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arXiv · 2405.19977

Consistent Submodular Maximization

Paul Dütting·Federico Fusco·Silvio Lattanzi·Ashkan Norouzi-Fard·Morteza Zadimoghaddam
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Abstract

Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper we study this problem in a dynamic environment with consistency constraints: elements arrive in a streaming fashion and the goal is maintaining a constant approximation to the optimal solution while having a stable solution (i.e., the number of changes between two consecutive solutions is bounded). We provide algorithms in this setting with different trade-offs between consistency and approximation quality. We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances.

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BibTeXRIS

Paul Dütting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam. 2024-05-30. Consistent Submodular Maximization. https://arxiv.org/abs/2405.19977

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