Numerical Optimization / Edition 1

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More About This Textbook

Overview

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.

For this new edition the book has been updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous-one that reveals both the beautiful nature of the discipline and its practical side.

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Product Details

  • ISBN-13: 9780387987934
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 4/28/2000
  • Series: Series in Operations Research
  • Edition description: Older Edition
  • Edition number: 1
  • Pages: 636
  • Product dimensions: 7.31 (w) x 9.48 (h) x 1.46 (d)

Table of Contents

1 Introduction 1
2 Fundamentals of unconstrained optimization 10
3 Line search methods 30
4 Trust-region methods 66
5 Conjugate gradient methods 101
6 Quasi-Newton methods 135
7 Large-scale unconstrained optimization 164
8 Calculating derivatives 193
9 Derivative-free optimization 220
10 Least-squares problems 245
11 Nonlinear equations 270
12 Theory of constrained optimization 304
13 Linear programming : the simplex method 355
14 Linear programming : interior-point methods 392
15 Fundamentals of algorithms for nonlinear constrained optimization 421
16 Quadratic programming 448
17 Penalty and augmented Lagrangian methods 497
18 Sequential quadratic programming 529
19 Interior-point methods for nonlinear programming 563
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Customer Reviews

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Sort by: Showing all of 2 Customer Reviews

  • Posted July 5, 2011

    One must-see book

    This book presents very clear pictures of fundamental theory and algorithms in numerical optimization. I especially like the way that it universally put Newton, Quazi-Newton, Conjugate Gradient methods into single framework and explains them in a very intuitive-friend way. To those who want to learn numerical optimization, I can only say: Read the Book.

    Was this review helpful? Yes  NoThank you for your feedback.   Report this reviewThank you, this review has been flagged.

  • Anonymous

    Posted April 15, 2004

    This is a poorly organized book.

    I find this book to be nearly useless. The authors do not summarize algorithms nor do they present them clearly. One has to hunt through multiple chapters to understand a single algorithm. If one wants to understand an algorithm from chapter 10, one will have to go back and read several previous chapters such as chapters 3 & 4 due to the authors presentation style. An algorithm will be loosely introduced in an early chapter and then forward referenced to a much later chapter. The authors presentation style is to introduce all algorithms at a level too vague to be useful to any mathematical scientist or engineer in the first half of the book and then follow up with an incoherent presentation in the second half of the book. This book is a poor text book and a worse reference book. I do not recommend this book.

    Was this review helpful? Yes  NoThank you for your feedback.   Report this reviewThank you, this review has been flagged.
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