RISK-AVERSE OPTIMIZATION IN TWO-STAGE STOCHASTIC MODELS: COMPUTATIONAL ASPECTS AND A STUDY

被引：12
|
作者：
Fabian, Csaba I. [1 ]
Wolf, Christian [2 ]
Koberstein, Achim [3 ]
Suhl, Leena [2 ]
机构：
[1] Kecskemet Coll, Dept Informat, H-6000 Kecskemet, Hungary
[2] Univ Paderborn, DS&OR Lab, D-33098 Paderborn, Germany
[3] European Univ Viadrina, Dept Informat & Operat Management IOM, D-15207 Frankfurt, Oder, Germany
来源：
SIAM JOURNAL ON OPTIMIZATION | 2015年 / 25卷 / 01期
关键词：
stochastic programming; risk-averse models; convex programming; cutting-plane methods; regularization; LEVEL BUNDLE METHODS; DOMINANCE CONSTRAINTS; DECOMPOSITION; FORMULATIONS; PROGRAMS; CRITERIA;
D O I：
10.1137/130918216
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We extend the on-demand accuracy approach of Oliveira and Sagastizabal to constrained convex optimization. The resulting method is applied to risk-averse two-stage stochastic programming problems. We present a survey of risk-averse models. The appropriate oracle is formulated for the case of a conditional value-at-risk constraint. We discuss computational aspects and compare different approaches in a study.
引用
收藏
页码：28 / 52
页数：25
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