$ E $-Optimality and $ E $-duality results for multiobjective variational problems and application to the cake-eating problem

IF 1.2 4区 工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY
Nisha Pokharna, Indira P. Tripathi
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引用次数: 0

Abstract

In this paper, we introduce a new class of generalized convexity for an $ E $-differentiable multiobjective variational problem, and to solve this problem, an associated multiobjective $ E $-variational problem is formulated with the help of an $ E $-operator. The necessary and sufficient optimality conditions are derived under the assumption of $ E $-convexity. The so-called Wolfe and Mond-Weir $ E $-dual problems are defined for the considered $ E $-differentiable multiobjective variational programming problem, and several Wolfe and Mond-Weir $ E $-duality theorems are derived under the $ E $-convexity assumption. Furthermore, to emphasize the significance of the results of this study, we considered a cake-eating problem, and the solution obtained by constructing its associated $ E $-differentiable problem and the sufficiency theorem. Throughout the paper, non-trivial illustrations are presented to support the findings.
多目标变分问题的$ E $-最优性和$ E $-对偶性结果及其在吃蛋糕问题中的应用
本文引入了$ E $可微多目标变分问题的一类新的广义凸性,并利用$ E $-算子构造了相应的$ E $-变分问题。在E -凸性的假设下,导出了最优性的充分必要条件。针对所考虑的$ E $可微多目标变分规划问题,定义了所谓的Wolfe和Mond-Weir $ E $-对偶问题,并在$ E $-凸性假设下导出了几个Wolfe和Mond-Weir $ E $-对偶定理。进一步,为了强调本研究结果的意义,我们考虑了一个吃蛋糕问题,并通过构造其关联的$ E $可微问题和充分性定理得到了解。在整个论文中,提出了非琐碎的插图来支持研究结果。
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来源期刊
CiteScore
2.50
自引率
15.40%
发文量
207
审稿时长
18 months
期刊介绍: JIMO is an international journal devoted to publishing peer-reviewed, high quality, original papers on the non-trivial interplay between numerical optimization methods and practically significant problems in industry or management so as to achieve superior design, planning and/or operation. Its objective is to promote collaboration between optimization specialists, industrial practitioners and management scientists so that important practical industrial and management problems can be addressed by the use of appropriate, recent advanced optimization techniques.
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