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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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Contributor : Emilie Chouzenoux <>
Submitted on : Monday, August 27, 2018 - 10:46:41 AM
Last modification on : Wednesday, April 8, 2020 - 3:27:10 PM
Document(s) archivé(s) le : Wednesday, November 28, 2018 - 1:32:26 PM


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  • HAL Id : hal-01862210, version 1


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. ⟨hal-01862210⟩



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