A model of distributionally robust two-stage stochastic convex programming with linear recourse

被引:32
|
作者
Li, Bin [1 ]
Qian, Xun [2 ]
Sun, Jie [3 ]
Teo, Kok Lay [4 ]
Yu, Changjun [5 ]
机构
[1] Sichuan Univ, Sch Elect Engn & Informat, Chengdu, Sichuan, Peoples R China
[2] Hong Kong Baptist Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China
[3] Chongqing Normal Univ, Sch Math Sci, Chongqing, Peoples R China
[4] Curtin Univ, Dept Math & Stat, Perth, WA, Australia
[5] Shanghai Univ, Dept Math, Shanghai, Peoples R China
基金
中国国家自然科学基金; 澳大利亚研究理事会;
关键词
Conic optimization; Duality; Stochastic programming; OPTIMIZATION; UNCERTAINTY;
D O I
10.1016/j.apm.2017.11.039
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We consider distributionally robust two-stage stochastic convex programming problems, in which the recourse problem is linear. Other than analyzing these new models case by case for different ambiguity sets, we adopt a unified form of ambiguity sets proposed by Wiesemann, Kuhn and Sim, and extend their analysis from a single stochastic constraint to the two-stage stochastic programming setting. It is shown that under a standard set of regularity conditions, this class of problems can be converted to a conic optimization problem. Numerical results are presented to show the efficiency of the distributionally robust approach. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码:86 / 97
页数:12
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