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

页码：554 / 573

页数：20

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