Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916258802302976 |
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| author | Harris, Blake Nagarajan, Viswanath |
| author_facet | Harris, Blake Nagarajan, Viswanath |
| contents | We show that the greedy algorithm for adaptive-submodular cover has approximation ratio at least 1.3*(1+ln Q). Moreover, the instance demonstrating this gap has Q=1. So, it invalidates a prior result in the paper ``Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization'' by Golovin-Krause, that claimed a (1+ln Q)^2 approximation ratio for the same algorithm. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_14995 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover Harris, Blake Nagarajan, Viswanath Data Structures and Algorithms Artificial Intelligence Machine Learning We show that the greedy algorithm for adaptive-submodular cover has approximation ratio at least 1.3*(1+ln Q). Moreover, the instance demonstrating this gap has Q=1. So, it invalidates a prior result in the paper ``Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization'' by Golovin-Krause, that claimed a (1+ln Q)^2 approximation ratio for the same algorithm. |
| title | Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover |
| topic | Data Structures and Algorithms Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.14995 |