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Conference papers

A Distributed Strategy for Computing Proximity Operators

Feriel Abboud 1, 2 Emilie Chouzenoux 1 Jean-Christophe Pesquet 1 Jean-Hugues Chenot 2 Louis Laborelli 2
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 INA - Institut National de l'Audiovisuel
Abstract : Various recent iterative optimization methods require to compute the proximity operator of a sum of functions. We address this problem by proposing a new distributed algorithm for a sum of non-necessarily smooth convex functions composed with arbitrary linear operators. In our approach, each function is associated with a node of a graph, which communicates with its neighbors. Our algorithm relies on a primal-dual splitting strategy that avoids to invert any linear operator, thus making it suitable for processing high-dimensional datasets. The proposed algorithm has a wide array of applications in signal/image processing and machine learning and its convergence is established.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01373644
Contributor : Emilie Chouzenoux Connect in order to contact the contributor
Submitted on : Thursday, September 29, 2016 - 1:41:43 AM
Last modification on : Saturday, January 15, 2022 - 3:56:21 AM

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CNRS | UPEC-UPEM | LIGM | LIGM_SIGNAL | PARISTECH | ENPC | ESIEE-PARIS

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Feriel Abboud, Emilie Chouzenoux, Jean-Christophe Pesquet, Jean-Hugues Chenot, Louis Laborelli. A Distributed Strategy for Computing Proximity Operators. Asilomar Conference on Signals, Systems, and Computers, Nov 2015, Asilomar, United States. p. 396-400, ⟨10.1109/ACSSC.2015.7421156⟩. ⟨hal-01373644⟩

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