Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

Convergence rate of a relaxed inertial proximal algorithm for convex minimization

Abstract : In a Hilbert space setting, the authors recently introduced a general class of relaxed inertial proximal algorithms that aim to solve monotone inclusions. In this paper, we specialize this study in the case of non-smooth convex minimization problems. We obtain convergence rates for values which have similarities with the results based on the Nesterov accelerated gradient method. The joint adjustment of inertia, relaxation and proximal terms plays a central role. In doing so, we highlight inertial proximal algorithms that converge for general monotone inclusions, and which, in the case of convex minimization, give fast convergence rates of values in the worst case.
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02415789
Contributeur : Imb - Université de Bourgogne <>
Soumis le : mardi 17 décembre 2019 - 11:57:16
Dernière modification le : mercredi 18 décembre 2019 - 01:49:13

Lien texte intégral

Identifiants

Citation

Hedy Attouch, Alexandre Cabot. Convergence rate of a relaxed inertial proximal algorithm for convex minimization. Optimization, Taylor & Francis, 2019, pp.1-32. ⟨10.1080/02331934.2019.1696337⟩. ⟨hal-02415789⟩

Partager

Métriques

Consultations de la notice

68