Karush-Kuhn-Tucker optimality conditions for non-smooth geodesic quasi-convex optimization on Riemannian manifolds

被引:0
|
作者
Babu, Feeroz [1 ]
Ali, Akram [2 ]
Alkhaldi, Ali H. [2 ]
机构
[1] Vellore Inst Technol, Sch Adv Sci & Languages, Dept Math, Bhopal, India
[2] King Khalid Univ, Coll Sci, Dept Math, Abha, Saudi Arabia
关键词
Quasi-convex optimization; quasi-subdifferentials; Riemannian manifolds; Karush-Kuhn-Tucker optimality conditions; SUBGRADIENT METHODS; CONVERGENCE; FEASIBILITY; EFFICIENCY; ALGORITHM;
D O I
10.1080/02331934.2023.2232793
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 is committed to studying Karush-Kuhn-Tucker (in short, KKT) type necessary and sufficient optimality conditions for non-smooth quasi-convex (geodesic sense) optimization problems on Riemannian manifolds. Recently, Ansari et al. [Ansari QH, Babu F, Zeeshan M. Incremental quasi-subgradient method for minimizing geodesic quasi-convex function on Riemannian manifolds with applications. Numer Funct Anal Optim. 2022;42(13):1492-1521. doi: 10.1080/01630563.2021.2001823] defined the quasi-subdifferential on Riemannian manifolds and established the existence results of the quasi-subdifferential. We provide several auxiliary results for the quasi-subdifferential in the current study. We offer the KKT optimality conditions for the quasi-convex optimization problems on Riemannian manifolds with or without the Slater-constraint qualifications. To verify the suggested outcomes, we formulate numerical examples. In addition, we also provide our results in the Euclidean spaces, which are original and distinct from earlier findings in the Euclidean spaces.
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页码:2721 / 2744
页数:24
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