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Approche de Douglas-Rachford aléatoire par blocs appliquée à la régression logistique parcimonieuse

Abstract : We propose a stochastic optimization algorithm for logistic regression based on a randomized version of Douglas-Rachford splitting method. Our algorithm sweeps the training set by randomly selecting a mini-batch of data at each iteration, and it performs the update step by leveraging the proximity operator of the logistic loss, for which a closed-form expression is derived. Experiments carried out on standard datasets compare the efficiency of our algorithm to stochastic gradient-like methods.
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https://hal.archives-ouvertes.fr/hal-01634525
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Submitted on : Tuesday, November 14, 2017 - 11:25:40 AM
Last modification on : Wednesday, April 8, 2020 - 3:27:08 PM
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  • HAL Id : hal-01634525, version 1

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Giovanni Chierchia, Afef Cherni, Emilie Chouzenoux, Jean-Christophe Pesquet. Approche de Douglas-Rachford aléatoire par blocs appliquée à la régression logistique parcimonieuse. GRETSI 2017, Sep 2017, Juan les Pins, France. pp.1-4. ⟨hal-01634525⟩

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