SECOND ORDER STOCHASTIC DOMINANCE CONSTRAINTS IN MULTI-OBJECTIVE STOCHASTIC PROGRAMMING PROBLEMS

被引：0
|
作者：
Kankova, Vlasta [1 ,2 ]
机构：
[1] AS CR, Inst Informat Theory & Automat, Prague, Czech Republic
[2] Acad Sci Czech Republ, Inst Informat Theory & Automat, Dept Econometr, Pod Vodarenskou Vezi 4, Prague 18208 8, Czech Republic
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE: QUANTITATIVE METHODS IN ECONOMICS: MULTIPLE CRITERIA DECISION MAKING XIX | 2018年
关键词：
Stochastic multi-objective optimization problems; efficient solution; Wasserstein metric and L-1 norm; Lipschitz property; second order stochastic dominance constraints; relaxation; OPTIMIZATION;
D O I：
暂无
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
Many economic and financial applications lead to deterministic optimization problems depending on a probability measure. These problems can be either static (one stage) or dynamic with finite (multistage) or infinite horizon, single-objective or multi-objective. Constraints sets can be "deterministic", given by probability constraints or stochastic dominance constraints. We focus on multi-objective problems and second order stochastic dominance constraints. To this end we employ the former results obtained for stochastic (mostly strongly) convex multi-objective problems and results obtained for one objective problems with second order stochastic dominance constraints. The relaxation approach will be included in the case of second order stochastic dominance constraints.
引用
收藏
页码：165 / 171
页数：7
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