A random block-coordinate Douglas–Rachford splitting method with low computational complexity for binary logistic regression

Luis M. Briceño-Arias, Giovanni Chierchia (), Emilie Chouzenoux and Jean-Christophe Pesquet
Additional contact information
Luis M. Briceño-Arias: Universidad Técnica Federico Santa María
Giovanni Chierchia: Université Paris-Est (UPEM)
Emilie Chouzenoux: Université Paris-Est (UPEM)
Jean-Christophe Pesquet: University Paris-Saclay

Computational Optimization and Applications, 2019, vol. 72, issue 3, No 7, 707-726

Abstract: 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 performs both function and variable splittings. It 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: Proximity operator; Douglas–Rachford splitting; Block-coordinate descent; Logistic regression (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10589-019-00060-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:coopap:v:72:y:2019:i:3:d:10.1007_s10589-019-00060-6

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-019-00060-6

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().