Minimax programming as a tool for studying robust multi-objective optimization problems

被引：9
|
作者：
Hong, Zhe [1 ,2 ]
Bae, Kwan Deok [2 ]
Kim, Do Sang [2 ]
机构：
[1] Yanbian Univ, Dept Math, Coll Sci, Yanji 133002, Peoples R China
[2] Pukyong Natl Univ, Dept Appl Math, Busan 48513, South Korea
来源：
ANNALS OF OPERATIONS RESEARCH | 2022年 / 319卷 / 02期
基金：
新加坡国家研究基金会;
关键词：
Multi-objective optimization; Minimax programming; Generalized convexity; KKT optimality conditions; Duality; SET-INCLUSIVE CONSTRAINTS; OPTIMALITY CONDITIONS; DUALITY; MINMAX;
D O I：
10.1007/s10479-021-04179-w
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
This paper aims to investigate optimality conditions for a weakly Pareto solution to a robust multi-objective optimization problem with locally Lipschitzian data. We do this by using a minimax programming approach, namely, by establishing the necessary optimality condition for a (local) optimal solution to a robust minimax optimization problem under a suitable constraint qualification, we then employ it to arrive in the desired target. In addition, some duality results for both robust minimax optimization problems and robust multi-objective optimization problems are also provided.
引用
收藏
页码：1589 / 1606
页数：18
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