DISTRIBUTIONALLY ROBUST STOCHASTIC PROGRAMMING

被引：90
|
作者：
Shapiro, Alexander [1 ]
机构：
[1] Georgia Inst Technol, Sch Ind & Syst Engn, Atlanta, GA 30332 USA
来源：
SIAM JOURNAL ON OPTIMIZATION | 2017年 / 27卷 / 04期
基金：
美国国家科学基金会;
关键词：
coherent risk measures; law invariance; Wasserstein distance; phi-divergence; sample average approximation; ambiguous chance constraints;
D O I：
10.1137/16M1058297
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper we study distributionally robust stochastic programming in a setting where there is a specified reference probability measure and the uncertainty set of probability measures consists of measures in some sense close to the reference measure. We discuss law invariance of the associated worst case functional and consider two basic constructions of such uncertainty sets. Finally we illustrate some implications of the property of law invariance.
引用
收藏
页码：2258 / 2275
页数：18
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