On joint probabilistic constraints with Gaussian coefficient matrix

W. van Ackooij; R. Henrion; A. Möller; R. Zorgati

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SPEPS Preprint

Author(s):	W. van Ackooij, Electricité de France R&D R. Henrion, WIAS Berlin A. Möller, WIAS Berlin R. Zorgati, Electricité de France R&D
Title:	On joint probabilistic constraints with Gaussian coefficient matrix
Date of Acceptance:	19.10.2010
Submission Date:	09.10.2010
Series Title:	Stochastic Programming E-Print Series (SPEPS)
Editors:	Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint:	pdf (urn:nbn:de:kobv:11-100176216)
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Abstract (eng):

The 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.

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