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Conference papers
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.
https://hal.archives-ouvertes.fr/hal-01373644
Contributor : Emilie Chouzenoux <>
Submitted on : Thursday, September 29, 2016 - 1:41:43 AM
Last modification on : Wednesday, February 26, 2020 - 7:06:07 PM
Contributor : Emilie Chouzenoux <>
Submitted on : Thursday, September 29, 2016 - 1:41:43 AM
Last modification on : Wednesday, February 26, 2020 - 7:06:07 PM
