利用凸化算子求解非光滑多目标分式问题的最优性和对偶性

IF 1.1 Q1 MATHEMATICS Journal of Nonlinear Functional Analysis Pub Date : 2021-01-01 DOI:10.23952/jnfa.2021.1

D. Luu, P. T. Linh

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引用次数: 3

摘要

．本文给出了包含等式、不等式和集合约束的多目标分数优化问题弱效率的Fritz John必要条件。利用Mangasarian-Fromovitz型约束条件，建立了Kuhn-Tucker必要效率条件。在广义凸性的假设下，给出了弱效率的充分条件，并给出了Wolfe型和Mond-Weir型的弱对偶、强对偶和逆对偶定理。

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Optimality and duality for nonsmooth multiobjective fractional problems using convexificators

. This paper presents Fritz John necessary conditions for the weak efﬁciency of multiobjective fractional optimization problems involving equality, inequality and set constraints. With a constraint qualiﬁcation of Mangasarian–Fromovitz type, Kuhn–Tucker necessary efﬁciency conditions are established. Under assumptions on generalized convexity, sufﬁcient conditions for weak efﬁciency are also given together with the theorems of the weak duality, the strong duality, and the inverse duality of Wolfe and Mond–Weir types.

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来源期刊

Journal of Nonlinear Functional Analysis Multiple-

CiteScore

2.40

自引率

0.00%

发文量

期刊介绍： Journal of Nonlinear Functional Analysis focuses on important developments in nonlinear functional analysis and its applications with a particular emphasis on topics include, but are not limited to: Approximation theory; Asymptotic behavior; Banach space geometric constant and its applications; Complementarity problems; Control theory; Dynamic systems; Fixed point theory and methods of computing fixed points; Fluid dynamics; Functional differential equations; Iteration theory, iterative and composite equations; Mathematical biology and ecology; Miscellaneous applications of nonlinear analysis; Multilinear algebra and tensor computation; Nonlinear eigenvalue problems and nonlinear spectral theory; Nonsmooth analysis, variational analysis, convex analysis and their applications; Numerical analysis; Optimal control; Optimization theory; Ordinary differential equations; Partial differential equations; Positive operator inequality and its applications in operator equation spectrum theory and so forth; Semidefinite programming polynomial optimization; Variational and other types of inequalities involving nonlinear mappings; Variational inequalities.