MATHEMATICAL PROGRAMMING

dc.creatorHantoute, Abderrahim
dc.creatorHenrion, Rene
dc.creatorPérez-Aros, Pedro Antonio
dc.date2021-08-23T22:53:24Z
dc.date2022-07-08T20:38:17Z
dc.date2021-08-23T22:53:24Z
dc.date2022-07-08T20:38:17Z
dc.date2019
dc.date.accessioned2023-08-23T00:19:40Z
dc.date.available2023-08-23T00:19:40Z
dc.identifier1151003
dc.identifier1151003
dc.identifierhttps://hdl.handle.net/10533/251168
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8354702
dc.descriptionProbability functions figure prominently in optimization problems of engineering. They may be nonsmooth even if all input data are smooth. This fact motivates the consideration of subdifferentials for such typically just continuous functions. The aim of this paper is to provide subdifferential formulae of such functions in the case of Gaussian distributions for possibly infinite-dimensional decision variables and nonsmooth (locally Lipschitzian) input data. These formulae are based on the spheric-radial decomposition of Gaussian random vectors on the one hand and on a cone of directions of moderate growth on the other. By successively adding additional hypotheses, conditions are satisfied under which the probability function is locally Lipschitzian or even differentiable.
dc.descriptionRegular 2015
dc.descriptionFONDECYT
dc.descriptionFONDECYT
dc.languageeng
dc.relationhandle/10533/111557
dc.relationhandle/10533/111541
dc.relationhandle/10533/108045
dc.relationhttps://doi.org/10.1007/s10107-018-1237-9
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.rightsinfo:eu-repo/semantics/article
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleSubdifferential characterization of probability functions under Gaussian distribution
dc.titleMATHEMATICAL PROGRAMMING
dc.typeArticulo
dc.typeinfo:eu-repo/semantics/publishedVersion


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