A Multicore Convex Optimization Algorithm with Applications to Video Restoration

Abstract : In this paper, we present a new distributed algorithm for minimizing a sum of non-necessarily differentiable convex functions composed with arbitrary linear operators. The overall cost function is assumed strongly convex. Each involved function is associated with a node of a hypergraph having the ability to communicate with neighboring nodes sharing the same hyperedge. Our algorithm relies on a primal-dual splitting strategy with established convergence guarantees. We show how it can be efficiently implemented to take full advantage of a multicore architecture. The good numerical performance of the proposed approach is illustrated in a problem of video sequence denoising, where a significant speedup is achieved.
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Communication dans un congrès
IEEE International Conference on Image Processing, Oct 2018, Athens, Greece. 2018, Proceedings of the IEEE International Conference on Image Processing (ICIP 2018)
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https://hal.archives-ouvertes.fr/hal-01862210
Contributeur : Emilie Chouzenoux <>
Soumis le : lundi 27 août 2018 - 10:46:41
Dernière modification le : jeudi 6 septembre 2018 - 01:11:03
Document(s) archivé(s) le : mercredi 28 novembre 2018 - 13:32:26

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  • HAL Id : hal-01862210, version 1

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Feriel Abboud, Emilie Chouzenoux, Jean-Christophe Pesquet, Hugues Talbot. A Multicore Convex Optimization Algorithm with Applications to Video Restoration. IEEE International Conference on Image Processing, Oct 2018, Athens, Greece. 2018, Proceedings of the IEEE International Conference on Image Processing (ICIP 2018). 〈hal-01862210〉

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