Fully Dynamic Submodular Maximization over Matroids

Autor:	Dütting, Paul, Fusco, Federico, Lattanzi, Silvio, Norouzi-Fard, Ashkan, Zadimoghaddam, Morteza
Rok vydání:	2023
Předmět:	Computer Science - Data Structures and Algorithms Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at ICML 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.19918 Zobrazit plný text záznamu View this record from Arxiv