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

Émilie Chouzenoux (CVN, CentraleSupélec/INRIA, Université Paris-Est Marne-La-Vallée)
November 24, 2017 — 14:00 — "Salle du conseil du L2S"

Abstract

In this talk, I will present a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas- Rachford splitting method. The 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. (joint work with Luis M. Briceño-Arias, Afef Cherni, Giovanni Chierchia and Jean- Christophe Pesquet )