Introduction to Derivative-Free Optimization
- Authors:
- Andrew R. Conn, IBM T J Watson Research Center, New York
- Katya Scheinberg, IBM T J Watson Research Center, New York
- Luís N. Vicente, Universidade de Coimbra, Portugal
- Date Published: April 2009
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
- format: Paperback
- isbn: 9780898716689
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The absence of derivatives, often combined with the presence of noise or lack of smoothness, is a major challenge for optimization. This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems. Although readily accessible to readers with a modest background in computational mathematics, it is also intended to be of interest to researchers in the field. Introduction to Derivative-Free Optimization is the first contemporary comprehensive treatment of optimization without derivatives. This book covers most of the relevant classes of algorithms from direct search to model-based approaches. It contains a comprehensive description of the sampling and modeling tools needed for derivative-free optimization; these tools allow the reader to better analyze the convergent properties of the algorithms and identify their differences and similarities.
Read more- Intended for anyone interested in using optimization on problems where derivatives are difficult or impossible to obtain
- Includes a comprehensive description of the sampling and modeling tools needed for derivative-free optimization
- Contains analysis of convergence for modified Nelder–Mead and implicit-filtering methods as well as for model-based methods
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×Product details
- Date Published: April 2009
- format: Paperback
- isbn: 9780898716689
- length: 295 pages
- dimensions: 255 x 178 x 15 mm
- weight: 0.53kg
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
Table of Contents
Preface
1. Introduction
Part I. Sampling and Modeling:
2. Sampling and linear models
3. Interpolating nonlinear models
4. Regression nonlinear models
5. Underdetermined interpolating models
6. Ensuring well poisedness and suitable derivative-free models
Part II. Frameworks and Algorithms:
7. Directional direct-search methods
8. Simplicial direct-search methods
9. Line-search methods based on simplex derivatives
10. Trust-region methods based on derivative-free models
11. Trust-region interpolation-based methods
Part III. Review of Other Topics:
12. Review of surrogate model management
13. Review of constrained and other extensions to derivative-free optimization
Appendix: software for derivative-free optimization
Bibliography
Index.-
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Introduction to Derivative-Free Optimization
Andrew R. Conn, Katya Scheinberg, Luís N. Vicente
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