An Introduction to Optimization, 4th Edition

An Introduction to Optimization, 4th Edition

Book Details
Hardcover: 640 pages
Publisher: Wiley; 4 edition (January 14, 2013)
Language: English
ISBN-10: 1118279018
ISBN-13: 978-1118279014
File Size: 8.0 Mb | File Format: Epub

Book Description

Praise for the Third Edition “. . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail.” —MAA Reviews

Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus.

This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also offers:

A new chapter on integer programming
Expanded coverage of one-dimensional methods
Updated and expanded sections on linear matrix inequalities
Numerous new exercises at the end of each chapter
MATLAB exercises and drill problems to reinforce the discussed theory and algorithms
Numerous diagrams and figures that complement the written presentation of key concepts
MATLAB M-files for implementation of the discussed theory and algorithms (available via the book’s website)

Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

About the Author

Edwin K. P. Chong, PHD, is Professor of Electrical and Computer Engineering as well as Professor of Mathematics at Colorado State University. He is a Fellow of the IEEE and Senior Editor of IEEE Transactions on Automatic Control.

Stanislaw H. Zak, PHD, is Professor in the School of Electrical and Computer Engineering at Purdue University. He is former associate editor of Dynamics and Control and IEEE Transactions on Neural Networks

Table of Contents

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
19 Integer Linear Programming

PART IV NONLINEAR CONSTRAINED OPTIMIZATION
20 Problems with Equality Constraints
21 Problems with Inequality Constraints
22 Convex Optimization Problems
23 Algorithms for Constrained Optimization
24 Multiobjective Optimization