Convergence rate of inertial Forward-Backward algorithm beyond Nesterov's rule
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en
Article de revue
Este ítem está publicado en
Mathematical Programming, Series A. 2018-11-12p. 1–20
Springer
Resumen en inglés
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 ...Leer más >
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 .< Leer menos
Palabras clave en inglés
Convex optimization
proximal operator
inertial FB algorithm
Nesterov’s rule
rate of convergence
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Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010
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