Online alternating direction method

Huahua Wang, Arindam Banerjee

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

65 Scopus citations

Abstract

Online optimization has emerged as powerful tool in large scale optimization. In this paper, we introduce efficient online algorithms based on the alternating directions method (ADM). We introduce a new proof technique for ADM in the batch setting, which yields the O(1/T) convergence rate of ADM and forms the basis of regret analysis in the online setting. We consider two scenarios in the online setting, based on whether the solution needs to lie in the feasible set or not. In both settings, we establish regret bounds for both the objective function as well as constraint violation for general and strongly convex functions. Preliminary results are presented to illustrate the performance of the proposed algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages1119-1126
Number of pages8
StatePublished - Oct 10 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: Jun 26 2012Jul 1 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012
Volume2

Other

Other29th International Conference on Machine Learning, ICML 2012
Country/TerritoryUnited Kingdom
CityEdinburgh
Period6/26/127/1/12

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