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2017-09-26Buch DOI: 10.18452/18407
A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming
dc.contributor.authorFábián, Csaba I.
dc.contributor.authorSzántai, Tamás
dc.date.accessioned2017-09-26T14:08:56Z
dc.date.available2017-09-26T14:08:56Z
dc.date.issued2017-09-26
dc.identifier.urihttp://edoc.hu-berlin.de/18452/19084
dc.description.abstractWe propose a randomized gradient method for the handling of a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic approximation, the present method builds a model problem. The approach requires that estimates of function values and gradients be provided at the iterates. We present a variance reduction Monte Carlo simulation procedure to provide such estimates in the case of certain probabilistic functions.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectConvex optimizationeng
dc.subjectStochastic optimizationeng
dc.subjectProbabilistic problemseng
dc.subject.ddc510 Mathematik
dc.titleA randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/19084-9
dc.identifier.doihttp://dx.doi.org/10.18452/18407
local.edoc.pages18
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
dc.identifier.zdb2936317-2
bua.series.nameStochastic Programming E-Print Series
bua.series.issuenumber2017,5
bua.departmentMathematisch-Naturwissenschaftliche Fakultät

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