Indexed by:期刊论文

Date of Publication:2014-06-01

Journal:COMPUTATIONAL OPTIMIZATION AND APPLICATIONS

Included Journals:SCIE、EI

Volume:58

Issue:2

Page Number:483-501

ISSN No.:0926-6003

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

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.