Distributed sparse regression by consensus-based primal-dual perturbation optimization

Tsung Hui Chang, Angelia Nedic, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper studies the decentralized solution of a multi-agent sparse regression problem in the form of a globally coupled objective function with a non-smooth sparsity promoting constraint. In particular, we propose a distributed primal-dual perturbation (PDP) method which combines the average consensus technique and the primaldual perturbed subgradient method. Compared to the conventional primal-dual (PD) subgradient method without perturbation, the PDP subgradient method exhibits a faster convergence behavior. In order to handle the non-smooth constraints, we propose a novel proximal gradient type perturbation point. The proposed distributed optimization algorithm can be implemented as a fully decentralized protocol, with each agent using its local information and exchanging messages between neighbors only. We show that the proposed method converges to the global optimum of the considered problem under standard convex problem and network assumptions.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages289-292
Number of pages4
DOIs
StatePublished - Dec 1 2013
Externally publishedYes
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

Keywords

  • Average consensus
  • Distributed optimization
  • Primal-dual subgradient method
  • Sparse regression

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

Cite this

Chang, T. H., Nedic, A., & Scaglione, A. (2013). Distributed sparse regression by consensus-based primal-dual perturbation optimization. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (pp. 289-292). [6736872] (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). https://doi.org/10.1109/GlobalSIP.2013.6736872