Sequential convex approximations to joint chance constrained programs : A Monte Carlo approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)617-630
Journal / PublicationOperations Research
Volume59
Issue number3
StatePublished - May 2011
Externally publishedYes

Abstract

When there is parameter uncertainty in the constraints of a convex optimization problem, it is natural to formulate the problem as a joint chance constrained program (JCCP), which requires that all constraints be satisfied simultaneously with a given large probability. In this paper, we propose to solve the JCCP by a sequence of convex approximations. We show that the solutions of the sequence of approximations converge to a Karush-Kuhn-Tucker (KKT) point of the JCCP under a certain asymptotic regime. Furthermore, we propose to use a gradient-based Monte Carlo method to solve the sequence of convex approximations. © 2011 INFORMS.

Research Area(s)

  • Programming, Stochastic: chance constrained program

Citation Format(s)

Sequential convex approximations to joint chance constrained programs : A Monte Carlo approach. / Hong, L. Jeff; Yang, Yi; Zhang, Liwei.

In: Operations Research, Vol. 59, No. 3, 05.2011, p. 617-630.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review