Title
Subdifferential characterization of probability functions under Gaussian distribution
Abstract
Probability 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.
Year
DOI
Venue
2019
10.1007/s10107-018-1237-9
Mathematical Programming
Keywords
Field
DocType
Probability functions,Probabilistic constraint,Stochastic optimization,Multivariate Gaussian distribution,Spheric-radial decomposition,Clarke subdifferential,Mordukhovich subdifferential,90C15,90C30,49J52,49J53
Discrete mathematics,Mathematical optimization,Inverse Gaussian distribution,Gaussian random field,Generalized inverse Gaussian distribution,Subderivative,Probability distribution,Gaussian process,Normal-inverse Gaussian distribution,Probability density function,Mathematics
Journal
Volume
Issue
ISSN
174.0
SP1-2
1436-4646
Citations 
PageRank 
References 
0
0.34
4
Authors
3
Name
Order
Citations
PageRank
Abderrahim Hantoute1226.09
René Henrion230529.65
Pedro Pérez-Aros300.34