Product Description

A modern, up-to-date introduction to optimization theory and methods
This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization.
Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides:
* A review of the required mathematical background material
* A mathematical discussion at a level accessible to MBA and business students
* A treatment of both linear and nonlinear programming
* An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods
* A chapter on the use of descent algorithms for the training of feedforward neural networks
* Exercise problems after every chapter, many new to this edition
* MATLAB(r) exercises and examples
* Accompanying Instructor's Solutions Manual available on request
An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business.

Product Details

• Amazon Sales Rank: #262684 in Books
• Published on: 2001-07-27
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 496 pages

Review
"...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." (SciTech Book News, Vol. 26, No. 2, June 2002)

From the Back Cover
A modern, up-to-date introduction to optimization theory and methods

This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization.

Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides:

• A review of the required mathematical background material
• A mathematical discussion at a level accessible to MBA and business students
• A treatment of both linear and nonlinear programming
• An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods
• A chapter on the use of descent algorithms for the training of feedforward neural networks
• Exercise problems after every chapter, many new to this edition
• MATLAB® exercises and examples
• Accompanying Instructor’s Solutions Manual available on request

An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business.

About the Author
EDWIN K. P. CHONG, PhD, is Professor of Electrical and Computer Engineering at Colorado State University, Fort Collins, Colorado. He was an Associate Editor for the IEEE Transactions on Automatic Control and received the 1998 ASEE Frederick Emmons Terman Award.
STANISLAW H. ZAK, PhD, is Professor in the School of Electrical and Computer Engineering at Purdue University, West Lafayette, Indiana. He was an Associate Editor of Dynamics and Control and the IEEE Transactions on Neural Networks.

Customer Reviews

took the class, liked the book
Drs. Chong and Zak are Professors of Electrical Engineering at Purdue, and Dr. Chong was the instructor for the ECE grad level optimization class when I took it spring '97. The book alone is good, detailed and rigorous enough for a graduate course without sacrificing readability or in-chapter examples. However, without the MATLAB examples that were developed by the authors to accompany lectures and illustrate each optimization method covered, the material might be a little abstract or dry for self-teaching. An excellent introduction or reference nonetheless, those without a solid base in linear algebra should keep a reference text handy while reading.

Rigor-Envy
I can only speak on the linear programming section in this book. This is an awful text for undergraduates. This is a math text written by engineers who have a huge case of mathematical rigor-envy. They sacrifice all context, specificity, and practicality in lieu of a ridiculus level of mathematical generality. I am experienced in upper division proofing. I found myself reading and understanding every line of the proofs( of which there are many!) and still having no idea what had just been demonstrated. If you already have a PhD in pure mathematics, then this might be the book for you. If you are an undergraduate, stay away! If you need this book for a linear programming course, do youself a favor and also buy Linear Programming be Vasek Chvatal. The Chvatal text is the premier text on LP. It's only disadvantage is that it does not cover interior point methods, but this material can be easily supplemented from other sources. If yor are a prof. and are considering using this book for a undergraduate course, don't. Do your students some good and use a better text.

It reads like source code
I'm an undergraduate math major who is using this book in a linear programming course. The general consesus in my class is that this is a very difficult book to comprehend. Everything seems like it's been abstracted to the n-th degree. Variables are frequently used without reference to definitions, which in many cases appear in earlier sections. It's a pain to try to look up something then have to hunt around for the meaning of all the components used in the definition. That's not to say this book isn't informative, it just takes a lot of work to glean useful information from it. As a student, I prefer books that are easy to reference. I simply don't have time to read the whole chapter about the simplex method when I just want to know how to compute cost coefficients.