An Introduction to Optimization
-
Wiley-Interscience Series in Discrete Mathematics and Optimization
John Wiley & Sons, Inc.
New York
Copyright © 1996
ISBN 0-471-08949-4, xiii+409 pp.
From the back cover:
An up-to-date, accessible introduction to an increasingly important
field
This timely 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 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
neural networks
- Exercise problems after every chapter
- MATLAB exercises and examples
- An optional solutions manual with MATLAB source listings
(Instructors only: To obtain a copy of the solutions manual, see
ordering information
below.)
This book helps students to 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.
Errata
An up-to-date errata is available, in
Postscript and
PDF formats.
Brief Table of Contents
(A more detailed table of contents is available.)
- Preface
Part I. Mathematical Review
- 1 Methods of Proof and Some Notation
- 2 Real Vector Spaces and Matrices
- 3 Transformations
- 4 Concepts from Geometry
- 5 Elements of Differential Calculus
Part II. Unconstrained Optimization
- 6 Basics of Unconstrained Optimization
- 7 One-Dimensional Search Methods
- 8 Gradient Methods
- 9 Newton's Method
- 10 Conjugate Direction Methods
- 11 Quasi-Newton Methods
- 12 Solving Ax=b
- 13 Unconstrained Optimization and Feedforward Neural Networks
- 14 Genetic Algorithms
Part III. Linear Programming
- 15 Introduction to Linear Programming
- 16 The Simplex Method
- 17 Duality
- 18 Non-Simplex 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
- Bibliography
- Index
Ordering information
Wiley has
information on how to order the book.
Instructors only:
Copies of the solutions manuals are held in-house at
Wiley's New York office.
For a copy of the solutions manual, fax an official request
letter on
university letterhead to 201-748-6825, or contact
Sari Friedman (sfriedman@wiley.com)
Professor Edwin Chong,
This document was last modified
September 09, 2020.