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Numerical Optimization

  • Textbook
  • © 1999

Overview

Editors:
  1. Jorge Nocedal

    1. ECE Department, Northwestern University, Evanston, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Stephen J. Wright

    1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, USA

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    You can also search for this editor in PubMed   Google Scholar

  • A comprehensive and up-to-date description of the most effective methods in continuous optimization
  • Responds to the growing interest in optimization in engineering, science, and business
  • Updated throughout with new chapters on nonlinear interior methods and derivative-free methods for optimization
  • Authors have produced a text that is informative, rigorous and pleasant to read
  • There is a selected solutions manual for instructors for the new edition

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xx
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  2. Introduction

    Pages 1-9

  3. Fundamentals of Unconstrained Optimization

    Pages 10-32

  4. Line Search Methods

    Pages 34-63

  5. Trust-Region Methods

    Pages 64-99

  6. Conjugate Gradient Methods

    Pages 100-133

  7. Practical Newton Methods

    Pages 134-163

  8. Calculating Derivatives

    Pages 164-191

  9. Quasi-Newton Methods

    Pages 192-221

  10. Large-Scale Quasi-Newton and Partially Separable Optimization

    Pages 222-249

  11. Nonlinear Least-Squares Problems

    Pages 250-275

  12. Nonlinear Equations

    Pages 276-312

  13. Theory of Constrained Optimization

    Pages 314-359

  14. Linear Programming: The Simplex Method

    Pages 360-391

  15. Linear Programming: Interior-Point Methods

    Pages 392-417

  16. Fundamentals of Algorithms for Nonlinear Constrained Optimization

    Pages 418-437

  17. Quadratic Programming

    Pages 438-486

  18. Penalty, Barrier, and Augmented Lagrangian Methods

    Pages 488-525

  19. Sequential Quadratic Programming

    Pages 526-573

  20. Back Matter

    Pages 574-634
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Keywords

About this book

This is a book for people interested in solving optimization problems. Because of the wide (and growing) use of optimization in science, engineering, economics, and industry, it is essential for students and practitioners alike to develop an understanding of optimization algorithms. Knowledge of the capabilities and limitations of these algorithms leads to a better understanding of their impact on various applications, and points the way to future research on improving and extending optimization algorithms and software. Our goal in this book is to give a comprehensive description of the most powerful, state-of-the-art, techniques for solving continuous optimization problems. By presenting the motivating ideas for each algorithm, we try to stimulate the reader’s intuition and make the technical details easier to follow. Formal mathematical requirements are kept to a minimum. Because of our focus on continuous problems, we have omitted discussion of important optimization topics such as discrete and stochastic optimization.

Reviews

MMOR Mathematical Methods of Operations Research, 2001: "The books looks very suitable to be used in an graduate-level course in optimization for students in mathematics, operations research, engineering, and others. Moreover, it seems to be very helpful to do some self-studies in optimization, to complete own knowledge and can be a source of new ideas.... I recommend this excellent book to everyone who is interested in optimization problems."

Editors and Affiliations

  • ECE Department, Northwestern University, Evanston, USA

    Jorge Nocedal

  • Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, USA

    Stephen J. Wright

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