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

页码：28 / 52

页数：25

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