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2005-11-04Buch DOI: 10.18452/2633
Scenario Reduction Algorithms in Stochastic Programming
dc.contributor.authorHeitsch, Holger
dc.contributor.authorRömisch, Werner
dc.date.accessioned2017-06-15T17:48:19Z
dc.date.available2017-06-15T17:48:19Z
dc.date.created2005-11-04
dc.date.issued2005-11-04
dc.identifier.issn0863-0976
dc.identifier.urihttp://edoc.hu-berlin.de/18452/3285
dc.description.abstractWe consider convex stochastic programs with an (approximate) initial probability distribution P having finite support supp P, i.e., finitely many scenarios. Such stochastic programs behave stable with respect to perturbations of P measured in terms of a Fortet-Mourier probability metric. The problem of optimal scenario reduction consists in determining a probability measure which is supported by a subset of supp P of prescribed cardinality and is closest to P in terms of such a probability metric. Two new versions of forward and backward type algorithms are presented for computing such optimally reduced probability measures approximately. Compared to earlier versions, the computational performance (accuracy, running time) of the new algorithms is considerably improved. Numerical experience is reported for different instances of scenario trees with computable optimal lower bounds. The test examples also include a ternary scenario tree representing the weekly electrical load process in a power management model.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.subjectStochastic programmingeng
dc.subjectprobability metriceng
dc.subjectscenario reductioneng
dc.subjectscenario treeeng
dc.subjectelectrical loadeng
dc.subject.ddc510 Mathematik
dc.titleScenario Reduction Algorithms in Stochastic Programming
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10052679
dc.identifier.doihttp://dx.doi.org/10.18452/2633
dc.subject.dnb27 Mathematik
local.edoc.container-titlePreprints aus dem Institut für Mathematik
local.edoc.pages54
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2001
local.edoc.container-issue8
local.edoc.container-year2001
local.edoc.container-erstkatid2075199-0

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