Edwin K. P. Chong & Stanislaw H. Zak 
An Introduction to Optimization [PDF ebook] 

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.

An Instructor’s Manual presenting detailed solutions to all the
problems in the book is available from the Wiley editorial
department.

Tabla de materias

Preface. xiii

PART I MATHEMATICAL REVIEW

Methods of Proof and Some Notation 1

Vector Spaces and Matrices 5

Transformations 21

Concepts from Geometry 39

Elements of Calculus 49

Part II UNCONSTRAINED OPTIMIZATION

Basics of Set-Constrained and Unconstrained Optimization 73

One-Dimensional Search Methods 91

Gradient Methods 113

Newton’s Method 139

Conjugate Direction Methods 151

Quasi-Newton Methods 167

Solving Ax = b 187

Unconstrained Optimization and Neural Networks 219

Genetic Algorithms 237

Part III LINEAR PROGRAMMING

Introduction to Linear Programming. 255

Simplex Method 287

Duality 321

Non-Simplex Methods 339

Part IV NONLINEAR CONSTRAINED OPTIMIZATION

Problems with Equality Constraints 365

Problems with Inequality Constraints 397

Convex Optimization Problems 417

Algorithms for Constrained Optimization 439

References 455

Index 462

Sobre el autor

EDWIN K. P. CHONG, Ph D, 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, Ph D, 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.