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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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https://hal.archives-ouvertes.fr/hal-01613292
Contributor : Emilie Chouzenoux <>
Submitted on : Tuesday, October 10, 2017 - 11:34:59 AM
Last modification on : Saturday, May 1, 2021 - 3:49:52 AM
Long-term archiving on: : Thursday, January 11, 2018 - 1:14:08 PM

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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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