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Mos-Siam Series on Optimization Ser.: Introduction to Derivative-Free Optimization by Luís N. Vicente, Andrew R. Conn and Katya Scheinberg (2008, Trade Paperback)

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By Conn, Andrew R.; Scheinberg, Katya; Vicente, Luís N.

About this product

Product Identifiers

PublisherSociety for Industrial AND Applied Mathematics

ISBN-100898716683

ISBN-139780898716689

eBay Product ID (ePID)109233145

Product Key Features

Number of Pages290 Pages

LanguageEnglish

Publication NameIntroduction to Derivative-Free Optimization

Publication Year2008

SubjectIndustrial Engineering, Engineering (General), Linear & Nonlinear Programming, General, Optimization, Mathematical Analysis

TypeTextbook

Subject AreaMathematics, Technology & Engineering

AuthorLuís N. Vicente, Andrew R. Conn, Katya Scheinberg

SeriesMos-Siam Series on Optimization Ser.

FormatTrade Paperback

Dimensions

Item Height0.8 in

Item Weight18.6 Oz

Item Length9 in

Item Width6 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2008-038005

Dewey Edition22

IllustratedYes

Dewey Decimal519.6

Table Of ContentPreface Chapter 1: Introduction Part I: Sampling and modeling Chapter 2: Sampling and linear models Chapter 3: Interpolating nonlinear models Chapter 4: Regression nonlinear models Chapter 5: Underdetermined interpolating models Chapter 6: Ensuring well poisedness and suitable derivative-free models Part II: Frameworks and algorithms Chapter 7: Directional direct-search methods Chapter 8: Simplicial direct-search methods Chapter 9: Line-search methods based on simplex derivatives Chapter 10: Trust-region methods based on derivative-free models Chapter 11: Trust-region interpolation-based methods Part III: Review of other topics Chapter 12: Review of surrogate model management Chapter 13: Review of constrained and other extensions to derivative-free optimization Appendix: Software for derivative-free optimization Bibliography Index.

SynopsisThe 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., 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., 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.

LC Classification NumberTA342 .C67 2009