Distributionally Robust Joint Chance Constrained Problem under Moment Uncertainty

被引：2

作者：

Ding, Ke-wei ^{[1
,2
]}

机构：

[1] Southwest Univ Nationalities, Dept Fdn Educ, Chengdu 610041, Sichuan, Peoples R China

[2] Sichuan Univ, Dept Math, Chengdu 610064, Sichuan, Peoples R China

来源：

JOURNAL OF APPLIED MATHEMATICS | 2014年

关键词：

OPTIMIZATION; PROGRAMS;

D O I：

10.1155/2014/487178

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We discuss and develop the convex approximation for robust joint chance constraints under uncertainty of first- and second-order moments. Robust chance constraints are approximated by Worst-Case CVaR constraints which can be reformulated by a semidefinite programming. Then the chance constrained problem can be presented as semidefinite programming. We also find that the approximation for robust joint chance constraints has an equivalent individual quadratic approximation form.

引用

页数：8

共 16 条

[1] Second-order cone programming [J].