Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach

Indexed by:期刊论文

Journal:OPERATIONS RESEARCH

Included Journals:SCIE、EI、SSCI、Scopus

Volume:59

Issue:3

Page Number:617-630

ISSN No.:0030-364X

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.

Date of Publication:2011-05-01