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Other titles in the Springer Optimization and Its Applications series:

Springer Optimization and Its Applications #9: Stochastic Global Optimization

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Synopses & Reviews

Publisher Comments:

This book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; the topics include the basic principles and methods of global random search, statistical inference in random search, Markovian and population-based random search methods, methods based on statistical models of multimodal functions and principles of rational decisions theory. Key features: * Inspires readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods; * Includes a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms; * Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and population based algorithms; *Provides a thorough description of the methods based on statistical models of objective function; *Discusses criteria for evaluating efficiency of optimization algorithms and difficulties occurring in applied global optimization. Stochastic Global Optimization is intended for mature researchers and graduate students interested in global optimization, operations research, computer science, probability, statistics, computational and applied mathematics, mechanical and chemical engineering, and many other fields where methods of global optimization can be used.

Synopsis:

This book examines the main methodological and theoretical developments in stochastic global optimization. It is designed to inspire readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods. Among the book's features is a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms.

Synopsis:

This book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; some of the topics include an introduction to global random search, statistical inference, several associated random search algorithms, and various approaches to statistical models, i.e., using a P-algorithm for a model with derivatives.

Key features:

* Inspires readers to explore several stochastic search optimization methods by presenting distinct applications;

* Includes an extensive discussion on probabilistic and statistical models used in global random search;

* Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and population based algorithms;

* Provides an extensive bibliography of classic Russian references as well as recent works.

Stochastic Global Optimization is intended for mathematicians, graduate students, researchers and engineers interested in global optimization, operations research, probability, statistics and mechanical and chemical engineering.

Table of Contents

Preface.- Introduction.- Basic Concepts and Ideas.- Global Random Search: Fundamentals and Statistical Inference.- Global Random Search: Extensions.- Statistical Models.- References.- Index.

Product Details

ISBN:
9780387740225
Author:
Zhigljavsky, Anatoly
Publisher:
Springer
Author:
Zilinskas, Antanasz
Subject:
Linear Programming
Subject:
Applied
Subject:
population-based methods
Subject:
random search
Subject:
statistical inference about minimum
Subject:
stochastic global optimization
Subject:
stochastic models about objective function
Subject:
OPTIMIZATION
Subject:
Probability Theory and Stochastic Processes
Subject:
Statistical Theory and Methods <P>Provides reader with a methodological and theoretical basis for developing and investigating optimization heuristics</P> <P>Summarizes basic ideas and presents recent progress and new results</P> <P>Includes an extensive
Subject:
Mathematics-Computer
Subject:
Statistical Theory and Methods
Copyright:
Edition Description:
Book
Series:
Springer Optimization and Its Applications
Series Volume:
9
Publication Date:
20071131
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
272
Dimensions:
235 x 155 mm 1250 gr

Related Subjects

Health and Self-Help » Health and Medicine » Medical Specialties
Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Computer
Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Springer Optimization and Its Applications #9: Stochastic Global Optimization New Hardcover
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$129.00 In Stock
Product details 272 pages Springer - English 9780387740225 Reviews:
"Synopsis" by , This book examines the main methodological and theoretical developments in stochastic global optimization. It is designed to inspire readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods. Among the book's features is a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms.
"Synopsis" by , This book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; some of the topics include an introduction to global random search, statistical inference, several associated random search algorithms, and various approaches to statistical models, i.e., using a P-algorithm for a model with derivatives.

Key features:

* Inspires readers to explore several stochastic search optimization methods by presenting distinct applications;

* Includes an extensive discussion on probabilistic and statistical models used in global random search;

* Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and population based algorithms;

* Provides an extensive bibliography of classic Russian references as well as recent works.

Stochastic Global Optimization is intended for mathematicians, graduate students, researchers and engineers interested in global optimization, operations research, probability, statistics and mechanical and chemical engineering.

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