Fully Dynamic Submodular Maximization over Matroids

Fuente: arXiv
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Bibliographic Details
Main Authors: Dütting, Paul, Fusco, Federico, Lattanzi, Silvio, Norouzi-Fard, Ashkan, Zadimoghaddam, Morteza
Format: Preprint
Published: 2023
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author Dütting, Paul
Fusco, Federico
Lattanzi, Silvio
Norouzi-Fard, Ashkan
Zadimoghaddam, Morteza
author_facet Dütting, Paul
Fusco, Federico
Lattanzi, Silvio
Norouzi-Fard, Ashkan
Zadimoghaddam, Morteza
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2305_19918
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Fully Dynamic Submodular Maximization over Matroids
Dütting, Paul
Fusco, Federico
Lattanzi, Silvio
Norouzi-Fard, Ashkan
Zadimoghaddam, Morteza
Data Structures and Algorithms
Machine Learning
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.
title Fully Dynamic Submodular Maximization over Matroids
topic Data Structures and Algorithms
Machine Learning
url https://arxiv.org/abs/2305.19918