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A Distributed Strategy for Computing Proximity Operators

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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Contributor : Emilie Chouzenoux <>
Submitted on : Thursday, September 29, 2016 - 1:41:43 AM
Last modification on : Wednesday, February 3, 2021 - 7:54:27 AM



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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