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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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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