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论文类型：期刊论文
发表时间：2014-10-01
发表刊物：JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
收录刊物：SCIE、Scopus
卷号：163
期号：1
页面范围：181-199
ISSN号：0022-3239
关键字：Joint chance-constrained programs; Smoothing function; Sequential convex approximation method; DC function
摘要：In this article, we consider a DC (difference of two convex functions) function approach for solving joint chance-constrained programs (JCCP), which was first established by Hong et al. (Oper Res 59:617-630, 2011). They used a DC function to approximate the probability function and constructed a sequential convex approximation method to solve the approximation problem. However, the DC function they used was nondifferentiable. To alleviate this difficulty, we propose a class of smoothing functions to approximate the joint chance-constraint function, based on which smooth optimization problems are constructed to approximate JCCP. We show that the solutions of a sequence of smoothing approximations converge to a Karush-Kuhn-Tucker point of JCCP under a certain asymptotic regime. To implement the proposed method, four examples in the class of smoothing functions are explored. Moreover, the numerical experiments show that our method is comparable and effective.