Penalty-based SAA method of stochastic nonlinear complementarity problems

We consider a class of stochastic nonlinear complementarityproblems. We first formulate the stochastic complementarity problemas a stochastic programming model. Based on this reformulation, wepropose a penalty-based sample average approximation (in short, SAA) method forstochastic complementarity problem and prove its convergence.Finally, we report some numerical test results to show theefficiency of our method.