Characterization of Norm-Based Robust Solutions in Vector Optimization

被引：6
|
作者：
Rahimi, Morteza [1 ]
Soleimani-damaneh, Majid [1 ]
机构：
[1] Univ Tehran, Sch Math Stat & Comp Sci, Coll Sci, Tehran, Iran
来源：
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2020年 / 185卷 / 02期
基金：
美国国家科学基金会;
关键词：
Vector optimization; Non-smooth optimization; Robust optimization; Clarke's generalized gradient; UNCERTAIN MULTIOBJECTIVE OPTIMIZATION; DOMINANCE;
D O I：
10.1007/s10957-020-01662-5
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：
In this paper, we study the norm-based robust (efficient) solutions of a vector optimization problem. We define two kinds of non-ascent directions in terms of Clarke's generalized gradient and characterize norm-based robustness by means of the newly defined directions. This is done under a basic constraint qualification. We extend the provided characterization to vector optimization problems with conic constraints and semi-infinite ones. Moreover, we derive a necessary condition for norm-based robustness utilizing a non-smooth gap function.
引用
收藏
页码：554 / 573
页数：20
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