TEACHING ARCHIVESTIEI
Course Description (for 2014)
Course Description
Tianjin International Engineering Institute
Course Name(Chinese): 最优化方法
(English):Optimization Methods
Course Name: Optimization Methods
Course Code:
Semester: 1
Credit:
Programme: Electronic
Course Module: Foundation of Engineering
Responsible: Zhang Qijun
E-mail: qjz@doe.carleton.ca
Department:School of Electronic Information Engineering
Time Layout (1 credit hour = 45 minutes)
Practice
Lecture
Lab-study
22
10
Project
Internship(days)
Personal Work
5
Course Resume
The course takes a unified view of optimization and covers the main areas of application and
the main optimization algorithms. It covers the following topics:

Linear optimization

Robust optimization

Network flows

Discrete optimization

Dynamic optimization

Nonlinear optimization
Pre-requirements
Mathematics ;Probability ;Linear Algebra
Course Objectives
This course introduces the principal algorithms for linear, network, discrete, robust,
nonlinear, dynamic optimization and optimal control. Emphasizes methodology and the
underlying mathematical structures. Topics include the simplex method, network flow methods,
branch and bound and cutting plane methods for discrete optimization, optimality conditions for
nonlinear optimization, interior point methods for convex optimization, Newton's method,
heuristic methods, and dynamic programming and optimal control methods.
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TEACHING ARCHIVESTIEI
Course Description (for 2014)
Course Syllabus
1 .Linear optimization
1.1 Applications of linear optimization
1.2 Geometry of linear optimization
1.3 Simplex method
1.4 Duality theory
1.5 Sensitivity analysis
2. Robust optimization
2.1 Robust optimization
2.2 Large scale optimization
3 .Network flows
4 .Discrete optimization
4.1 Applications of discrete optimization
4.2 Branch and bound and cutting planes
4.3 Lagrange an methods
4.4 Heuristics and approximation algorithms
5 .Dynamic programming
6. Nonlinear optimization
6.1 Applications of nonlinear optimization
6.2 Optimality conditions and gradient methods
6.3 Line searches and Newton's method
6.4 Conjugate gradient methods
6.5 Affine scaling algorithm
6.6 Interior point methods
Text Book & References
Textbook: Bertsimas, Dimitris, and John Tsitsiklis. Introduction to Linear Optimization.
Belmont, MA: Athena Scientific, 1997. ISBN: 9781886529199.
We may also use readings from a few textbooks: Stephen Boyd and LievenVandenberghe,
Convex Optimization, Cambridge University Press, 2009
Capability Tasks
CT1
CT2
CT3
CT4
CT10
Achievements
To be able to establish a model, analysis and solve the model, apply this model in practice Level: M
k between various systems and optimization approaches or schemes - Level: M
extract and propose an optimization issuefor a practical engineering problemLevel: A
Students: Electronic year 1
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TEACHING ARCHIVESTIEI
Course Description (for 2014)
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