On the Convergence Rates of Proximal Splitting AlgorithmsReport as inadecuate

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1 Equipe Image - Laboratoire GREYC - UMR6072 GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen 2 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision

Abstract : 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.

Keywords : Convex optimization Proximal splitting Convergence rates Inverse problems

Author: Jingwei Liang - Jalal M. Fadili - Gabriel Peyré -

Source: https://hal.archives-ouvertes.fr/


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