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Stochastic Optimization Simulation Based by Wan Xiaotao (8 results)
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Condition: As New. Unread book in perfect condition.
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Stochastic Optimization with Simulation Based Optimization: A Surrogate Model Framework
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
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Stochastic Optimization With Simulation Based Optimization : A Surrogate Model Framework
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Stochastic Optimization With Simulation Based Optimization : A Surrogate Model Framework
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: As New. Unread book in perfect condition.
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Stochastic Optimization with Simulation Based Optimization A Surrogate Model Framework
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
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Stochastic Optimization with Simulation Based Optimization : A Surrogate Model Framework
Language: English
Published by VDM Verlag Dr. Müller, 2009
ISBN 10: 363914015X ISBN 13: 9783639140156
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Stochastic optimization is vital to making soundengineering and business decisions under uncertainty.While the limited capability of handling complexdomain structures and random variables rendersanalytic methods helpless in many circumstances,stochastic optimization based on simulation is widelyapplicable. This work extends the traditionalresponse surface methodology into a surrogate modelframework to address high dimensional stochasticproblems. The framework integrates Latin hypercubesampling (LHS), domain reduction techniques, leastsquare support vector machine (LSSVM) and design &analysis of computer experiment (DACE) to buildsurrogate models that effectively captures domainstructures. In comparison with existing simulationbased optimization methods, the proposed frameworkleads to better solutions especially for problemswith high dimensions and high uncertainty. Thesurrogate model framework also demonstrates thecapability of addressing the curse-of-dimensionalityin stochastic dynamic risk optimization problems,where several important modification of the classicalBellman equation for stochastic dynamic problems(SDP) is also proposed.
