Stochastic mathematical programs with probabilistic complementarity constraints: SAA and distributionally robust approaches

被引：0
作者：
Shen Peng
Jie Jiang
机构：
[1] KTH Royal Institute of Technology,Optimization and Systems Theory, Department of Mathematics
[2] Chongqing University,College of Mathematics and Statistics
来源：
Computational Optimization and Applications | 2021年 / 80卷
关键词：
Stochastic programming; Complementarity problem; Chance constraint; Sample average approximation; Distributionally robust; 90C15; 90C33; 90C25;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a class of stochastic mathematical programs with probabilistic complementarity constraints is considered. We first investigate convergence properties of sample average approximation (SAA) approach to the corresponding chance constrained relaxed complementarity problem. Our discussion can be not only applied to the specific model in this paper, but also viewed as a supplementary for the SAA approach to general joint chance constrained problems. Furthermore, considering the uncertainty of the underlying probability distribution, a distributionally robust counterpart with a moment ambiguity set is proposed. The numerically tractable reformulation is derived. Finally, we use a production planing model to report some preliminary numerical results.
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页码：153 / 184
页数：31
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