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

Luis M. Briceno-Arias(luis.briceno***at***usm.cl)
Giovanni Chierchia(giovanni.chierchia***at***esiee.fr)
Emilie Chouzenoux(emilie.chouzenoux***at***centralesupelec.fr)
Jean-Christophe Pesquet(jean-christophe***at***pesquet.eu)

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.

Keywords: proximal algorithms, machine learning, block coordinate methods, stochastic methods

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Category 2: Applications -- Science and Engineering (Statistics )

Citation: internal report, CentraleSupelec, Dec. 2017

Download: [PDF]

Entry Submitted: 12/31/2017
Entry Accepted: 12/31/2018
Entry Last Modified: 12/31/2017

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