RELAXED LAGRANGIAN DUALITY IN CONVEX INFINITE OPTIMIZATION: REVERSE STRONG DUALITY AND OPTIMALITY

被引：0
|
作者：
Dinh N. [1 ,2 ]
Goberna M.A. [3 ]
López M.A. [3 ,4 ]
Volle M. [5 ]
机构：
[1] Department of Mathematics, International University, VNU-HCM, Thu Duc
[2] Department of Mathematics, Vietnam National University - HCMC, Thu Duc
[3] Department of Mathematics, University of Alicante, Alicante
[4] Centre for Informatics and Applied Optimization (CIAO), Federation University, Ballarat
[5] Laboratoire de Mathématiques d’Avignon, EA 2151, Avignon University, Avignon
来源：
Journal of Applied and Numerical Optimization | 2022年 / 4卷 / 01期
关键词：
Convex infinite programming; Haar duality; Lagrangian duality; Optimality;
D O I：
10.23952/jano.4.2022.1.02
中图分类号：
学科分类号：
摘要：
We associate with each convex optimization problem posed on some locally convex space with an infinite index set T, and a given non-empty family H formed by finite subsets of T, a suitable Lagrangian-Haar dual problem. We provide reverse H -strong duality theorems, H -Farkas type lemmas, and optimality theorems. Special attention is addressed to infinite and semi-infinite linear optimization problems. ©2022 Journal of Applied and Numerical Optimization
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页码：3 / 18
页数：15
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