An asynchronous distributed proximal gradient method for composite convex optimization

Necdet S. Aybat, Z. Wang, G. Iyengar

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

12 Scopus citations

Abstract

We propose a distributed first-order augmented Lagrangian (DFAL) algorithm to minimize the sum of composite convex functions, where each term in the sum is a private cost function belonging to a node, and only nodes connected by an edge can directly communicate with each other. This optimization model abstracts a number of applications in distributed sensing and machine learning. We show that any limit point of DFAL iterates is optimal; and for any ∈ > 0, an ∈-optimal and e-feasible solution can be computed within O(log(∈-1)) DFAL iterations, which require O(ψ1.5max/dm-1) proximal gradient computations and communications per node in total, where ψmax denotes the largest eigenvalue of the graph Laplacian, and dmin is the minimum degree of the graph. We also propose an asynchronous version of DFAL by incorporating randomized block coordinate descent methods; and demonstrate the efficiency of DFAL on large scale sparse-group LASSO problems.

Original languageEnglish (US)
Title of host publication32nd International Conference on Machine Learning, ICML 2015
EditorsFrancis Bach, David Blei
PublisherInternational Machine Learning Society (IMLS)
Pages2444-2452
Number of pages9
Volume3
ISBN (Electronic)9781510810587
StatePublished - Jan 1 2015
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: Jul 6 2015Jul 11 2015

Other

Other32nd International Conference on Machine Learning, ICML 2015
CountryFrance
CityLile
Period7/6/157/11/15

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Science Applications

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  • Cite this

    Aybat, N. S., Wang, Z., & Iyengar, G. (2015). An asynchronous distributed proximal gradient method for composite convex optimization. In F. Bach, & D. Blei (Eds.), 32nd International Conference on Machine Learning, ICML 2015 (Vol. 3, pp. 2444-2452). International Machine Learning Society (IMLS).