Sample average approximation method is one of the well-behaved methods in the stochastic optimization. This paper presents a sample average approximation method based on a D-gap function for stochastic variational inequality problems. An unconstrained optimization reformulation is proposed for the expected-value formulation of stochastic variational inequality problems based on the D-gap function. An implementable sample average approximation method for the reformulation is established and it is proven that the optimal values and the optimal solutions of the approximation problems converge to their true counterpart with probability one as the sample size increases under some moderate assumptions. Finally, the preliminary numerical results for some test examples are reported, which show that the proposed method is promising.

This article presents a novel nonlinear Lagrange algorithm for solving minimax optimization problems with both inequality and equality constraints, which eliminates the nonsmoothness of the considered problems and the numerical difficulty of the penalty method. The convergence of the proposed algorithm is analyzed under some mild assumptions, in which the sequence of the generated solutions converges locally to a Karush-Kuhn-Tucker solution at a linear rate when the penalty parameter is less than a threshold and the error bound of the solutions is also obtained. Finally, the detailed numerical results for several typical testproblems are given in order to show the performance of the proposed algorithm.