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

L. Jeff Hong (), Yi Yang () and Liwei Zhang ()
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L. Jeff Hong: Department of Industrial Engineering and Logistics Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
Yi Yang: Department of Computer Science, University of California, Irvine, Irvine, California 92617
Liwei Zhang: School of Mathematical Science, Dalian University of Technology, Dalian 116024, China

Operations Research, 2011, vol. 59, issue 3, 617-630

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.

Keywords: programming; stochastic; chance constrained program (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (29)

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