Consistent Submodular Maximization
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910464395444224 |
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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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19977 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Consistent Submodular Maximization Dütting, Paul Fusco, Federico Lattanzi, Silvio Norouzi-Fard, Ashkan Zadimoghaddam, Morteza Data Structures and Algorithms Machine Learning 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. |
| title | Consistent Submodular Maximization |
| topic | Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/2405.19977 |