Asymptotic Analysis for One-Stage Stochastic Linear Complementarity Problems and Applications

One-stage stochastic linear complementarity problem (SLCP) is a special case of multi-stage problem, which has important applications in economic engineering and operations management. In this paper, we establish asymptotic analysis results sample-average approximation (SAA) estimator for the SLCP. The normality stochastic-constrained optimization are extended to SLCP model then conditions, ensure convergence distribution multivariate normal with zero mean vector covariance matrix, obtained. obtained finally applied estimating confidence region solution