A Feasible Semismooth Gauss-Newton Method for Solving a Class of SLCPs (Journal of Computational Mathematics, 2018)
In this paper, we consider a class of the stochastic linear complementarity problems (SLCPs) with finitely many elements. A feasible semismooth damped Gauss-Newton algorithm for the SLCP is proposed. The global and local quadratic convergence of the proposed algorithm are obtained under suitable conditions. Some numerical results are reported in this paper, which confirm the good theoretical properties of the proposed algorithm.
Keywords: Stochastic linear complementarity problems; Gauss-Newton algorithm; Convergence analysis; Numerical results.