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.
@article{arxiv.2405.19977,
title = {Consistent Submodular Maximization},
author = {Paul Dütting and Federico Fusco and Silvio Lattanzi and Ashkan Norouzi-Fard and Morteza Zadimoghaddam},
journal= {arXiv preprint arXiv:2405.19977},
year = {2024}
}