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.
|Title of host publication||37th International Conference on Machine Learning, ICML 2020|
|Editors||Hal Daume, Aarti Singh|
|Publisher||International Machine Learning Society (IMLS)|
|Number of pages||11|
|State||Published - 2020|
|Event||37th International Conference on Machine Learning, ICML 2020 - Virtual, Online|
Duration: 13 Jul 2020 → 18 Jul 2020
|Name||37th International Conference on Machine Learning, ICML 2020|
|Conference||37th International Conference on Machine Learning, ICML 2020|
|Period||13/07/20 → 18/07/20|
Bibliographical noteFunding Information:
The research of Moran Feldman and Ran Haba was partially supported by ISF grant 1357/16. Amin Karbasi is partially supported by NSF (IIS-1845032), ONR (N00014-19-1-2406), and AFOSR (FA9550-18-1-0160).
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