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2013-04-09Buch DOI: 10.18452/8433
Computational aspects of risk-averse optimizationin two-stage stochastic models
dc.contributor.authorFábián, Csaba I.
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T20:29:17Z
dc.date.available2017-06-16T20:29:17Z
dc.date.created2013-04-12
dc.date.issued2013-04-09
dc.date.submitted2013-02-12
dc.identifier.urihttp://edoc.hu-berlin.de/18452/9085
dc.description.abstractComputational studies on two-stage stochastic programming problems indicate that aggregate models have better scale-up properties than disaggregate ones, though the threshold of breaking even may be high. In this paper we attempt to explain this phenomenon, and to lower this threshold.We present the on-demand accuracy approach of Oliveira and Sagastizábal in a form which shows that this approach, when applied to two-stage stochastic programming problems, combines the advantages of the disaggregate and the aggregate models.Moreover, we generalize the on-demand accuracy approach to constrained convex problems, and showhow to apply it to risk-averse two-stage stochastic programming problems.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.subjectlinear programmingeng
dc.subjectstochastic programmingeng
dc.subjectrisk-averse modelseng
dc.subjectconvex programmingeng
dc.subjectcutting-plane methodseng
dc.subjectsimplex methodeng
dc.subject.ddc510 Mathematik
dc.titleComputational aspects of risk-averse optimizationin two-stage stochastic models
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100208567
dc.identifier.doihttp://dx.doi.org/10.18452/8433
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.pages27
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2013
local.edoc.container-issue3
local.edoc.container-erstkatid2936317-2

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