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Introduction to Derivative-Free OptimizationIntroduction to Derivative-Free Optimization

Andrew R. Conn, Katya Scheinberg, and Luis N. Vicente



MOS-SIAM Series on Optimization 8

This book is the first contemporary comprehensive treatment of optimization without derivatives, and it covers most of the relevant classes of algorithms from direct-search to model-based approaches. Readily accessible to readers with a modest background in computational mathematics, Introduction to Derivative-Free Optimization contains:

• a comprehensive description of the sampling and modeling tools needed for derivative-free optimization that allow the reader to better understand the convergent properties of the algorithms and identify their differences and similarities;

• analysis of convergence for modified Nelder–Mead and implicit-filtering methods as well as for model-based methods such as wedge methods and methods based on minimum–norm Frobenius models.


Audience
The book is intended for anyone interested in using optimization on problems where derivatives are difficult or impossible to obtain. Such audiences include chemical, mechanical, aeronautical, and electrical engineers, as well as economists, statisticians, operations researchers, management scientists, biological and medical researchers, and computer scientists. It is also appropriate for use in an advanced undergraduate or early graduate-level course on optimization for students having a background in calculus, linear algebra, and numerical analysis.

Contents
Preface
Index
SIREV Book Review

About the Authors
Andrew R. Conn is a Research Staff Member at the IBM Thomas J. Watson Research Center in Yorktown Heights, New York. He is an honorary visiting professor in Veszprém, Hungary. His current major application projects are in the petroleum industry.

Katya Scheinberg is a Research Staff Member in the Business Analytics and Mathematical Sciences Department at the IBM Thomas J. Watson Research Center in Yorktown Heights, New York. She has been working in the area of derivative-free optimization for over 10 years and is the author of multiple papers on the subject as well as the widely known open-source DFO software.

Luis Nunes Vicente is a Professor of Mathematics at the University of Coimbra, Portugal. His research interests include the development and analysis of numerical methods for large-scale nonlinear programming and derivative-free optimization problems, and applications in sciences, engineering, and finance. He serves as associate editor of the SIAM Journal on Optimization and the Journal of Global Optimization.

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Keywords
derivative-free optimization; sampling modeling; algorithms; global convergence

2009 / xii + 277 pages / Softcover / ISBN: 978-0-898716-68-9 
List Price $77.50 / MOS/SIAM Member Price $54.25 / Order Code MP08
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