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

Alessandro Benfenati 1 Emilie Chouzenoux 1, 2 Jean-Christophe Pesquet 2
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
Inria Saclay - Ile de France, Ecole Centrale Paris
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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Communication dans un congrès
BASP 2017 - International Biomedical and Astronomical Signal Processing Frontiers workshop, Jan 2017, villars-sur-oulon, Switzerland. 2017
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Dernière modification le : jeudi 5 juillet 2018 - 14:45:42
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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. 2017. 〈hal-01613292〉

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