Online resource allocation with structured diversification

Nicholas Johnson, Arindam Banerjee

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

1 Scopus citations


A variety of modern data analysis problems, ranging from finance to job scheduling, can be considered as online resource allocation (ORA) problems. A key consideration in such ORA problems is some notion of risk, and suitable ways to alleviate risk. In several settings, the risk is structured so that groups of assets, such as stocks, are exposed to similar risks. In this paper, we present a formulation for online resource allocation with structured diversification (ORASD) as a constrained online convex optimization problem. The key novel component of our formulation is a constraint on the L(∞1) group norm of the resource allocation vector, which ensures that no single group gets a large share of the resource and, unlike L(1, p)norms used for overlapping group Lasso, does not impose sparsity structures over groups. We instantiate the problem in the context of portfolio selection, propose an efficient ADMM algorithm, and illustrate the effectiveness of the formulation through extensive experiments on two benchmark datasets.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
EditorsSuresh Venkatasubramanian, Jieping Ye
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781510811522
StatePublished - 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Publication series

NameSIAM International Conference on Data Mining 2015, SDM 2015


OtherSIAM International Conference on Data Mining 2015, SDM 2015

Bibliographical note

Funding Information:
Acknowledgements: The research was supported in part by NSF grants IIS-1447566, IIS-1422557, CCF-1451986, CNS-1314560, IIS-0953274, IIS-1029711, and by NASA grant NNX12AQ39A. AB also acknowledges support from IBM and Yahoo. The authors would also like to thank Maria Gini for her helpful feedback.

Publisher Copyright:
Copyright © SIAM.


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