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 multiobjective optimization, the Fourth Edition also offers:
A new chapter on integer programming
Expanded coverage of onedimensional 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 Mfiles 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.
Table of Contents
 Cover
 Half Title page
 Title page
 Copyright page
 Dedication
 Preface
 Part I: Mathematical Review

Part II: Unconstrained Optimization
 Chapter 6: Basics of SetConstrained and Unconstrained Optimization
 Chapter 7: OneDimensional Search Methods
 Chapter 8: Gradient Methods
 Chapter 9: Newton’s Method
 Chapter 10: Conjugate Direction Methods
 Chapter 11: QuasiNewton Methods
 Chapter 12: Solving Linear Equations
 Chapter 13: Unconstrained Optimization and Neural Networks
 Chapter 14: Global Search Algorithms

Part III: Linear Programming

Chapter 15: Introduction to Linear Programming
 15.1 Brief History of Linear Programming
 15.2 Simple Examples of Linear Programs
 15.3 TwoDimensional Linear Programs
 15.4 Convex Polyhedra and Linear Programming
 15.5 Standard Form Linear Programs
 15.6 Basic Solutions
 15.7 Properties of Basic Solutions
 15.8 Geometric View of Linear Programs
 Exercises
 Chapter 16: Simplex Method
 Chapter 17: Duality
 Chapter 18: Nonsimplex Methods
 Chapter 19: Integer Linear Programming

Chapter 15: Introduction to Linear Programming
 Part IV: Nonlinear Constrained Optimization
 References
 Index
Product Information
 Title: An Introduction to Optimization, 4th Edition
 Author(s):
 Release date: January 2013
 Publisher(s): Wiley
 ISBN: 9781118515150