A Proximal Approach for Solving Matrix Optimization Problems Involving a Bregman Divergence - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

A Proximal Approach for Solving Matrix Optimization Problems Involving a Bregman Divergence

Résumé

In recent years, there has been a growing interest in problems such as shape classification, gene expression inference, inverse covariance estimation. Problems of this kind have a common underlining mathematical model, which involves the minimization in a matrix space of a Bregman divergence function coupled with a linear term and a regularization term. We present an application of the Douglas-Rachford algorithm which allows to easily solve the optimization problem.
Fichier principal
Vignette du fichier
Benfenati.pdf (161.75 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01613292 , version 1 (10-10-2017)

Identifiants

  • HAL Id : hal-01613292 , version 1

Citer

Alessandro Benfenati, Emilie Chouzenoux, Jean-Christophe Pesquet. A Proximal Approach for Solving Matrix Optimization Problems Involving a Bregman Divergence. BASP 2017 - International Biomedical and Astronomical Signal Processing Frontiers workshop, Jan 2017, villars-sur-oulon, Switzerland. ⟨hal-01613292⟩
228 Consultations
147 Téléchargements

Partager

Gmail Facebook X LinkedIn More