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Conference Papers Year : 2017

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

Abstract

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.
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Dates and versions

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

Identifiers

  • HAL Id : hal-01613292 , version 1

Cite

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⟩
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