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2000-01-31Buch DOI: 10.18452/8226
Stochastic programming by Monte Carlo simulation methods
dc.contributor.authorShapiro, Alexander
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T19:36:44Z
dc.date.available2017-06-16T19:36:44Z
dc.date.created2005-09-07
dc.date.issued2000-01-31
dc.date.submitted1999-12-10
dc.identifier.urihttp://edoc.hu-berlin.de/18452/8878
dc.description.abstractWe consider in this paper stochastic programming problems which can be formulated as an optimization problem of an expected value function subject to deterministic constraints. We discuss a Monte Carlo simulation approach based on sample average approximations to a numerical solution of such problems. In particular, we give a survey of a statistical inference of the sample average estimators of the optimal value and optimal solutions of the true problem. We also discuss stopping rules and a validation analysis for such sample average approximation optimization procedures and give some illustration examples.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc510 Mathematik
dc.titleStochastic programming by Monte Carlo simulation methods
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10046191
dc.identifier.doihttp://dx.doi.org/10.18452/8226
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.pages30
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2000
local.edoc.container-issue3
local.edoc.container-year2000
local.edoc.container-erstkatid2936317-2

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