Monday, 15:15 - 15:40 h, Room: H 1012


Gabriel Peyré
A review of proximal splitting methods with a new one

Coauthors: Jalal Fadili, Hugo Raguet


In the first part of this talk, I will review proximal splitting methods for the resolution of large scale non-smooth convex problems (see for instance [1, 2]). I will show how each algorithm is able to take advantage of the structure of typical imaging problems. In the second part of this talk I will present the Generalized Forward Backward (GFB) splitting method [3] that is tailored for the minimization of the sum of a smooth function and an arbitrary number of "simple'' functions (for which the proximal operator can be computed in closed form). I will show on several imaging applications the advantage of our approach over state of the art proximal splitting schemes. Demos and codes for these proximal splitting schemes can be obtained by visiting .
%This is a joint work with Hugo Raguet (Ceremade) and Jalal Fadili (Caen).

  1. P. L. Combettes and J.-C. Pesquet, "Proximal splitting methods in signal processing'', 2011.
  2. A. Beck and M. Teboulle, "Gradient-Based Algorithms with Applications in Signal Recovery Problems'', 2010.
  3. H. Raguet, J. Fadili and G. Peyré, "Generalized Forward-Backward Splitting'', preprint HAL-00613637.


Talk 1 of the invited session Mon.3.H 1012
"Nonsmooth optimization in imaging sciences I" [...]
Cluster 17
"Nonsmooth optimization" [...]


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