A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
Optimization and Control
2019-04-09 v1 Artificial Intelligence
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.
Cite
@article{arxiv.1712.09131,
title = {A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression},
author = {Luis M. Briceno-Arias and Giovanni Chierchia and Emilie Chouzenoux and Jean-Christophe Pesquet},
journal= {arXiv preprint arXiv:1712.09131},
year = {2019}
}