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 O (k2) amortized update time (in the number of additions and deletions) and yields a 4-approximate solution, where k is the rank of the matroid.
Fully Dynamic Submodular Maximization over Matroids / Dütting, P.; Fusco, F.; Lattanzi, S.; Norouzi-Fard, A.; Zadimoghaddam, M.. - 202:(2023), pp. 8821-8835. (Intervento presentato al convegno International Conference on Machine Learning tenutosi a Honolulu; USA).
Fully Dynamic Submodular Maximization over Matroids
Fusco F.
;Lattanzi S.;
2023
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 O (k2) amortized update time (in the number of additions and deletions) and yields a 4-approximate solution, where k is the rank of the matroid.File | Dimensione | Formato | |
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