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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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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Soumis le : mardi 10 octobre 2017 - 11:34:59
Dernière modification le : mercredi 11 octobre 2017 - 18:11:00

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  • HAL Id : hal-01613292, version 1

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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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