Streaming submodular maximization under a k-set system constraint

Ran Haba, Ehsan Kazemi, Moran Feldman, Amin Karbasi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization. This reduction readily leads to the currently tightest deterministic approximation ratio for sub modular maximization subject to a k-matchoid constraint. Moreover, we propose the first stream ing algorithm for monotone submodular maxi mization subject to k-extendible and k-set system constraints. Together with our proposed reduction, we obtain O(k log k) and O(k 2 log k) approxima tion ratio for submodular maximization subject to the above constraints, respectively. We exten sively evaluate the empirical performance of our algorithm against the existing work in a series of experiments including finding the maximum independent set in randomly generated graphs, maximizing linear functions over social networks, movie recommendation, Yelp location summa rization, and Twitter data summarization.

Original languageEnglish
Title of host publication37th International Conference on Machine Learning, ICML 2020
EditorsHal Daume, Aarti Singh
PublisherInternational Machine Learning Society (IMLS)
Pages3897-3907
Number of pages11
ISBN (Electronic)9781713821120
StatePublished - 2020
Externally publishedYes
Event37th International Conference on Machine Learning, ICML 2020 - Virtual, Online
Duration: 13 Jul 202018 Jul 2020

Publication series

Name37th International Conference on Machine Learning, ICML 2020
VolumePartF168147-6

Conference

Conference37th International Conference on Machine Learning, ICML 2020
CityVirtual, Online
Period13/07/2018/07/20

Bibliographical note

Publisher Copyright:
© International Conference on Machine Learning, ICML 2020. All rights reserved.

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