
On the Convergence Rates of Proximal Splitting Algorithms
Liang, Jingwei; Fadili, Jalal; Peyré, Gabriel (2014), On the Convergence Rates of Proximal Splitting Algorithms, IEEE International Conference on Image Processing (ICIP 2014), IEEE : Piscataway, NJ. 10.1109/ICIP.2014.7025842
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Type
Communication / ConférenceDate
2014Conference title
IEEE International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, FranceConference date
2014-10Conference city
ParisConference country
FranceBook title
IEEE International Conference on Image Processing (ICIP 2014)Publisher
IEEE
Published in
Piscataway, NJ
ISBN
978-1-4799-5751-4
Publication identifier
Metadata
Show full item recordAuthor(s)
Liang, JingweiGroupe de Recherche en Informatique, Image et Instrumentation de Caen [GREYC]
Fadili, Jalal
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Peyré, Gabriel
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
In this work, we first provide iteration–complexity bounds (pointwise and ergodic) for the inexact Krasnosel'ski˘ ı–Mann iteration built from nonexpansive operators. Moreover, un-der an appropriate regularity assumption on the fixed point operator, local linear convergence rate is also established. These results are then applied to analyze the convergence rate of various proximal splitting methods in the litera-ture, which includes the Forward–Backward, generalized Forward–Backward, Douglas–Rachford, ADMM and some primal–dual splitting methods. For these algorithms, we develop easily verifiable termination criteria for finding an approximate solution, which is a generalization of the ter-mination criterion for the classical gradient descent method. We illustrate the usefulness of our results on a large class of problems in signal and image processing.Subjects / Keywords
Convex optimization; Proximal splitting; Convergence rates; Inverse problemsRelated items
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