Adding vs. averaging in distributed primal-dual optimization

Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I. Jordan, Peter Richtárik, Martin Takáč

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

83 Scopus citations


Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accurately aggregating partial work from different machines. In this paper, we present a novel generalization of the recent communication-efficient primal-dual framework (CoCoA) for distributed optimization. Our framework, CoCoA+, allows for additive combination of local updates to the global parameters at each iteration, whereas previous schemes with convergence guarantees only allow conservative averaging. We give stronger (primal-dual) convergence rate guarantees for both CoCoA as well as our new variants, and generalize the theory for both methods to cover non-smooth convex loss functions. We provide an extensive experimental comparison that shows the markedly improved performance of CoCoA+ on several real-world distributed datasets, especially when scaling up the number of machines.
Original languageEnglish (US)
Title of host publication32nd International Conference on Machine Learning, ICML 2015
PublisherInternational Machine Learning Society (IMLS)
Number of pages10
ISBN (Print)9781510810587
StatePublished - Jan 1 2015
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-25


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