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Condition: As New. Unread book in perfect condition.
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems (Paperback or Softback)
Language: English
Published by Biblioscholar 10/9/2012, 2012
ISBN 10: 1249592526 ISBN 13: 9781249592525
Paperback or Softback. Condition: New. Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems. Book.
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Condition: As New. Unread book in perfect condition.
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
US$ 74.47
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Language: English
Published by BiblioScholar 2012-10, 2012
ISBN 10: 1249592526 ISBN 13: 9781249592525
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Language: English
Published by Creative Media Partners, LLC Mai 2025, 2025
ISBN 10: 1025092457 ISBN 13: 9781025092454
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Condition: New. KlappentextrnrnA new class of algorithms is introduced and analyzed for bound and linearly constrained optimization problems with stochastic objective functions and a mixture of design variable types. The generalized pattern search (GPS) class o.
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Language: English
Published by Creative Media Partners, LLC Mai 2025, 2025
ISBN 10: 1025088670 ISBN 13: 9781025088679
Buch. Condition: Neu. Neuware.
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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems
Language: English
Published by Creative Media Partners, LLC Okt 2012, 2012
ISBN 10: 1249592526 ISBN 13: 9781249592525
Taschenbuch. Condition: Neu. Neuware - A new class of algorithms is introduced and analyzed for bound and linearly constrained optimization problems with stochastic objective functions and a mixture of design variable types. The generalized pattern search (GPS) class of algorithms is extended to a new problem setting in which objective function evaluations require sampling from a model of a stochastic system. The approach combines GPS with ranking and selection (RS) statistical procedures to select new iterates. The derivative-free algorithms require only black-box simulation responses andare applicable over domains withmixedvariables (continuous, discrete numeric, and discrete categorical)to include bound and linear constraints on the continuous variables. A convergence analysis for the general class of algorithms establishes almost sure convergence of an iteration subsequence to stationary points appropriately defined in the mixed-variable domain. Additionally, specific algorithm instances are implemented that provide computational enhancements to the basic algorithm. Implementation alternatives include the use of modern RS procedures designed to provide efficientsamplingstrategies andthe use of surrogate functions that augment the search by approximating the unknown objective function with nonparametric response surfaces. In a computational evaluation, six variants of the algorithm are tested along with four competing methods on 26 standardized test problems. The numerical results validate the use of advanced implementations as a means to improve algorithm performance.
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Condition: New. Print on Demand.
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
