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2002-11-05Buch DOI: 10.18452/8282
Learning algorithms for separable approximations of stochastic optimization problems
dc.contributor.authorPowell, Warren
dc.contributor.authorRuszczynski, Andrzej
dc.contributor.authorTopaloglu, Huseyin
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-16T19:49:39Z
dc.date.available2017-06-16T19:49:39Z
dc.date.created2006-02-22
dc.date.issued2002-11-05
dc.date.submitted2002-10-30
dc.identifier.urihttp://edoc.hu-berlin.de/18452/8934
dc.description.abstractWe propose the use of sequences of separable, piecewise linear approximations for solving classes of nondiffferential stochastic optimization problems. The approximations are estimated adaptively using a combination of stochastic gradient information and possibly sample information on the objective function itself. We prove the convergence of several versions of such methods when the objective function is separable and illustrate their behavior on numerical examples. We then demonstrate the preformance on nonseparable problems that arise in the context of two-stage stochastic programming problems, and demonstrate that these techniques provide near optimal solutions with a very fast rate of convergence compared to other solution techniques.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.subject.ddc510 Mathematik
dc.titleLearning algorithms for separable approximations of stochastic optimization problems
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10058600
dc.identifier.urnurn:nbn:de:kobv:11-10058611
dc.identifier.doihttp://dx.doi.org/10.18452/8282
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.pages35
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2002
local.edoc.container-issue18
local.edoc.container-erstkatid2936317-2

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