Edwin K. P. Chong & Stanislaw H. Zak 
An Introduction to Optimization [EPUB ebook] 

Praise from the Second Edition

‘…an excellent introduction to optimization theory…’
(Journal of Mathematical Psychology, 2002)

‘A textbook for a one-semester course on optimization theory and
methods at the senior undergraduate or beginning graduate level.’
(Sci Tech Book News, Vol. 26, No. 2, June 2002)

Explore the latest applications of optimization theory and
methods

Optimization is central to any problem involving decision making
in many disciplines, such as engineering, mathematics, statistics,
economics, and computer science. Now, more than ever, it is
increasingly vital to have a firm grasp of the topic due to the
rapid progress in computer technology, including the development
and availability of user-friendly software, high-speed and parallel
processors, and networks. Fully updated to reflect modern
developments in the field, An Introduction to Optimization,
Third Edition fills the need for an accessible, yet rigorous,
introduction to optimization theory and methods.

The book begins with a review of basic definitions and notations
and also provides the related fundamental background of linear
algebra, geometry, and calculus. With this foundation, the authors
explore the essential topics of unconstrained optimization
problems, linear programming problems, and nonlinear constrained
optimization. An optimization perspective on global search methods
is featured and includes discussions on genetic algorithms,
particle swarm optimization, and the simulated annealing algorithm.
In addition, the book includes an elementary introduction to
artificial neural networks, convex optimization, and
multi-objective optimization, all of which are of tremendous
interest to students, researchers, and practitioners.

Additional features of the Third Edition include:

* New discussions of semidefinite programming and Lagrangian
algorithms

* A new chapter on global search methods

* A new chapter on multipleobjective optimization

* New and modified examples and exercises in each chapter as well
as an updated bibliography containing new references

* An updated Instructor’s Manual with fully worked-out solutions
to the exercises

Numerous diagrams and figures found throughout the text
complement the written presentation of key concepts, and each
chapter is followed by MATLAB exercises and drill problems that
reinforce the discussed theory and algorithms. With innovative
coverage and a straightforward approach, An Introduction to
Optimization, Third Edition is an excellent book for courses in
optimization theory and methods at the upper-undergraduate and
graduate levels. It also serves as a useful, self-contained
reference for researchers and professionals in a wide array of
fields.

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Preface.

Part I: Mathematical Review.

1. Methods of Proof and Some Notation.

2. Vector Spaces and Matrices.

3. Transformations.

4. Concepts from geometry.

5. Elements of Calculus.

Part II: Unconstrained Optimization.

6. Basics of Set-Constrained and Unconstrained Optimization.

7. One-Dimensional Search Methods.

8. Gradient Methods.

9. Newton’s Method.

10. Conjugate Direction Methods.

11. Quasi-Newton Methods.

12. Solving Linear Equations.

13. Unconstrained Optimization and Neural Networks.

14. Global Search Algorithms.

Part III: Linear Programming.

15. Introduction to Linear Programming.

16. Simplex Method.

17. Duality.

18. Nonsimplex Methods.

Part IV: Nonlinear Constrained Optimization

19. Problems with Equality Constraints.

20. Problems with Inequality Constraints.

21. Convex Optimization Problems.

22. Algorithms for Constrained Optimization.

23. Multiobjective Optimization.

References.

Index.

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Edwin K.P. Chong, PHD, is Professor of Electrical and
Computer Engineering and Professor of Mathematics at Colorado State
University. He currently serves as Editor of Computer
Networks and the Journal of Control Science and
Engineering. Dr. Chong was the recipient of the 1998 ASEE
Frederick Emmons Terman Award.

Stanislaw H.Zak, PHD, is Professor of Electrical and
Computer Engineering at Purdue University. He is the former
associate editor of Dynamics and Control and the IEEE
Transactions on Neural Networks, and his research interests
include control, optimization, nonlinear systems, neural networks,
and fuzzy logic control.