Relaxed Lagrangian duality in convex infinite optimization: reducibility and strong duality
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http://hdl.handle.net/10045/121574
Títol: | Relaxed Lagrangian duality in convex infinite optimization: reducibility and strong duality |
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Autors: | Dinh, Nguyen | Goberna, Miguel A. | López Cerdá, Marco A. | Volle, Michel |
Grups d'investigació o GITE: | Laboratorio de Optimización (LOPT) |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Matemáticas |
Paraules clau: | Convex infinite programming | Lagrangian duality | Haar duality | Reducibility |
Àrees de coneixement: | Estadística e Investigación Operativa |
Data de publicació: | 7-de febrer-2022 |
Editor: | Taylor & Francis |
Citació bibliogràfica: | Optimization. 2023, 72(1): 189-214. https://doi.org/10.1080/02331934.2022.2031192 |
Resum: | We associate with each convex optimization problem, posed on some locally convex space, with infinitely many constraints indexed by the set T, and a given non-empty family H of finite subsets of T, a suitable Lagrangian-Haar dual problem. We obtain necessary and sufficient conditions for H-reducibility, that is, equivalence to some subproblem obtained by replacing the whole index set T by some element of H. Special attention is addressed to linear optimization, infinite and semi-infinite, and to convex problems with a countable family of constraints. Results on zero H-duality gap and on H-(stable) strong duality are provided. Examples are given along the paper to illustrate the meaning of the results. |
Patrocinadors: | This research was supported by the Vietnam National University HoChiMinh City (VNU-HCM) [grant number B2021-28-03], and by Ministerio de Ciencia, Innovación y Universidades (MCIU), Agencia Estatal de Investigación (AEI), and European Regional Development Fund (ERDF) (Project PGC2018-097960-B-C22). |
URI: | http://hdl.handle.net/10045/121574 |
ISSN: | 0233-1934 (Print) | 1029-4945 (Online) |
DOI: | 10.1080/02331934.2022.2031192 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2022 Informa UK Limited, trading as Taylor & Francis Group |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.1080/02331934.2022.2031192 |
Apareix a la col·lecció: | INV - LOPT - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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Dinh_etal_2022_Optimization_preprint.pdf | Preprint (acceso abierto) | 448,86 kB | Adobe PDF | Obrir Vista prèvia |
Dinh_etal_2022_Optimization_final.pdf | Versión final (acceso restringido) | 2,13 MB | Adobe PDF | Obrir Sol·licitar una còpia |
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