoke’s Theorem)
z2
= iˆ (0 – 0) – ˆj (0 – 0) kˆ (1 2 y) (1 2 y) kˆ
Normal vector = F
ˆ
ˆj
kˆ
= iˆ
( y z – 2) ˆj k
x
y
z
ˆj kˆ
Unit normal vector n̂
=
2
dx dy
ds =
ˆ . kˆ
3y
+
z=
2
x2 + y2 = 1
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Vectors
439
On putting the values of curl F , nˆ and ds in (1), we get
ˆj kˆ dx dy
C F . dr = S (1 2 y) kˆ . 2 ˆj kˆ
. kˆ
2
1 2 y dx dy
2 1
(1 2 y ) dx dy
1
=
= (1 2 r sin ) r d d r
2
0
0
Y
2
=
=
2
1
0 0 (r 2r
2
0
2
sin ) d d r
1
r 2 2r 3
2
d
sin
0
3
2
0
r ddr
1 2
2 3 sin d
2
2
2
2
= – cos – – 0 =
3
3
2 3
0
Example 88. Apply Stoke’s Theorem to find the value of
X
O
Ans.
c ( y dx z dy x dz )
where c is the curve of intersection of x2 + y2 + z2 = a2 and x + z = a. (Nagpur, Summer 2001)
Solution.
c ( y dx z dy x dz )
ˆ
ˆ
= c ( yiˆ zjˆ xk ) (iˆ dx ˆj dy k dz )
= S curl ( yiˆ zjˆ xkˆ) nˆ ds
=
S iˆ x ˆj y kˆ z ( yiˆ zjˆ xkˆ) nˆ ds
C ( yiˆ zjˆ xkˆ) dr
=
(By Stoke’s Theorem)
=
S (iˆ ˆj kˆ) nˆ ds
...(1)
where S is the circle formed by the intersection of x2 + y2 + z2 = a2 and x + z = a.
n̂ =
| |
iˆ
n̂ =
2
Putting the vlaue of n̂
ˆ
ˆj
kˆ ( x z a )
i
iˆ kˆ
x
y
z
=
=
11
| |
kˆ
2
in (1), we have
iˆ
kˆ
ds
= S (iˆ ˆj kˆ)
2
2
1
1
= S
ds
2
2
2
2
2 a
a2
ds
=
2 S
2 2
2
Example 89. Directly or by Stoke’s Theorem, evaluate
a2 a2
2
2
2
2
Use r R p a
2
2
Ans.
s curl v nˆ ds, v iyˆ ˆjz kxˆ ,
s is
the surface of the paraboloid z = 1 – x2 – y2, z3 > 0 and n̂ is the unit vector normal to s.
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Vectors
Solution.
iˆ
ˆj
v = x
y
y
z
kˆ
iˆ ˆj kˆ
z
x
n̂ = kˆ.
Obviously
( v ) nˆ = (– iˆ – ˆj – kˆ). kˆ – 1
Therefore
( v ) nˆ ds
Hence
S
=
( 1) dx dy
S
= dx dy
S
= – (1)2 = – .
Example 90. Use Stoke’s Theorem to evaluate
c v dr ,
(Area of circle = r2) Ans.
where v y 2 iˆ xyjˆ x z kˆ, and c
is the bounding curve of the hemisphere x2 + y2 + z2 = 9, z > 0, oriented in the positive
direction.
Solution. By Stoke’s theorem
c v dr
=
S (curl v ) nˆ ds S ( v ) nˆ ds
iˆ
v =
x
ˆj
y
kˆ
z
y2
xy
xz
(0 0) iˆ ( z 0) ˆj ( y 2 y ) kˆ
zjˆ ykˆ
2
2
2
i
j
k
( x y z 9)
x
y
z
n̂ =
=
| |
| |
ˆ
2 xiˆ 2 yjˆ 2 zk
xiˆ yjˆ zkˆ
xiˆ yjˆ zkˆ
=
3
4 x2 4 y 2 4 z2
x2 y 2 z 2
xiˆ yjˆ zkˆ yz yz 2 yz
( v ) nˆ = ( zjˆ ykˆ)
3
3
3
ˆ yjˆ zkˆ
z
xi
. kˆ dx = dx dy ds = dx dy
n̂ kˆ ds = dx dy
3
3
3
ds = dx dy
z
2 yz 3
S ( v ) nˆ ds = 3 z dx dy = 2 y dx dy
2
= 2 r sin r d dr = 2
3
0
3
sin d r 2 dr
0
r3
= 2 ( cos )20 = – 2 (– 1 + 1) 9 = 0 Ans.
3 0
Example 91. Evaluate the surface integral
S
curl F . nˆ d S by transforming it into a line
integral, S being that part of the surface of the paraboloid z = 1 – x2 – y2 for which
z 0 and F y iˆ z ˆj x kˆ .
(K. University, Dec. 2008)
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Vectors
441
Solution.
iˆ
ˆj
F = x
y
y
z
kˆ
iˆ ˆj kˆ
z
x
n̂ = kˆ.
Obviously
( F ) nˆ = (– iˆ – ˆj – kˆ). kˆ – 1
Therefore
( F ) nˆ ds
Hence
S
=
( 1) dx dy
S
= dx dy
= – (1)2 = – .
Example 92. Evaluate
C F dr
S
(Area of circle = r2) Ans.
by Stoke’s Theorem, where F y 2 iˆ x 2 ˆj ( x z ) kˆ and
C is the boundary of triangle with vertices at (0, 0, 0), (1, 0, 0) and (1, 1, 0).
(U.P., I Semester, Winter 2000)
Solution. We have, curl F = F
iˆ
=
x
kˆ
z
ˆj
y
0. iˆ ˆj 2 ( x y ) kˆ.
y 2 x2 ( x z)
We observe that z co-ordinate of each vertex of the triangle is zero.
Therefore, the triangle lies in the xy-plane.
n̂ = k̂
curl F nˆ [ ˆj 2 ( x y ) kˆ] . kˆ 2 ( x y ).
In the figure, only xy-plane is considered.
The equation of the line OB is y = x
By Stoke’s theorem, we have
F dr =
C
(curl F nˆ) ds
S
x
1
y2
x 0 y 0 2 ( x y) dx dy = 2 0 xy 2 dx
0
1
2
1 2
x2
1x
1 2
3
dx = x dx = x = 1 .
= 2 0 x dx = 2
0 2
0
2
3
3 0
=
1
x
Ans.
by Stoke’s Theorem, where F ( x 2 y 2 ) iˆ 2 xy ˆj and C
is the boundary of the rectangle x = a, y = 0 and y = b. (U.P., I Semester, Winter 2002)
Solution. Since the z co-ordinate of each vertex of the given rectangle is zero, hence the given
rectangle must lie in the xy-plane.
Here, the co-ordinates of A, B, C and D are (a, 0), (a, b), (– a, b) and (– a, 0) respectively.
Example 93. Evaluate
C F dr
iˆ
x
ˆj
y
x2 y 2
2x y
Curl F =
kˆ
=–4yk
z
0
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Vectors
Here, nˆ kˆ, so by Stoke’s theorem, we’ve
C F dr
=
=
S curl F nˆ d s
S ( 4 y kˆ) (kˆ) d x d y = 4
a
=4
b
y2
2
2 dx = 2 b
0
a
a
b
y dx dy
x a y 0
a
dx 4 a b
2
Ans.
a
Example 94. Apply Stoke’s Theorem to calculate
c 4 y dx 2 z dy 6 y dz
where c is the curve of intersection of x2 + y2 + z2 = 6 z and z = x + 3.
c F dr
Solution.
=
c 4 y dx 2 z dy 6 y dz
=
ˆ )
ˆ ˆjdy kdz
c (4 yiˆ 2 zjˆ 6 ykˆ) (idx
F = 4 yiˆ 2 zjˆ 6 ykˆ
ˆj
iˆ
kˆ
(6 2) iˆ (0 0) ˆj (0 4) kˆ
F = x y z
4 iˆ 4 kˆ
4y 2z 6y
S is the surface of the circle x2 + y2 + z2 = 6z, z = x + 3, n̂ is normal to the plane x – z + 3 = 0
ˆ
ˆj
kˆ ( x z 3)
i
iˆ kˆ iˆ kˆ
y
z
x
=
=
n̂ =
| |
11
2
| |
iˆ kˆ
44
( F ) nˆ = (4 iˆ 4 kˆ)
=
= 4 2
2
2
c F dr = S (curl F ) nˆ ds
=
S 4
2 ( dx dz ) = 4 2 (area of circle)
Centre of the sphere x2 + y2 + (z – 3)2 = 9, (0, 0, 3) lies on the plane z = x + 3. It means that
the given circle is a great circle of sphere, where radius of the circle is equal to the radius of the
sphere.
Radius of circle = 3, Area = (3)2 = 9
S ( F ) nˆ ds
= 4 2(9 ) 36 2
Ans.
Example 95. Verify Stoke’s Theorem for the function F z iˆ x ˆj y kˆ, where C is the unit
circle in xy-plane bounding the hemisphere z =
Solution. Here
Also,
(1 x 2 y 2 ). (U.P., I Semester Comp. 2002)
F = z iˆ x ˆj y kˆ.
r = xiˆ y ˆj z kˆ
F dr = z dx + x dy + y dz.
...(1)
dr = d xiˆ dy ˆj dz kˆ.
C F dr = C ( z dx x dy y dz ).
...(2)
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Vectors
443
On the circle C, x2 + y2 = 1, z = 0 on the xy-plane. Hence on C, we
have z = 0 so that dz = 0. Hence (2) reduces to
C F dr = C x dy.
x2
Now the parametric equations of C, i.e.,
x = cos , y = sin .
Using (4), (3) reduces to
C F . dr
=
...(3)
+
y2
= 1 are
...(4)
2
0 cos cos d
=
21
0
cos 2
d
2
2
1
sin 2
=
=
...(5)
2
2 0
2
2
2
Let P(x, y, z) be any point on the surface of the hemisphere x + y + z = 1, O origin is the
centre of the sphere.
Radius = OP = xiˆ y ˆj z kˆ
Normal = xiˆ y ˆj z kˆ
ˆ
ˆ
ˆ
n̂ = x i y j z k x iˆ y ˆj z kˆ
x2 y2 z2
(Radius is to tangent i.e. Radius is normal)
x = sin cos , y = sin sin , z = cos
...(6)
n̂ = sin cos iˆ + sin sin ĵ + cos k̂
ˆj
iˆ
kˆ
Curl F = / x / y / z iˆ ˆj kˆ
z
x
y
Also,
s
...(7)
Curl F nˆ = (iˆ ˆj kˆ) . (sin cos iˆ sin sin ˆj sin kˆ)
= sin cos + sin sin + cos
/ 2 2
(iˆ ˆj kˆ)
Curl F nˆ dS =
0 0
. (sin cos iˆ + sin sin ĵ + cos k̂ ) sin d d
=
/2
2
0 sin d 0 (sin cos sin sin cos ) d
[ dS = Elementary area on hemisphere = sin d d]
=
=
/2
0
/2
0
sin d [sin sin sin ( cos ) cos ]20 =
/ 2
(0 0 2 sin cos ) d =
0
= – (/2) [– 1 – 1] = .
From (5) and (8),
C
F dr =
S curl F nˆ d S ,
/2
0
sin d
/ 2
sin 2 d = cos 2
2 0
which verifies Stokes’s theorem.
Example 96. Verify Stoke’s theorem for the vector field F (2 x – y ) iˆ – yz 2 ˆj – y 2 z kˆ over
the upper half of the surface x2 + y2 + z2 = 1 bounded by its projection on xy- plane.
(Nagpur University, Summer 2001)
2
Solution. Let S be the upper half surface of the sphere x + y2 + z2 = 1. The boundary C or S
is a circle in the xy plane of radius unity and centre O. The equation of C are x2 + y2 = 1,
z = 0 whose parametric form is
x = cos t, y = sin t, z = 0, 0 < t < 2
C F . dr
=
C [ (2 x – y) iˆ – yz
2
ˆj – y 2 z kˆ ].[iˆ dx ˆj dy kˆ dz ]
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Vectors
=
C [ (2 x – y ) dx – yz
2
dy – y 2 z dz ] =
C (2 x – y) dx,
since on C, z = 0 and 2z = 0
2
dx
dt (2 cos t – sin t ) (– sin t ) dt
= 0 (2 cos t – sin t )
0
dt
2
2
1 – cos 2t
2
= 0 (– sin 2t sin t ) dt 0 – sin 2t
dt
2
2
1
1
cos 2t t sin 2t
–
=
2 – 2
2
2
4
0
ˆj
iˆ
kˆ
2
x
y
z
2x – y
– yz 2
– y2 z
Curl F =
...(1)
= (– 2 yz 2 yz ) iˆ (0 – 0) ˆj (0 1) kˆ kˆ
Z
Curl F . nˆ = kˆ . nˆ nˆ . kˆ
nˆ . kˆ ds
Curl F . nˆ ds =
dx dy
.
nˆ kˆ
Where R is the projection of S on xy-plane.
S
=
S
1
1– x
–1 –
2
1 – x2
dx dy
=
1
–1 2
R nˆ . kˆ .
2
1 – x dx 4
1
0
1
O
1 – x dx
1
x
1
2
–1
= 4 2 1 – x 2 sin x 4 2 . 2
0
From (1) and (2), we have
C F . dr
=
Y
2
X
C
...(2)
Curl F . nˆ ds which is the Stoke's theorem.
Ans.
Example 97. Verify Stoke’s Theorem for F ( x 2 y 4) iˆ 3 xyjˆ (2 xz z 2 ) kˆ
over the surface of hemisphere x2 + y2 + z2 = 16 above the xy-plane.
Solution.
c F dr, where c is the boundary of the circle x2 + y2 + z2 = 16
=
Putting
(bounding the hemispherical surface)
ˆ ˆjdy )
[( x 2 y 4) iˆ 3 xyjˆ (2 xz z 2 ) kˆ ] (idx
c
2
c [( x y 4) dx 3 xy dy)]
=
x = 4 cos , y = 4 sin , dx = – 4 sin d , dy = 4 cos d
=
2
0
[(16 cos 2 4 sin 4) ( 4 sin d ) (192 sin cos 2 d )]
2
2
2
2
= 16 0 [ 4 cos sin sin sin 12 sin cos ] d
2
2
2
= 16 0 (8 sin cos sin sin ) d
2
2
= 16 0 sin d
1
2
= 16 4 02 sin d = 64
= – 16 .
2 2
ˆj
iˆ
kˆ
To evaluate surface integral F =
x
y
z
2
sin n cos d 0
2
n
cos
sin
d
0
0
0
x 2 y 4 3 xy 2 xz z 2
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= (0 – 0) iˆ – (2 z – 0) ˆj (3 y – 1) kˆ – 2 zjˆ (3 y – 1) kˆ
ˆ
ˆj
kˆ ( x 2 y 2 z 2 16)
i
x
y
z
n̂ =
=
| |
| |
ˆ
ˆ
ˆ
ˆ
xi yjˆ zkˆ
2 xi 2 yj 2 zk
xiˆ yjˆ zkˆ
=
=
=
4
x2 y 2 z2
4 x2 4 y 2 4 z2
xiˆ yjˆ zkˆ
2 yz (3 y 1) z
=
( F ) nˆ = [– 2 zjˆ (3 y – 1) kˆ]
4
4
z
xiˆ yjˆ zkˆ
k̂ n ds = dx dy
. k ds = dx dy
ds = dx dy
4
4
4
ds =
dx dy
z
2 yz (3 y 1) z 4
dx dy = [ 2 y (3 y 1)] dx dy =
( F ) nˆ ds =
4
z
On putting x = r cos , y = r sin , dx dy = r d dr, we get
=
(r sin 1) r d dr4
2
2
d (r
2
sin r ) dr
2
64
= d sin 8
3
0
0
0
2
64
64
64
16
cos 8 =
=
= – 16
3
3
3
0
The line integral is equal to the surface integral, hence Stoke’s Theorem is verified. Proved.
=
r
3
=
( y 1) dx dy
d 3
sin
r
2
Example 98. Verify Stoke’s theorem for a vector field defined by F ( x 2 – y 2 ) iˆ 2 xy ˆj in
the rectangular in xy-plane bounded by lines x = 0, x = a, y = 0, y = b.
(Nagpur University, Summer 2000)
Solution. Here we have to verify Stoke’s theorem C F . dr = S ( F ) . nˆ ds
Where ‘C’ be the boundary of rectangle (ABCD) and S be the surface enclosed by curve C.
F = ( x2 – y 2 ) iˆ (2 xy ) ˆj
2
2
F . dr = [ ( x – y ) iˆ 2 xy ˆj] . [iˆ dx ˆj dy]
F . dr = (x2 + y2) dx + 2xy dy
...(1)
C F . dr = OA F . d r AB F . d r BC F . d r CO F . d r
Now,
Along AB, put x = a so that dx = 0 in (1), we get
Where y is from 0 to b.
b
AB F . d r 0 2ay dy
= [ay 2 ] b0 ab 2
...(2)
Along OA, put y = 0 so that k dy = 0 in (1) and F . d r = x2 dx,
Where x is from 0 to a.
a
x3
a3
a 2
OA F . dr 0 x dx = 3 0 3
F .dr
...(3)
= 2ay dy
...(4)
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Vectors
Along BC, put y = b and dy = 0 in (1) we get F . dr = (x2 – b2) dx,
where x is from a to 0.
0
x3
– a3
( x 2 – b2 ) dx – b2 x
b2a
F
.
d
r
=
a
BC
3
3
a
0
Along CO, put x = 0 and dx = 0 in (1), we get F . d r 0
...(5)
Y
C
...(6)
CO F . d r = 0
Putting the values of integrals (3), (4), (5) and (6) in (2),
x=0
we get
a3
a3
ab2 –
ab2 0 2ab2 ...(7)
=
F
.
d
r
O
C
3
3
Now we have to evaluate R.H.S. of Stoke’s Theorem i.e.
S
y=b
B
(a, b)
x=a
A
X
y=0
( F ) . nˆ ds
We have,
iˆ
x
ˆj
y
x2 – y 2
2 xy
F =
kˆ
(2 y 2 y ) kˆ 4 y kˆ
z
0
Also the unit vector normal to the surface S in outward direction is n̂ k
( z-axis is normal to surface S)
Also in xy-plane ds = dx dy
S ( F ) . nˆ. ds = R 4 y kˆ . kˆ dx dy R 4 y dx dy.
Where R be the region of the surface S.
Consider a strip parallel to y-axis. This strip starts on line y = 0 (i.e. x-axis) and end on the line
y = b, We move this strip from x = 0 (y-axis) to x = a to cover complete region R.
S
( F ) . nˆ. ds =
=
C
a
b
a
0 0 4 y dy dx 0 [2 y
a
0 2b
2
2 b
]0
dx
dx 2b 2 [ x] a0 2 ab 2
...(8)
From (7) and (8), we get
F . dr =
S ( F ) . nˆ ds and hence the Stoke’s theorem is verified.
Example 99. Verify Stoke’s Theorem for the function
2
F = x iˆ – xyjˆ
integrated round the square in the plane z = 0 and bounded by the lines
x = 0, y = 0, x = a, y = a.
2
Solution. We have, F = x iˆ – xyjˆ
ˆj
iˆ
kˆ
F =
x
y z
x2
xy
0
= (0 – 0) iˆ – (0 – 0) ˆj (– y – 0) kˆ – ykˆ
( n̂ to xy plane i.e. k̂ )
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Vectors
447
S ( F ) nˆ ds = S ( yk ) k dx dy
a
a
y2
a2
a3
( x)a0 =
=
= dx ydy = dx
2
2
2 0
0
0
0
To obtain line integral
a
a
F d r
=
C
(x
2
iˆ xyjˆ) (iˆ dx ˆj dy ) =
C
(x
2
...(1)
dx xy dy )
C
where c is the path OABCO as shown in the figure.
Also,
Along
F d r =
C
OABCO
OA, y = 0, dy = 0
OA F d r
=
F dr =
OA ( x
2
OA
AB
BC
a
Along
AB F dr
=
2
AB ( x
2
dx x y d y )
dy = 0
dx = 0
dy= 0
dx = 0
Lower Upper
limit
limit
x=0 x=a
y=0 y=a
x=a x=0
y=a y=0
a
y2
a3
= 0 a y d y = a =
2
2 0
y = a, dy = 0
a
Along BC,
BC
...(2)
CO
line Eq. of
line
OA y = 0
AB x = a
BC y = a
CO x = 0
dx xydy )
x3
a3
= 0 x dx = =
3
3 0
AB, x = a, dx = 0
a
F dr F dr F dr F dr
2
F dr = BC ( x dx xy dy ) =
Along CO,
0 2
x dx
a
x = 0, dx = 0
0
x3
a3
= =
3
3 a
2
= CO ( x dx xy dy ) = 0
Putting the values of these integrals in (2), we have
CO F dr
C F dr =
a3
a3 a3 a3
0 =
2
3
2
3
From (1) and (3),
( F ) nˆ ds =
S
...(3)
F dr
C
Hence, Stoke’s Theorem is verified.
Ans.
Example 100. Verify Stoke’s Theorem for F = (x + y) iˆ + (2x – z) ĵ + (y + z) k̂ for the
surface of a triangular lamina with vertices (2, 0, 0), (0, 3, 0) and (0, 0, 6).
(Nagpur University 2004, K. U. Dec. 2009, 2008, A.M.I.E.T.E., Summer 2000)
Solution. Here the path of integration c consists of the straight lines AB, BC, CA where the
co-ordinates of A, B, C and (2, 0, 0), (0, 3, 0) and (0, 0, 6) respectively. Let S be the plane
surface of triangle ABC bounded by C. Let n̂ be unit normal vector to surface S. Then by
Stoke’s Theorem, we must have
c F dr =
s curl F nˆ ds
...(1)
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448
Vectors
L.H.S. of (1)=
c
ABC
F dr =
AB F dr BC F dr CA F dr
Along line AB, z = 0, equation of AB is
x y
=1
2 3
3
3
(2 x), dy = dx
2
2
At A, x = 2, At B, x = 0, r = xiˆ yjˆ
y =
AB F dr
=
=
=
ˆ ˆjdy )
AB [( x y) iˆ 2 xjˆ ykˆ] (idx
AB ( x y) dx 2 xdy
AB x 3
3x
3
dx 2 x dx
2
2
0
7 x2
7x
3 dx
3x
= 2
4
2
2
= (7 – 6) = + 1
0
line
AB
BC
CA
Eq. of
Lower
line
limit
At A
3
x y
1 dy – dx
2
x2
2 3
z=0
At B
y z
1 dz = – 2dy
y3
3 6
x=0
At C
x z
1 dz = – 3dx
x0
2 6
y=0
Along line BC, x = 0, Equation of BC is
Upper
limit
At B
x0
At C
y0
At A
x2
y z
= 1 or z = 6 – 2y,, dz = – 2dy
3 6
At B, y = 3, At C, y = 0, r = yjˆ zkˆ
BC F dr = BC [ yi zj ( y z )k ] ( jdy kdz) = BC zdy ( y z) dz
0
= 3 ( 6 2 y ) dy ( y 6 2 y ) ( 2dy )
0
2
0
= 3 (4 y 18) dy (2 y 18 y ) 3 = 36
x z
Along line CA, y = 0, Eq. of CA, = 1 or z = 6 – 3x, dz = – 3dx
2 6
At C, x = 0, at A, x = 2, r = xiˆ zkˆ
CA F dr
=
CA [ xiˆ (2 x z) ˆj zkˆ] [dxiˆ dzkˆ] = CA ( xdx zdz )
=
0 xdx (6 3x) ( 3dx)
2
=
2
0 (10 x 18) dx
2
2
= [5 x 18 x]0 = – 16
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Vectors
449
L.H.S. of (1) =
ABC F dr
=
AB F dr BC F dr CA F dr
= 1 + 36 – 16 = 21
...(2)
ˆj
kˆ [( x y ) iˆ (2 x z ) ˆj ( y z ) k ]
Curl F = F = iˆ
y
z
x
ˆj
iˆ
kˆ
(1 1) iˆ (0 0) ˆj (2 1) kˆ 2iˆ kˆ
y
z
= x
x y 2x z y z
Equation of the plane of ABC is
x y z
=1
2 3 6
Normal to the plane ABC is
ˆ ˆ ˆ
x y z
ˆj
kˆ 1 = i j k
= iˆ
x
y
z
2
3
6
2 3 6
ˆi
ˆj kˆ
Unit Normal Vector = 2 3 6
1 1 1
4 9 36
1
n̂ =
(3iˆ 2 ˆj kˆ)
14
R.H.S. of (1) =
s curl F n ds
=
s (2iˆ kˆ)
1
4
(3iˆ 2 ˆj kˆ)
dx dy
1
(3iˆ 2 ˆj kˆ).kˆ
14
(6 1) dx dy
= 7 dx dy = 7 Area of OAB
1
14
14
1
= 7 2 3 = 21
2
with the help of (2) and (3) we find (1) is true and so Stoke’s Theorem is verified.
Example 101. Verify Stoke’s Theorem for
=
s
...(3)
F = (y – z + 2) iˆ + (yz + 4) ĵ – (xz) k̂
over the surface of a cube x = 0, y = 0, z = 0, x = 2, y = 2, z = 2 above the XOY plane
(open the bottom).
Solution. Consider the surface of the cube as shown in the figure. Bounding path is OABCO
shown by arrows.
ˆ )
ˆ ˆjdy kdz
F d r = [( y z 2) iˆ ( yz 4) ˆj ( xz ) kˆ] (idx
c
=
( y z 2) dx ( yz 4) dy xzdz
c
F dr
c
=
OA
F dr
AB
F dr
BC
F dr
(1) Along OA, y = 0, dy = 0, z = 0, dz = 0
F dr ...(1)
CO
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450
Vectors
Line
of line
y 0 dy 0
1 OA
2 AB
3 BC
4 CO
Equ.
z0
x2
z 0
y2
z0
x0
z 0
dz 0
dx 0
dz 0
dy 0
dz 0
dx 0
dz 0
2
F dr =
Lower Upper F . dr
limit
limit
x=0
x=2
2 dx
y=0
y=2
4 dy
x=2
x=0
4 dx
y=2
y=0
4 dy
2
2 dx [2 x]0
=4
0
OA
(2) Along AB, x = 2, dx = 0, z = 0, dz = 0
2
F dr =
2
4 dy 4 ( y)0
=8
0
AB
(3) Along BC, y = 2, dy = 0, z = 0, dz = 0
2
F dr =
0
(2 0 2) dx (4 x) 2
=–8
0
BC
(4) Along CO, x = 0, dx = 0, z = 0, dz = 0
F dr =
CO
( y 0 2) 0 (0 4) dy 0
= 4 dy 4 ( y ) 02 = – 8
On putting the values of these integrals in (1), we get
F dr
=4+8–8–8=–4
c
To obtain surface integral
iˆ
kˆ
ˆj
x
y
z
=
F
y z 2 yz 4 xz
= (0 – y) iˆ – (– z + 1) ĵ + (0 – 1) k̂ = – y iˆ + (z – 1) ĵ – k̂
Here we have to integrate over the five surfaces, ABDE, OCGF, BCGD, OAEF, DEFG.
Over the surface ABDE (x = 2), n̂ = i, ds = dy dz
( F ) nˆ ds =
=
[ yi ( z 1) j k ] i dx dz
z f ( x, y )
R F3 ( x, y, z) z f ( x, y ) dx dy
y dy dz
2
1
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Vectors
451
Outward
normal
ABDE
i
OCGF
–i
BCGD
j
OAEF
–j
DEFG
k
Surface
1
2
3
4
5
ds
dy dz
dy dz
dx dz
dx dz
dx dy
x=2
x=0
y=2
y=0
z=2
2
2
2
y2
2
= y dy dz [ z ]0 4
2
0
0
0
Over the surface OCGF (x = 0), n̂ = – i, ds = dy dz
( F ) nˆ ds =
[ yiˆ ( z 1) ˆj kˆ] (iˆ) dy dz
2
2
2
y2
= y dy dz y dy dz 2 4
2 0
0
0
(3) Over the surface BCGD, (y = 2), n̂ = j, ds = dx dz
( F ) nˆ ds =
[ yiˆ ( z 1) ˆj kˆ] ˆj dx dz
2
= ( z 1) dx dz =
2
2
z2
dx ( z 1) dz = ( x)20 2 z = 0
0
0
0
(4) Over the surface OAEF, (y = 0), n̂ = – ĵ , ds = dx dz
(
F ) nˆ d s
=
[ yiˆ ( z 1) ˆj kˆ] ( ˆj) dx dz
2
2
2
z2
= ( z 1) dx dz = – dx ( z 1) dz = ( x)20
z = 0
0
0
2
0
(5) Over the surface DEFG, (z = 2), n̂ = k, ds = dx dy
( F ) nˆ ds
=
[ yiˆ ( z 1) ˆj kˆ] kˆ dx dy
2
=–
dx dy
2
= – dx dy = [ x]20 [ y]02 = – 4
0
0
Total surface integral = – 4 + 4 + 0 + 0 – 4 = – 4
Thus
S curl F nˆ ds
=
F dr = – 4
c
which verifies Stoke’s Theorem.
Ans.
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452
Vectors
EXERCISE 5.14
1.
y
Use the Stoke’s Theorem to evaluate
2
dx xy dy xz dz ,
C
where C is the bounding curve of the hemisphere x2 + y2 + z2 = 1, z 0, oriented in the positive
direction.
Ans. 0
2.
Evaluate
s (curl F ) nˆ dA,
using the Stoke’s Theorem, where F yiˆ zjˆ xkˆ and s is the paraboloid
z = f (x, y) = 1 – x2 – y2 , z 0.
3.
Evaluate the integral for
C y
2
Ans.
2
2
dx z dy x dz , where C is the triangular closed path joining the points
(0, 0, 0), (0, a, 0) and (0, 0, a) by transforming the integral to surface integral using Stoke’s Theorem.
Ans.
a3
.
3
4.
Verify Stoke’s Theorem for A 3 yiˆ xzjˆ yz 2 kˆ, where S is the surface of the paraboloid 2z = x2 + y2
bounded by z = 2 and c is its boundary traversed in the clockwise direction.
Ans. – 20
5.
Evaluate
6.
7.
If S is the surface of the sphere x2 + y2 + z2 = 9. Prove that
Verify Stoke’s Theorem for the vector field
F (2 y z ) iˆ ( x – z ) ˆj ( y – x) kˆ
C F
d R where F yiˆ xz 3 ˆj zy 3kˆ, C is the circl x2 + y2 = 4, z = 1.5
S curl F ds
Ans. 19
2
= 0.
over the portion of the plane x + y + z = 1 cut off by the co-ordinate planes.
8.
Evaluate
c F dr
by Stoke’s Theorem for F yz iˆ zx ˆj xy k and C is the curve of intersection of
x2 + y2 = 1 and y = z2.
9.
If F ( x – z ) iˆ ( x yz ) ˆj 3 xy kˆ and S is the surface of the cone z = a –
xy-plane, show that
10.
Ans. 0
3
2
s curl F dS
2
2
( x y ) above the
= 3 a4 / 4.
If F 3 yiˆ xyjˆ yz 2kˆ and S is the surface of the paraboloid 2z = x2 + y2 bounded by z = 2, show by
using Stoke’s Theorem that
11.
s ( F ) dS
= 20 .
If F ( y 2 z 2 – x 2 ) iˆ ( z 2 x 2 – y 2 ) ˆj ( x 2 y 2 – z 2 ) kˆ, evaluate
x2
curl F nˆ ds
integrated over
y2
the portion of the surface + – 2ax + az = 0 above the plane z = 0 and verify Stoke’s Theorem; where
(A.M.I.E.T.E., Winter 20002) Ans. 2 a3
n̂ is unit vector normal to the surface.
12.
Evaluate by using Stoke’s Theorem
C sin z dx cos x dy sin y dz
rectangle 0 x , 0 y 1, z 3 .
where C is the boundary of
(AMIETE, June 2010)
5.40 GAUSS’S THEOREM OF DIVERGENCE
(Relation between surface integral and volume integral)
(U.P., Ist Semester, Jan., 2011, Dec, 2006)
Statement. The surface integral of the normal component of a vector function F taken around
a closed surface S is equal to the integral of the divergence of F taken over the volume V enclosed
by the surface S.
Mathematically
S F . nˆ ds V div Fdw
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Vectors
453
Proof. Let F F1iˆ F2 ˆj F3 kˆ.
Putting the values of F , nˆ in the statement of the divergence theorem, we have
S F1 iˆ F2
ˆj F3 kˆ nˆ ds =
=
We require to prove (1).
Let us first evaluate
V
V
V iˆ x ˆj y kˆ z ( F1 iˆ F2
F1
V x
ˆj F3 kˆ) dx dy dz.
F2 F3
dx dy dz
y
z
...(1)
F3
dx dy dz .
z
F3
dx dy dz =
z
z f 2 (x, y )
R z f1 ( x, y )
F3
dz dx dy
z
= R [ F3 ( x, y , f 2 ) F3 ( x, y, f1 )] dx dy
For the upper part of the surface i.e. S2, we have
dx dy = ds2 cos r2 = n̂ 2. k̂ ds2
Again for the lower part of the surface i.e. S1, we have,
...(2)
dx dy = – cos r1, ds1 = n̂ 1. k̂ ds1
R F3 ( x, y, f2 ) dx dy = S2 F3 nˆ2 kˆ ds2
and
R F3 ( x, y, f1 ) dx dy = S1 F3 nˆ1 kˆ ds1
Putting these values in (2), we have
F3
V z dv = S2 F3 nˆ2 kˆ ds2 S1 F3 nˆ1 kˆ ds1 =
Similarly, it can be shown that
F2
...(4)
V y dv = S F2 nˆ ˆj ds
S F3 nˆ kˆ ds
...(3)
F1
dv = S F1 nˆ iˆ ds
...(5)
x
Adding (3), (4) & (5), we have
F1 F2 F3
V x y z dv
ˆ
= ( F1 iˆ F2 ˆj F3 k ) nˆ ds
V
S
V ( F ) dv = S F nˆ ds Proved.
Example 102. State Gauss’s Divergence theorem
S
F . nˆ ds Div F dv where S is the
surface of the sphere x2 + y2 + z2 = 16 and F 3 x iˆ 4 y ˆj 5 z kˆ.
(Nagpur University, Winter 2004)
Solution. Statement of Gauss’s Divergence theorem is given in Art 24.8 on page 597.
Thus by Gauss’s divergence theorem,
S F . nˆ ds
=
v . F dv
Here F 3 x iˆ 4 y ˆj 5 z kˆ
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454
Vectors
ˆ
ˆj
kˆ
. F = iˆ
. (3xiˆ 4 yjˆ 5zk )
x
y
z
. F = 3 + 4 + 5 = 14
Putting the value of . F, we get
S F . nˆ ds = v 14 . dv
where v is volume of a sphere
= 14 v
4
3584
= 14 (4)3
3
3
S F . nˆ ds where F 4 xz iˆ – y
Example 103. Evaluate
Ans.
2
ˆj yz kˆ and S is the surface of the
cube bounded by x = 0, x = 1, y = 0, y = 1, z = 0, z = 1.
(U.P., Ist Semester, 2009, Nagpur University, Winter 2003)
Solution. By Divergence theorem,
S F . nˆ ds = v ( . F ) dv
=
v iˆ x ˆj y kˆ z . (4 xz iˆ – y
=
v x (4 xz ) y (– y
=
v (4 z – 2 y y) dx dy dz
2
1
=
v (4 z – y) dx dy dz 0
=
0 0 (2 z
1
1
2
– yz ) 10 dx dy
)
2
ˆj yz kˆ) dv
( yz ) dx dy dz
z
1
4z 2
0 2 – yz dx dy
0
1
1
0
1
0 (2 – y) dx dy
1
3
3
3
y2
3 1
1
= 0 2 y –
dx 0 dx = [ x ] 0 (1) Ans.
2
2
2
2
2
0
Note: This question is directly solved as on example 14 on Page 574.
1
2
Example 104. Find F nˆ ds, where F (2 x 3 z ) iˆ – ( xz y ) ˆj ( y 2 z ) kˆ and S is
the surface of the sphere having centre (3, – 1, 2) and radius 3.
(AMIETE, Dec. 2010, U.P., I Semester, Winter 2005, 2000)
Solution. Let V be the volume enclosed by the surface S.
By Divergence theorem, we’ve
S F nˆ ds =
V div F dv.
2
Now, div F = x (2 x 3z ) y [ ( xz y )] z ( y 2 z ) = 2 – 1 + 2 = 3
S F nˆ ds = V 3 dv = 3 V dv = 3V.
Again V is the volume of a sphere of radius 3. Therefore
4
4
V = r 3 = (3)3 = 36 .
3
3
S F nˆ ds
= 3V = 3 × 36 = 108
Ans.
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Vectors
455
Example 105. Use Divergence Theorem to evaluate
S
A ds ,
where A x i y j z kˆ and S is the surface of the sphere x2 + y2 + z2 = a2.
(AMIETE, Dec. 2009)
Solution.
3ˆ
S
3ˆ
A ds =
3
v div A dV
ˆj
kˆ ( x 3 iˆ y 3 ˆj z 3 kˆ) dV
= v iˆ
y
z
x
2
2
2
= (3 x 2 3 y 2 3 z 2 ) dV = 3 ( x y z ) dV
v
v
On putting x = r sin cos , y = r sin sin , z = r cos , we get
2
= 3 r 2 (r 2 sin dr d d ) = 3 × 8
v
2
a 4
d sin d 0 r
0
dr
0
a
a5 12 a5
r5
= 24 ()02 ( cos )02 = 24 2 ( 0 1) 5 5
5 0
Example 106. Use divergence Theorem to show that
Ans.
y2 z2 ) d s = 6 V
where S is any closed surface enclosing volume V.
(U.P., I Semester, Winter 2002)
ˆ
2
2
2
ˆj
kˆ
(x y z )
Solution. Here (x2 + y2 + z2) = i
y
z
x
= 2 x iˆ 2 y ˆj 2 z kˆ 2 ( x iˆ y ˆj z kˆ)
S ( x
2
S (x
2
y 2 z 2 ) ds =
S ( x
2
y 2 z 2 ) nˆ ds
n̂ being outward drawn unit normal vector to S
= 2 ( x iˆ y ˆj z kˆ) nˆ ds
S
= 2 div ( x iˆ y ˆj z kˆ) d v
V
...(1)
(By Divergence Theorem)
(V being volume enclosed by S)
ˆ
ˆj
kˆ
( x iˆ y ˆj z k )
div. ( x iˆ y ˆj zkˆ) = iˆ
x
y
z
x y z
=
=3
x y z
From (1) & (2), we have
Now,
( x
2
y 2 z 2 ) dS = 2 3 dv = 6 V dv = 6 V
V
Example 107. Evaluate
S ( y
...(2)
Proved.
z i z 2 x 2 ˆj z 2 y 2 kˆ) nˆ dS , where S is the part of the spheree
2 2ˆ
x2 + y2 + z2 = 1 above the xy-plane and bounded by this plane.
Solution. Let V be the volume enclosed by the surface S. Then by divergence Theorem, we
have
2 2
2 2
2 2
div ( y 2 z 2 iˆ z 2 x 2 ˆj z 2 y 2 kˆ) dV
( y z iˆ z x ˆj z y kˆ) nˆ dS =
V
S
=
V x ( y
2 2
z )
2 2
2 2
2
2
(z x )
( z y ) dV
V 2 z y dV 2 V zy dV
y
z
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Vectors
Changing to spherical polar coordinates by putting
x = r sin cos ,
y= r sin sin ,
z = r cos , dV = r2 sin dr d d
To cover V, the limits of r will be 0 to 1, those of will be 0 to
and those of will be 0 to
2
2.
2
2 zy 2 dV = 2
V
0
2
= 2 0
/2
1
/2
1 5
0 0 (r cos ) (r
0 0 r
2
sin 2 sin 2 ) r 2 sin dr d d
sin 3 cos sin 2 dr d d
1
2 sin 3
0
r6
= 2
cos sin d d
0
6 0
2 2 2
2
1 2 2
d =
sin d =
= 0 sin
0
6
4.2
12
12
2
2
Example 108. Use Divergence Theorem to evaluate
and S is the surface bounding the region
x2
+
y2
Solution. By Divergence Theorem,
S
F dS =
V
S
Ans.
2
2ˆ
F d S where F 4 xiˆ – 2 y ˆj z k
= 4, z = 0 and z = 3.
(A.M.I.E.T.E., Summer 2003, 2001)
div F dV
=
V iˆ x ˆj y kˆ z (4 xiˆ 2 y
=
V (4 4 y 2 z ) dx dy dz
2
ˆj z 2 kˆ ) dV
3
= dx dy (4 4 y 2 z )dz =
0
= (12 12 y 9) dx dy =
Let us put x = r cos , y = r sin
2
=
2 3
]0
(21 12 y) dx dy
2
d 0 (21 r 12 r
(21 12 r sin ) r d dr =
2
sin ) dr
0
2
2
dx dy [4 z 4 yz z
2
21 r 2
4 r 3 sin =
= d
2
0
0
= 84 + 32 – 32 = 84
2
d (42 32 sin ) (42 32 cos )0
0
Ans.
Example 109. Apply the Divergence Theorem to compute
^
u n ds, where s is the surface of
ˆ.
ˆ – ˆjy kz
the cylinder x2 + y2 = a2 bounded by the planes z = 0, z = b and where u ix
Solution. By Gauss’s Divergence Theorem
ˆ
u nds
=
=
=
=
V dv
=
V ( u ) dv
V iˆ x ˆj y kˆ z (ixˆ ˆjy kzˆ )dv
x
y
z
V x y z dv
V dx dy dz
=
V 1 1 1 dv
= Volume of the cylinder = a2b Ans.
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Vectors
457
Example 110. Apply Divergence Theorem to evaluate
V F . nˆ ds,
wheree
F = 4x3iˆ x 2 y ˆj x 2 zkˆ and S is the surface of the cylinder x2 + y2 = a2 bounded by the
planes z = 0 and z = b.
(U.P. Ist Semester, Dec. 2006)
Solution. We have,
3
2
2
F = 4x iˆ x y ˆj x zkˆ
ˆ
3ˆ
2 ˆ
2 ˆ
ˆ
ˆ
div F = i x j y k z (4 x i x yj x zk )
2
3
2
= x (4 x ) y ( x y) z ( x z ) = 12x2 – x2 + x2 = 12 x2
Now,
V div F dV
= 12
a 2 x2
a
xay
a 2 x2
a
= 12 x a
b
a 2 x2
z 0 x
2
dz dy dx
a
y a 2 x2
x 2 ( z )b0 dy dx = 12 b x 2 ( y )
a
a
a
2
2
2
= 12 b a x .2 a x dx
a
2
= 48 b 0 x
a 2 x 2 dx
2
= 24 b a x
a 2 x2
a 2 x2
dx
a 2 x 2 dx
[Put x = a sin , dx = a cos d]
/ 2 2
a sin 2 a cos a cos d
= 48 b 0
= 48 ba
4
/ 2
0
2
2
sin cos d = 48 ba
1
1
4
2
= 48 ba 2
= 3 b a4
2 2
4
3 3
2 2
23
Ans.
where F = (x2 + y2 + z2) (iˆ ˆj kˆ), S
is the surface of the tetrahedron x = 0, y = 0, z = 0, x + y + z = 2 and n is the unit normal in
the outward direction to the closed surface S.
Solution. By Divergence theorem
Example 111. Evaluate surface integral
F nˆ ds,
S F nˆ ds = V div F dv
where S is the surface of tetrahedron x = 0, y = 0, z = 0, x + y + z = 2
ˆj
kˆ ( x 2 y 2 z 2 ) (iˆ ˆj kˆ) dv
= V iˆ
y
z
x
=
=
V (2 x 2 y 2 z ) dv
2 ( x y z ) dx dy dz
V
2
2x
= 2 0 dx 0
2
2 x
= 2 0 dx 0
dy
2x y
0
( x y z ) dz
2 x y
z2
dy x z y z
2 0
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458
Vectors
2
2x
= 2 0 dx 0
(2 x y )2
dy 2 x x 2 xy 2 y xy y 2
2
2 x
2
y 3 (2 x y )3
= 2 dx 2 xy x 2 y x y 2 y 2
0
3
6
0
2
(2 x)3 (2 x )3
= 2 dx 2 x (2 x ) x 2 (2 x ) x (2 x) 2 (2 x)2
0
3
6
3
3
2
(2 x)
(2 x )
= 2 4 x 2 x 2 2 x 2 x3 4 x 4x 2 x 3 (2 x )2
0
3
6
2
4 x3 x 4
4 x3 x 4 (2 x)3 (2 x)4 (2 x) 4
= 2 2x2
2x2
3
4
3
4
3
12
24 0
2
(2 x)3 (2 x) 4 (2 x )4
8 16 16
= 2
= 2 3 12 24 = 4
3
12
24 0
Example 112. Use the Divergence Theorem to evaluate
Ans.
S ( x dy dz y dz dx z dx dy)
where S is the portion of the plane x + 2 y + 3 z = 6 which lies in the first Octant.
(U.P., I Semester, Winter 2003)
Solution.
S ( f1 dy dz
f 2 f 3
dxd ydz
y
z
where S is a closed surface bounding a volume V.
=
f1
f 2 dx dz f3 dx dy )
V x
S ( x dy dz y dz dx z dx dy)
=
x
y
z
V x y z dx dy dz
= V (1 1 1) dx dy dz = 3 V dx dy dz
= 3 (Volume of tetrahedron OABC)
1
= 3 [( Area of the base OAB) height OC ]
3
1 1
= 3 6 3 2 = 18
3
2
Ans.
Example 113. Use Divergence Theorem to evaluate : ( x dy dz y dz dx z dx dy )
over the surface of a sphere radius a.
(K. University, Dec. 2009)
Solution.
Here, we have
x dy dz y dx dz z dx dy
S
f
f
x y z
f
1 2 3 dx dy dz
dx dy dz
V x
V
y
z
x y z
(1 + 1 + 1) dx dy dz = 3 (volume of the sphere)
V
4 3
= 3 a = 4 a3
3
Ans.
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Vectors
459
Example 114. Using the divergence theorem, evaluate the surface integral
( yz dy dz zx dz dx xy dy dx) where S : x2 + y2 + z2 = 4.
S
(AMIETE, Dec. 2010, UP, I Sem., Dec 2008)
Solution.
S
( f1 dy dz f 2 dx dz f3 dx dy )
f1
f 2 f3
dx dy dz
y
z
where S is closed surface bounding a volume V.
v x
=
S ( yz dy dz zx dx dz xy dx dy )
=
( yz ) ( zx ) ( xy )
dx dy dz =
x
y
z
v
v (0 0 0) dx dy dz
= 0
Example 115. Evaluate xz 2 dy dz ( x 2 y z 3 ) dz dx (2 xy y 2 z ) dx dy
S
where S is the surface of hemispherical region bounded by
z =
Solution.
a 2 x2 y 2 and z = 0.
S ( f1 dy dz
f 2 dz dx f3 dx dy ) =
f1
V x
where S is a closed surface bounding a volume V.
S xz
=
2
f 2 f3
dx dy dz
y
z
dy dz ( x 2 y z 3 ) dz dx (2 xy y 2 z ) dx dy
V x ( xz
2
)
2
(x y z3)
(2 xy y 2 z ) dx dy dz
y
z
(Here V is the volume of hemisphere)
2
2
2
= V ( z x y ) dx dy dz
Let x = r sin cos , y = r sin sin , z = r cos
=
Ans.
2
2
r (r sin dr d d ) =
5
2
/ 2 r
(
)
(
cos
)
0
0
=
5
Example 116. Evaluate
S F . nˆ ds
2
0
a
d 2 sin d r 4 dr
0
0
a
a5
2 a5
= 2 ( 0 1)
=
5
5
0
Ans.
over the entire surface of the region above the xy-plane
bounded by the cone z2 = x2 + y2 and the plane z = 4, if F = 4 xz iˆ xyz 2 ˆj 3z kˆ.
Solution. If V is the volume enclosed by S, then V is bounded by the surfaces z = 0, z = 4,
z2 = x2 + y2.
By divergence theorem, we have
S F . nˆ ds
=
V div F dx dy dz
=
V x (4 xz ) y ( xyz
=
V (4 z xz
Limits of z are
2
2
)
(3 z ) dx dy dz
z
3) dx dy dz
x 2 y 2 and 4.
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460
Vectors
=
4
2 xz 3
3z
(4 z xz 3) dz dy dx = 2 z
2
2
3
x y
4
dy dx
2
32
x2 y 2
64 x
12 {2( x 2 y 2 ) x ( x 2 y 2 )3 / 2 3 x 2 y 2 } dy dx
3
64 x
2( x 2 y 2 ) x( x 2 y 2 )3/ 2 3 x 2 y 2 dy dx
3
Putting x = r cos and y = r sin , we have
64 r cos
2r 2 r cos r 3 3r r d dr
= 44
3
Limits of r are 0 to 4.
and limits of are 0 to 2
2 4
64 r 2 cos
44
r
2r 3 r 5 cos 3r 2 d dr
= 0 0
3
=
=
44
0
4
64 r 3 cos r 4 r 6
cos r 3 d
22r
9
2
6
0
2
2
64 (4)3 cos (4)4 (4)6
2
cos (4)3 d
22(4)
9
2
6
6
2
64 64
(4)
cos 128
cos 64 d
= 0 352
9
6
6
64 64 (4)
2
cos d
= 0 160 9
6
2
=
0
64 64 (4)6
= 160 9
6
= 320
2
64 64 (4)6
sin = 160 (2)
sin 2
6
0
9
Ans.
Example 117. The vector field F x 2iˆ zjˆ yzkˆ is defined over the volume of the cuboid
given by 0 x a, 0 y b, 0 z c, enclosing the surface S. Evaluate the surface integral
S F . d s
(U.P., I Semester, Winter 2001)
Solution. By Divergence Theorem, we have
2
2
( x iˆ z ˆj yz kˆ) . ds div ( x iˆ z ˆj yz kˆ) dv,
S
v
where V is the volume of the cuboid enclosing the surface S.
2
ˆ
ˆj
kˆ
. ( x iˆ z ˆj yz k ) dv
= v iˆ
y
z
x
2
( z)
( y z ) dx dy dz
y
z
=
v x ( x
=
x 0 y 0 z 0 (2 x y ) dx dy dz
=
a
b
)
c
a
b
a
b
0
0
0
0
=
a
b
c
0 dx 0 dy 0 (2 x y) dz
c
dx [2 xz yz] 0 dy dx (2 xc yc) dy
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Vectors
461
b
a
b
a
a
y2
b2
= c dx (2 x y ) dy c 2 xy
dx
dx c 2bx
2
2
0
0
0
0
0
a
2bx 2 b 2 x
2
ab2
b
= c
c a b
abc a
2 0
2
2
2
Ans.
Example 118. Verify the divergence Theorem for the function F = 2 x2yi – y2j + 4 x z2k
taken over the region in the first octant bounded by y2 + z2 = 9 and x = 2.
Solution.
V F dV
=
iˆ x ˆj y kˆ z (2 x
=
(4 xy 2 y 8 xz ) dx dy dz =
=
0 dx 0 dy (4 xyz 2 yz 4 xz
=
0 dx 0
2
3
2
3
2
2
0
)0
2
yiˆ y 2 ˆj 4 xz 2 k ) dV
9 y2
3
dx dy
0
0
(4 xy 2 y 8 xz ) dz
9 y2
[4 xy 9 y 2 2 y 9 y 2 4 x (9 y 2 )] dy
3
4x 2
2
4 xy 3
2 3/ 2
2 3/ 2
dx
(9
y
)
(9
y
)
36
xy
= 0
3
3 0
2 3
2
2
2
x2
108
18
x
(0
0
108
x
36
x
36
x
18)
dx
(108
x
18)
dx
= 0
= 0
=
2
0
= 216 – 36 = 180
...(1)
2
Here
S
F nˆ ds =
F nˆ ds =
BDEC
Normal vector
F nˆ ds
OABC
2
F nˆ ds
OCE
F nˆ ds
OADE
(2 x yiˆ y 2 ˆj 4 xz 2 kˆ) . nˆ ds
F nˆ ds
ABD
F nˆ ds
BDEC
BDEC
ˆj
kˆ (y2 + z2 – 9)
= = iˆ
y
z
x
= 2 yjˆ 2 z kˆ
2 yjˆ 2 zkˆ
yjˆ zkˆ
Unit normal vector = n̂ =
=
2
2
4y 4z
y2 z 2
yjˆ zkˆ
yjˆ zkˆ
=
=
3
9
ˆ zk
yj
1
2
2
2
(2 x yiˆ y ˆj 4 xz k ) 3 ds = 3 ( y3 4 xz3 ) ds
BDEC
BDEC
yjˆ zkˆ
z
dx
dy
kˆ ds or ds
dx dy ds (nˆ k ) ds
z
3
3
3
2
3
y3
1
3
3 dxdy
dx
4 xz 2 dy
(
y
4
xz
)
=
=
0
0
z
3
z
BDEC
2
=
0 dx
y 3 sin ,
z 3 cos
3
2
0
3
27 sin
4 x (9 cos 2 )
3 cos
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462
Vectors
=
2
2
2
0 dx 27 3 108 x 3
=
2
0 ( 18 72 x) dx
2
2
= 18 x 36 x = 108
0
2 ˆ
2
2ˆ
ˆ
F nˆ ds = (2 x yi y ˆj 4 xz k ) ( k ) ds
...(2)
OABC
OABC
=
4 x z 2 ds = 0
(2 x 2 yiˆ y 2 ˆj 4 xz 2 kˆ) ( ˆj ) ds =
...(3) because in OABC xy-plane, z = 0
OABC
F nˆ ds =
OADE
(2 x
2
yiˆ y 2 ˆj 4 xz 2 kˆ) ( iˆ) ds =
OCE
F nˆ ds =
(2 x
2
yiˆ y 2 ˆj 4 xz 2 kˆ) (iˆ) ds =
ABD
=
2 x
2
2 x 2 y ds = 0
OCE
ABD
...(4)
because in OADE xz-plane, y = 0
F nˆ ds =
y 2 ds = 0
OADE
OADE
OCE
9 z2
3
y dy dz =
0 dz 0
...(5)
because in OCE yz-plane, x = 0
2 x2 y ds
ABD
2 (2)2 y dy
because in ABD plane, x = 2
9 z2
3
y2
3
z3
= 8 dz
= 4 dz (9 z 2 ) = 4 9 z = 4 [27 – 9] = 72
0
0
3
2 0
0
On adding (2), (3), (4), (5) and (6), we get
3
S F nˆ ds
= 108 + 0 + 0 + 0 + 72 = 180
V F dV
From (1) and (7), we have
=
...(6)
...(7)
S F nˆ ds
Hence the theorem is verified.
Example 119. Verify the Gauss divergence Theorem for
2
2
2
F = (x – yz) iˆ + (y – zx) ĵ + (z – xy) k̂ taken over the rectangular parallelopiped
0 x a, 0 y b, 0 z c.
(U.P., I Semester, Compartment 2002)
Solution. We have
2
2
2
ˆ
ˆj
kˆ
[( x yz ) iˆ ( y zx) ˆj ( z xy ) k ]
div F = F = iˆ
x
y
z
2
2
2
( x yz )
( y zx)
( z xy ) = 2x + 2y + 2z
=
x
y
z
Volume integral =
V F dV
a
b
=
V 2 ( x y z ) dV
c
a
b
c
= 2 x 0 y 0 z 0 ( x y z ) dx dy dz = 2 0 dx 0 dy 0 ( x y z ) dz
a
= 2 0 dx
b
0
c
z2
a
dy xz yz
= 2 dx
2
0
0
b
b
0 dy cx cy
c2
2
a
y 2 c2 y
a
b2 c b c 2
= 2 dx bcx
= 2 0 dx c x y c
0
2
2 0
2
2
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Vectors
463
a
bc x 2 b 2 cx bc 2 x
2
= [a2bc + ab2c + abc2]
=
2
2 0
2
= abc (a + b + c)
...(A)
To evaluate
S F nˆ ds, where S consists of six plane surfaces.
S F nˆ ds =
OABC F nˆ ds
=
OABC F nˆ ds DEFG F nˆ ds OAFG F nˆ ds
BCDE
F nˆ ds
OABC {( x
ABEF
F nˆ ds
OCDG
F nˆ ds
yz ) iˆ ( y 2 xz ) ˆj ( z 2 xy ) kˆ} ( kˆ ) dx dy
2
a b
2
= ( z xy ) dx dy
0 0
ab
a 2 b2
4
= (0 xy ) dx dy =
00
...(1)
DEFG F nˆ ds = DEFG {( x 2 yz ) iˆ ( y 2 xz ) ˆj ( z 2 xy ) kˆ} (kˆ) dx dy
ab
=
2
( z xy) dx dy =
00
a
=
c
S.No. Surface Outward
normal
1
OABC
–k
2
DEFG
k
3
OAFG
–j
4
BCDE
j
5
ABEF
i
6
OCDG
–i
ab
2
(c xy ) dx dy
00
2
y
0
2 b
xy
dx =
2 0
a
2
c b
0
xb
2
2
dx
a
2
x2 b2
a2 b2
c
bx
= abc 2
=
...(2)
4 0
4
{( x 2 yz ) iˆ ( y 2 zx) ˆj ( z 2 xy ) kˆ} ( ˆj ) dx dz
F nˆ ds =
OAFG
ds
dx dy
dx dy
dx dz
dx dz
dy dz
dy dz
z=0
z=c
y=0
y=b
x=a
x=0
OAFG
2
= OAFG ( y zx ) dx dz
a
c
a
= dx (0 zx ) dz =
0
0
x z2
dx
2
0
c
=
0
a
a
x2 c 2
x c2
a 2 c2
2 dx = 4 = 4
0
0
BCDE F nˆ ds = {( x2 y z ) iˆ ( y 2 zx) ˆj ( z 2 xy) kˆ} ˆj dx dz = BCDE ( y
a
c
c
a
2
x z2
= dx (b x z ) dz = b z
dx =
2 0
0
0
0
2
...(3)
2
xz ) dx dz
a
2
x c2
b
c
dx
2
0
a
ABEF
a 2 c2
x2 c2
2
= b2 c x
= ab c
4
4
0
2
2
2
ˆ
F