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.
विषयसूची
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
लेखक के बारे में
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.
एक ही लेखक से अधिक ईबुक / संपादक
49,025 इस श्रेणी में ईबुक
