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 language | English |
---|---|
Title of host publication | 37th International Conference on Machine Learning, ICML 2020 |
Editors | Hal Daume, Aarti Singh |
Publisher | International Machine Learning Society (IMLS) |
Pages | 3897-3907 |
Number of pages | 11 |
ISBN (Electronic) | 9781713821120 |
State | Published - 2020 |
Externally published | Yes |
Event | 37th International Conference on Machine Learning, ICML 2020 - Virtual, Online Duration: 13 Jul 2020 → 18 Jul 2020 |
Publication series
Name | 37th International Conference on Machine Learning, ICML 2020 |
---|---|
Volume | PartF168147-6 |
Conference
Conference | 37th International Conference on Machine Learning, ICML 2020 |
---|---|
City | Virtual, Online |
Period | 13/07/20 → 18/07/20 |
Bibliographical note
Publisher Copyright:© International Conference on Machine Learning, ICML 2020. All rights reserved.