Accelerated Self-Adaptive Method for Solving Nonsmooth Convex Minimization Problem in Real Hilbert Spaces

Résumé

In this manuscript, we propose a proximal gradient type algorithm together with a two step inertia method for approximating solution of convex minimization problem in real Hilbert spaces. The proposed proximal gradient type method is designed in such a way that it does not depend on the Lipschitz constant. Using a self-adaptive rule, we obtain a weak convergence result under the condition that the gradient function of one of the convex functions is uniformly continuous. Preliminary numerical results show that our proposed method has a better convergence in comparison to some other related results in the literature.

Citer ce document

Accès au document

Texte intégral en lecture en ligne, réservé aux abonnés SPHAERO et aux membres de l'institution. Se connecter

Auteur(s)

Statistiques

Consultations : 1

Téléchargements : 0