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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.
Cited literature [36 references]
https://hal.archives-ouvertes.fr/hal-01862210
Contributor : Emilie Chouzenoux Connect in order to contact the contributor
Submitted on : Monday, August 27, 2018 - 10:46:41 AM
Last modification on : Friday, February 4, 2022 - 3:11:35 AM
Long-term archiving on: : Wednesday, November 28, 2018 - 1:32:26 PM
Contributor : Emilie Chouzenoux Connect in order to contact the contributor
Submitted on : Monday, August 27, 2018 - 10:46:41 AM
Last modification on : Friday, February 4, 2022 - 3:11:35 AM
Long-term archiving on: : Wednesday, November 28, 2018 - 1:32:26 PM
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