Name
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. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | convex optimization, distributed algorithms, proximal methods, video processing, parallel programming |
Field | DocType | ISSN |
Convergence (routing),Computer vision,Mathematical optimization,Video processing,Computer science,Hypergraph,Convex function,Differentiable function,Distributed algorithm,Artificial intelligence,Convex optimization,Speedup | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feriel Abboud | 1 | 0 | 0.34 |
| Emilie Chouzenoux | 2 | 202 | 26.37 |
| Jean-Christophe Pesquet | 3 | 18 | 11.52 |
| Hugues Talbot | 4 | 871 | 80.69 |