Stochastic Gradient Estimation

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dc.contributor.authorFu, Michael C.
dc.date.accessioned2005-07-01T12:31:02Z
dc.date.available2005-07-01T12:31:02Z
dc.date.issued2005-07-01T12:31:02Z
dc.descriptionThis is a pre-print version of Chapter 19 in Handbooks in Operations Research and Management Science: Simulation, S.G. Henderson and B.L. Nelson, eds., Elsevier.en
dc.description.abstractWe consider the problem of efficiently estimating gradients from stochastic simulation. Although the primary motivation is their use in simulation optimization, the resulting estimators can also be useful in other ways, e.g., sensitivity analysis. The main approaches described are finite differences (including simultaneous perturbations), perturbation analysis, the likelihood ratio/score function method, and the use of weak derivatives.en
dc.description.sponsorshipThis work was supported in part by the National Science Foundation under Grants DMI 9988867 and DMI 0323220, and by the Air Force Office of Scientific Research under Grants F496200110161 and FA95500410210.en
dc.format.extent303248 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/2298
dc.language.isoen_US
dc.relation.isAvailableAtRobert H. Smith School of Businessen_us
dc.relation.isAvailableAtDecision & Information Technologiesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_us
dc.subjectsimulationen
dc.subjectgradient estimationen
dc.subjectperturbation analysisen
dc.subjectlikelihood ratio methoden
dc.subjectweak derivativesen
dc.titleStochastic Gradient Estimationen
dc.typeBook chapteren

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