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.
https://hal.archives-ouvertes.fr/hal-01862210
Contributor : Emilie Chouzenoux
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Submitted on : Monday, August 27, 2018 - 10:46:41 AM
Last modification on : Wednesday, March 27, 2019 - 11:52:14 AM
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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. ⟨hal-01862210⟩
