Fully Dynamic Submodular Maximization over Matroids
Abstract
Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning. We study this classic problem in the fully dynamic setting, where elements can be both inserted and deleted in real-time. Our main result is a randomized algorithm that maintains an efficient data structure with an $\tilde{O}(k^2)$ amortized update time (in the number of additions and deletions) and yields a $4$-approximate solution, where $k$ is the rank of the matroid.
Cite
Text
Duetting et al. "Fully Dynamic Submodular Maximization over Matroids." International Conference on Machine Learning, 2023.Markdown
[Duetting et al. "Fully Dynamic Submodular Maximization over Matroids." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/duetting2023icml-fully/)BibTeX
@inproceedings{duetting2023icml-fully,
title = {{Fully Dynamic Submodular Maximization over Matroids}},
author = {Duetting, Paul and Fusco, Federico and Lattanzi, Silvio and Norouzi-Fard, Ashkan and Zadimoghaddam, Morteza},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {8821-8835},
volume = {202},
url = {https://mlanthology.org/icml/2023/duetting2023icml-fully/}
}