Synopses & Reviews
An up-to-date, accessible introduction to an increasingly important field
This timely and authoritative book fills a growing need for an introductory text to optimization methods and theory at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization 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-out 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 the most recent developments, including neural networks, genetic algorithms, and the nonsimplex method of Karmarkar
- A chapter on the use of descent algorithms for the training of feedforward neural networks
- Exercise problems after every chapter
- MATLAB exercises and examples
- An optional solutions manual with MATLAB source listings
This book 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.
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)
Description
Includes bibliographical references (p. 396-399) and index.
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.
Table of Contents
Partial table of contents:
MATHEMATICAL REVIEW.
Methods of Proof and Some Notation.
Real Vector Spaces and Matrices.
Transformations.
Concepts from Geometry.
UNCONSTRAINED OPTIMIZATION.
One-Dimensional Search Methods.
Gradient Methods.
Newton's Method.
Conjugate Direction Methods.
Quasi-Newton Methods.
Genetic Algorithms.
LINEAR PROGRAMMING.
Introduction to Linear Programming.
The Simplex Method.
Duality.
Non-Simplex Methods.
NONLINEAR CONSTRAINED OPTIMIZATION.
Problems with Inequality Constraints.
Convex Optimization Problems.
Bibliography.
Index.