Stochastic Methods Based onVU-Decomposition Methods for Stochastic Convex Minimax Problems

摘要

This paper applies sample average approximation (SAA) method based onVU-space decomposition theory to solve stochastic convex minimax problems. Under some moderate conditions, the SAA solution converges to its true counterpart with probability approaching one and convergence is exponentially fast with the increase of sample size. Based on theVU-theory, a superlinear convergentVU-algorithm frame is designed to solve the SAA problem.