A smoothing sample average approximation method for stochastic optimization problems with CVaR risk measure

被引：0
|
作者：
Fanwen Meng
Jie Sun
Mark Goh
机构：
[1] National University of Singapore,The Logistic Institute—Asia Pacific
[2] National University of Singapore,School of Business and Risk Management Institute
[3] National University of Singapore,School of Business and The Logistics Institute—Asia Pacific
[4] University of South Australia,undefined
来源：
Computational Optimization and Applications | 2011年 / 50卷
关键词：
Conditional value-at-risk; Sample average approximation; Smoothing method; Stochastic optimization;
D O I：
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学科分类号：
摘要：
This paper is concerned with solving single CVaR and mixed CVaR minimization problems. A CHKS-type smoothing sample average approximation (SAA) method is proposed for solving these two problems, which retains the convexity and smoothness of the original problem and is easy to implement. For any fixed smoothing constant ε, this method produces a sequence whose cluster points are weak stationary points of the CVaR optimization problems with probability one. This framework of combining smoothing technique and SAA scheme can be extended to other smoothing functions as well. Practical numerical examples arising from logistics management are presented to show the usefulness of this method.
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页码：379 / 401
页数：22
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