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论文类型：期刊论文
发表时间：2011-05-01
发表刊物：OPERATIONS RESEARCH
收录刊物：SCIE、EI、SSCI、Scopus
卷号：59
期号：3
页面范围：617-630
ISSN号：0030-364X
摘要：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.