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 thoroughly 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.
There is a selected solutions manual for instructors for the new edition.
Author(s): Jorge Nocedal
Series: Series in Operations Research and Financial Engineering
Edition: 2nd
Publisher: Springer
Year: 2006
Language: English
Pages: 683
Title......Page 1
Contents......Page 4
Preface......Page 14
Preface to the Second Edition......Page 18
01 Introduction......Page 20
02 Fundamentals of Unconstrained Optimization......Page 29
03 Line Search Methods......Page 49
04 Trust-Region Methods......Page 85
05 Conjugate Gradient Methods......Page 120
06 Quasi-Newton Methods......Page 154
07 Large-Scale Unconstrained Optimization......Page 183
08 Calculating Derivatives......Page 212
09 Derivative-Free Optimization......Page 239
10 Least-Squares Problems......Page 264
11 Nonlinear Equations......Page 289
12 Theory of Constrained Optimization......Page 323
13 Linear Programming: The Simplex Method......Page 374
14 Linear Programming: Interior-Point Methods......Page 411
15 Fundamentals of Algorithms for Nonlinear Constrained Optimization......Page 440
16 Quadratic Programming......Page 467
17 Penalty and Augmented Lagrangian Methods......Page 516
18 Sequential Quadratic Programming......Page 548
19 Interior-Point Methods for Nonlinear Programming......Page 582
Appendix A. Background Material......Page 617
Appendix B. A Regularization Procedure......Page 654
References......Page 656
Index......Page 672
