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论文类型：期刊论文

发表时间：2014-06-01

发表刊物：COMPUTATIONAL OPTIMIZATION AND APPLICATIONS

收录刊物：SCIE、EI

卷号：58

期号：2

页面范围：483-501

ISSN号：0926-6003

关键字：Stochastic complementarity problems; Sample average approximation; CVaR; Penalized smoothing method; R-0 function

摘要：We reformulate a stochastic nonlinear complementarity problem as a stochastic programming problem which minimizes an expected residual defined by a restricted NCP function with nonnegative constraints and CVaR constraints which guarantee the stochastic nonlinear function being nonnegative with a high probability. By applying smoothing technique and penalty method, we propose a penalized smoothing sample average approximation algorithm to solve the CVaR-constrained stochastic programming. We show that the optimal solution of the penalized smoothing sample average approximation problem converges to the solution of the corresponding nonsmooth CVaR-constrained stochastic programming problem almost surely. Finally, we report some preliminary numerical test results.