CVaR-constrained stochastic programming reformulation for stochastic nonlinear complementarity problems

Liyan Xu () and Bo Yu ()

Computational Optimization and Applications, 2014, vol. 58, issue 2, 483-501

Abstract: 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. Copyright Springer Science+Business Media New York 2014

Keywords: Stochastic complementarity problems; Sample average approximation; CVaR; Penalized smoothing method; R 0 function (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s10589-013-9625-9

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