Optimality analysis for $$\epsilon $$ -quasi solutions of optimization problems via $$\epsilon $$ -upper convexificators: a dual approach

IF 1.8 3区 数学 Q1 Mathematics Journal of Global Optimization Pub Date : 2024-07-08 DOI:10.1007/s10898-024-01415-y
Tran Van Su
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Abstract

The theory of duality is of fundamental importance in the study of vector optimization problems and vector equilibrium problems. A Mond–Weir-type dual model for such problems is important in practice. Therefore, studying such problems with a dual approach is really useful and necessary in the literature. The goal of this article is to formulate Mond–Weir-type dual models for the minimization problem (P), the constrained vector optimization problem (CVOP) and the constrained vector equilibrium problem (CVEP) in terms of \(\epsilon \)-upper convexificators. By applying the concept of \(\epsilon \)-pseudoconvexity, some weak, strong and converse duality theorems for the primal problem (P) and its dual problem (DP), the primal vector optimization problem (CVOP) and its Mond–Weir-type dual problem (MWCVOP), the primal vector equilibrium problem (P) and its Mond–Weir-type dual problem (MWCVEP) are explored.

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通过 $$\epsilon $$ -upper convexificators 对优化问题的 $$\epsilon $$ -quasi 解进行最优性分析:一种对偶方法
对偶理论对于研究矢量优化问题和矢量均衡问题具有根本性的重要意义。针对此类问题的蒙德-韦尔型对偶模型在实践中非常重要。因此,用对偶方法研究这类问题在文献中是非常有用和必要的。本文的目标是用\(epsilon \)-上凸化器为最小化问题(P)、约束向量优化问题(CVOP)和约束向量均衡问题(CVEP)建立蒙德-韦尔型对偶模型。通过应用 \(\epsilon\)-pseudoconvexity 的概念,探讨了原始问题(P)及其对偶问题(DP)、原始矢量优化问题(CVOP)及其蒙德-韦尔型对偶问题(MWCVOP)、原始矢量均衡问题(P)及其蒙德-韦尔型对偶问题(MWCVEP)的一些弱、强和逆对偶定理。
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来源期刊
Journal of Global Optimization
Journal of Global Optimization 数学-应用数学
CiteScore
0.10
自引率
5.60%
发文量
137
审稿时长
6 months
期刊介绍: The Journal of Global Optimization publishes carefully refereed papers that encompass theoretical, computational, and applied aspects of global optimization. While the focus is on original research contributions dealing with the search for global optima of non-convex, multi-extremal problems, the journal’s scope covers optimization in the widest sense, including nonlinear, mixed integer, combinatorial, stochastic, robust, multi-objective optimization, computational geometry, and equilibrium problems. Relevant works on data-driven methods and optimization-based data mining are of special interest. In addition to papers covering theory and algorithms of global optimization, the journal publishes significant papers on numerical experiments, new testbeds, and applications in engineering, management, and the sciences. Applications of particular interest include healthcare, computational biochemistry, energy systems, telecommunications, and finance. Apart from full-length articles, the journal features short communications on both open and solved global optimization problems. It also offers reviews of relevant books and publishes special issues.
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