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2012-04-09Buch DOI: 10.18452/8424
SDDP for multistage stochastic linear programs based on spectral risk measures
dc.contributor.authorGuiges, Vincent
dc.contributor.authorRömisch, Werner
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T20:26:46Z
dc.date.available2017-06-16T20:26:46Z
dc.date.created2012-04-11
dc.date.issued2012-04-09
dc.identifier.urihttp://edoc.hu-berlin.de/18452/9076
dc.description.abstractWe consider risk-averse formulations of multistage stochastic linear programs. Forthese formulations, based on convex combinations of spectral risk measures, risk-averse dynamicprogramming equations can be written. As a result, the Stochastic Dual Dynamic Programming(SDDP) algorithm can be used to obtain approximations of the corresponding risk-averse recoursefunctions. This allows us to define a risk-averse nonanticipative feasible policy for the stochasticlinear program. Formulas for the cuts that approximate the recourse functions are given.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.subjectstochastic programmingeng
dc.subjectMonte-Carlo samplingeng
dc.subjectspectral risk measureeng
dc.subjectrisk-averse optimizationeng
dc.subjectdecomposition algorithmseng
dc.subject.ddc510 Mathematik
dc.titleSDDP for multistage stochastic linear programs based on spectral risk measures
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100201028
dc.identifier.doihttp://dx.doi.org/10.18452/8424
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.pages10
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2012
local.edoc.container-issue4
local.edoc.container-year2012
local.edoc.container-erstkatid2936317-2

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