Robustness Characterizations for Uncertain Optimization Problems via Image Space Analysis

被引：5
|
作者：
Wei, Hong-Zhi [1 ]
Chen, Chun-Rong [2 ]
Li, Sheng-Jie [2 ]
机构：
[1] Shaanxi Normal Univ, Sch Math & Informat Sci, Xian 710062, Peoples R China
[2] Chongqing Univ, Coll Math & Stat, Chongqing 401331, Peoples R China
来源：
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2020年 / 186卷 / 02期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Image space analysis; Robust optimization; Vector; set-valued optimization; Robust optimality condition; CONSTRAINED EXTREMUM PROBLEMS; VARIATIONAL-INEQUALITIES; OPTIMALITY CONDITIONS; NONLINEAR SEPARATION; UNIFIED APPROACH; PERSPECTIVES; PROGRAMS; THEOREMS; SETS;
D O I：
10.1007/s10957-020-01709-7
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, by means of linear and nonlinear (regular) weak separation functions, we obtain some characterizations of robust optimality conditions for uncertain optimization problems, especially saddle point sufficient optimality conditions. Additionally, the relationships between three approaches used for robustness analysis: image space analysis, vector optimization and set-valued optimization, are discussed. Finally, an application for finding a shortest path is given to verify the validity of the results derived in this paper.
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页码：459 / 479
页数：21
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