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A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression

Abstract : In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method. Our algorithm sweeps the training set by randomly selecting a mini-batch of data at each iteration, and it allows us to update the variables in a block coordinate manner. Our approach leverages the proximity operator of the logistic loss, which is expressed with the generalized Lambert W function. Experiments carried out on standard datasets demonstrate the efficiency of our approach w.r.t. stochastic gradient-like methods.
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Submitted on : Monday, December 25, 2017 - 9:50:25 PM
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Luis Briceño-Arias, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet. A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression. Computational Optimization and Applications, Springer Verlag, 2019, 72 (3), pp.707-726. ⟨10.1007/s10589-019-00060-6⟩. ⟨hal-01672507⟩

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