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arXiv · 2106.01662

Relaxed Lagrangian duality in convex infinite optimization: reducibility and strong duality

Nguyen Dih·Miguel A. Goberna·Marco A. López·Michel Volle
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Abstract

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.

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Nguyen Dih, Miguel A. Goberna, Marco A. López, Michel Volle. 2021-06-03. Relaxed Lagrangian duality in convex infinite optimization: reducibility and strong duality. https://arxiv.org/abs/2106.01662

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