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2010-10-19Buch DOI: 10.18452/3038
On joint probabilistic constraints with Gaussian coefficient matrix
dc.contributor.authorAckooij, W. van
dc.contributor.authorHenrion, R.
dc.contributor.authorMöller, A.
dc.contributor.authorZorgati, R.
dc.contributor.editorHigle, Julie L.
dc.contributor.editorRömisch, Werner
dc.contributor.editorSen, Surrajeet
dc.date.accessioned2017-06-15T19:19:25Z
dc.date.available2017-06-15T19:19:25Z
dc.date.created2010-10-19
dc.date.issued2010-10-19none
dc.date.submitted2010-10-09
dc.identifier.otherhttp://edoc.hu-berlin.de/series/speps/2010-6/PDF/6.pdf
dc.identifier.urihttp://edoc.hu-berlin.de/18452/3690
dc.description.abstractThe paper deals with joint probabilistic constraints defined by a Gaussian coefficient matrix. It is shown how to explicitly reduce the computation of values and gradients of the underlying probability function to that of Gaussian distribution functions. This allows to employ existing efficient algorithms for calculating this latter class of function in order to solve probabilistically constrained optimization problems of the indicated type. Results are illustrated by an example from energy production.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik
dc.relation.ispartofseriesStochastic Programming E-Print Series - 6, SPEPS
dc.subject.ddc510 Mathematik
dc.titleOn joint probabilistic constraints with Gaussian coefficient matrix
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100176216
dc.identifier.doihttp://dx.doi.org/10.18452/3038
local.edoc.container-titleStochastic Programming E-Print Series
local.edoc.container-titleSPEPS
local.edoc.pages7
local.z-edoc.journal-periodikumAusgabe6,
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2010
local.edoc.container-issue6

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