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Numerical Optimization

By Jorge Nocedal , Stephen Wright

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The new edition of this book presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It is enhanced by new chapters on nonlinear interior methods and derivative-free methods for optimization.

Full Description

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  • ISBN13: 978-0-3873-0303-1
  • 686 Pages
  • Publication Date: December 11, 2006
  • Available eBook Formats: PDF
Full Description
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.
Table of Contents

Table of Contents

  1. Preface.
  2. Preface to the Second Edition.
  3. Introduction.
  4. Fundamentals of Unconstrained Optimization.
  5. Line Search Methods.
  6. Trust
  7. Region Methods.
  8. Conjugate Gradient Methods.
  9. Quasi
  10. Newton Methods.
  11. Large
  12. Scale Unconstrained Optimization.
  13. Calculating Derivatives.
  14. Derivative
  15. Free Optimization.
  16. Least
  17. Squares Problems.
  18. Nonlinear Equations.
  19. Theory of Constrained Optimization.
  20. Linear Programming: The Simplex Method.
  21. Linear Programming: Interior
  22. Point Methods.
  23. Fundamentals of Algorithms for Nonlinear Constrained Optimization.
  24. Quadratic Programming.
  25. Penalty and Augmented Lagrangian Methods.
  26. Sequential Quadratic Programming.
  27. Interior
  28. Point Methods for Nonlinear Programming.
  29. Background Material.
  30. Regularization Procedure.
Errata

If you think that you've found an error in this book, please let us know by emailing to editorial@apress.com . You will find any confirmed erratum below, so you can check if your concern has already been addressed.
No errata are currently published

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