Convergence analysis of stationary points in sample average approximation of stochastic programs with second order stochastic dominance constraints

• 成果类型: Article
• 署名作者: Sun, Hailin; Xu, Huifu
• 署名单位: Harbin Institute of Technology; City St Georges, University of London
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-013-0711-7
• 发表日期: 2014
• 页码: 31-59
• 关键词: uniform exponential convergence; optimization problems; mathematical programs; large numbers; subdifferentials; optimality; STABILITY; Duality

摘要

Sample average approximation (SAA) method has recently been applied to solve stochastic programs with second order stochastic dominance (SSD) constraints. In particular, Hu et al. (Math Program 133:171-201, 2012) presented a detailed convergence analysis of -optimal values and -optimal solutions of sample average approximated stochastic programs with polyhedral SSD constraints. In this paper, we complement the existing research by presenting convergence analysis of stationary points when SAA is applied to a class of stochastic minimization problems with SSD constraints. Specifically, under some moderate conditions we prove that optimal solutions and stationary points obtained from solving sample average approximated problems converge with probability one to their true counterparts. Moreover, by exploiting some recent results on large deviation of random functions and sensitivity analysis of generalized equations, we derive exponential rate of convergence of stationary points.