Optimization Online


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

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society