Articoli correlati a Introduction to Derivative-Free Optimization
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The first contemporary comprehensive treatment of optimization without derivatives.
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Informazioni sugli autori
Andrew R. Conn is a research staff member at the IBM T. J. Watson Research Center, Yorktown Heights, NY. In 1994 he was (with N. I. M. Gould and Ph. L. Toint) a joint recipient of the Beale/Orchard-Hays Prize for Computational Excellence in Mathematical Programming and with Chandu Visweswariah he received an IBM Corporate Award in 2002 for contributions to circuit tuning. Currently his 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 T. J. Watson Research Center. She obtained her PhD in 1997 from Columbia University in New York. She has been working in the area of derivative-free optimization for over ten years and is the author of multiple papers on the subject as well as the open source widely known DFO software.
Luis Nunes Vicente is a Professor of Mathematics at the University of Coimbra, Portugal. He obtained his PhD from Rice University, TX in 1996 under a Fulbright scholarship and was among the three finalists of the 94-96 A. W. Tucker Prize of the Mathematical Programming Society. His research has been strongly supported by the European Union and the European Space Agency. He is a member of several editorial boards including SIAM Journal on Optimization and Journal of Global Optimization and he recently ended a six year term as editor of the SIAM SIAG/Optimization Views-and-News.
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Introduction to Derivative-Free Optimization
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Society for Industrial and Applied Mathematics (SIAM), 2009
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ISBN 13: 9780898716689
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Introduction to Derivative-Free Optimization (Mps-siam Series on Optimization)
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Society for Industrial and Applied Mathematics, 2009
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Paperback. Condizione: New. 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 understand the convergent properties of the algorithms and identify their differences and similarities. Introduction to Derivative-Free Optimization also contains 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. Codice articolo LU-9780898716689
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Introduction to Derivative-Free Optimization
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Introduction to Derivative-Free Optimization (Mps-siam Series on Optimization)
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Society for Industrial and Applied Mathematics, 2009
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ISBN 13: 9780898716689
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Introduction to Derivative-Free Optimization (Mps-Siam Series on Optimization)
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Society for Industrial and Applied Mathematics, 2009
ISBN 10: 0898716683
ISBN 13: 9780898716689
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