Adaptive consensus ADMM for distributed optimization

Zheng Xu*, Gavin Taylor, Hao Li, Mário A.T. Figueiredo, Xiaoming YUAN, Tom Goldstein

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) convergence rate for adaptive ADMM methods with node-specific parameters, and propose adaptive consensus ADMM (ACADMM), which automatically tunes parameters without user oversight.

Original languageEnglish
Title of host publication34th International Conference on Machine Learning, ICML 2017
PublisherInternational Machine Learning Society (IMLS)
Pages5864-5877
Number of pages14
ISBN (Electronic)9781510855144
Publication statusPublished - 2017
Event34th International Conference on Machine Learning, ICML 2017 - Sydney, Australia
Duration: 6 Aug 201711 Aug 2017

Publication series

Name34th International Conference on Machine Learning, ICML 2017
Volume8

Conference

Conference34th International Conference on Machine Learning, ICML 2017
Country/TerritoryAustralia
CitySydney
Period6/08/1711/08/17

Scopus Subject Areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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