可微分线性约束伪凸程序的最优条件

IF 1.4 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS

Decisions in Economics and Finance Pub Date : 2024-05-28 DOI:10.1007/s10203-024-00454-0

Riccardo Cambini, Rossana Riccardi

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引用次数: 0

摘要

本文旨在研究可微分线性约束伪凸程序的最优性条件。所述结果基于新的横向性条件，可以用来代替互补性条件。在适当的广义凸性属性下，阐述了必要和充分的最优性条件。此外，还提出了两对不同的对偶问题，并证明了弱对偶和强对偶结果。最后，说明了如何应用横向性条件来描述凸二次问题的最优性，以及如何有效地解决一类特殊的最大最小问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Optimality conditions for differentiable linearly constrained pseudoconvex programs

The aim of this paper is to study optimality conditions for differentiable linearly constrained pseudoconvex programs. The stated results are based on new transversality conditions which can be used instead of complementarity ones. Necessary and sufficient optimality conditions are stated under suitable generalized convexity properties. Moreover, two different pairs of dual problems are proposed and weak and strong duality results proved. Finally, it is shown how transversality conditions can be applied to characterize optimality of convex quadratic problems and to efficiently solve a particular class of Max-Min problems

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来源期刊

Decisions in Economics and Finance SOCIAL SCIENCES, MATHEMATICAL METHODS-

CiteScore

2.50

自引率

9.10%

发文量

期刊介绍： Decisions in Economics and Finance: A Journal of Applied Mathematics is the official publication of the Association for Mathematics Applied to Social and Economic Sciences (AMASES). It provides a specialised forum for the publication of research in all areas of mathematics as applied to economics, finance, insurance, management and social sciences. Primary emphasis is placed on original research concerning topics in mathematics or computational techniques which are explicitly motivated by or contribute to the analysis of economic or financial problems.