Convergence rate of inertial Forward-Backward algorithm beyond Nesterov's rule
Language
en
Article de revue
This item was published in
Mathematical Programming, Series A. 2018-11-12p. 1–20
Springer
English Abstract
In this paper we study the convergence of an Inertial Forward-Backward algorithm, with a particular choice of an over-relaxation term. In particular we show that for a sequence of overrrelaxation parameters, that do not ...Read more >
In this paper we study the convergence of an Inertial Forward-Backward algorithm, with a particular choice of an over-relaxation term. In particular we show that for a sequence of overrrelaxation parameters, that do not satisfy Nesterov’s rule one can still expect some relatively fast convergence properties for the objective function. In addition we complement this work by studying the convergence of the algorithm in the case where the proximal operator is inexactly computed with the presence of some errors and we give sufficient conditions over these errors in order to obtain some convergence properties for the objective function .Read less <
English Keywords
Convex optimization
proximal operator
inertial FB algorithm
Nesterov’s rule
rate of convergence
ANR Project
Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010
Origin
Hal imported