Approximation Algorithms for Size-Constrained Non-Monotone Submodular Maximization in Deterministic Linear Time-Reference-Cited by-同舟云学术

Approximation Algorithms for Size-Constrained Non-Monotone Submodular Maximization in Deterministic Linear Time

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Chen Yixin¹^ORCID,Kuhnle Alan¹^ORCID

Affiliation:

1. Texas A&M University, College Station, TX, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599259

Reference34 articles.

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2. Georgios Amanatidis , Federico Fusco , Philip Lazos , Stefano Leonardi , and Rebecca Reiffenhä user. 2020. Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint. arXiv ( 2020 ), 1--23. arxiv: 2007.05014 Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, and Rebecca Reiffenhä user. 2020. Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint. arXiv (2020), 1--23. arxiv: 2007.05014

3. Ashwinkumar Badanidiyuru Baharan Mirzasoleiman Amin Karbasi and Andreas Krause. 2014. Streaming Submodular Maximization: Massive Data Summarization on the Fly. In ACM SIGKDD Knowledge Discovery and Data Mining (KDD). 671--680. https://doi.org/10.1145/2623330.2623637 10.1145/2623330.2623637

4. Ashwinkumar Badanidiyuru Baharan Mirzasoleiman Amin Karbasi and Andreas Krause. 2014. Streaming Submodular Maximization: Massive Data Summarization on the Fly. In ACM SIGKDD Knowledge Discovery and Data Mining (KDD). 671--680. https://doi.org/10.1145/2623330.2623637

5. Fast algorithms for maximizing submodular functions