Learning-Augmented Online Covering Problems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916832706822144 |
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| author | Ameli, Afrouz Jabal Sanita, Laura Venzin, Moritz |
| author_facet | Ameli, Afrouz Jabal Sanita, Laura Venzin, Moritz |
| contents | We give a very general and simple framework to incorporate predictions on requests for online covering problems in a rigorous and black-box manner. Our framework turns any online algorithm with competitive ratio $ρ(k, \cdot)$ depending on $k$, the number of arriving requests, into an algorithm with competitive ratio of $ρ(η, \cdot)$, where $η$ is the prediction error. With accurate enough prediction, the resulting competitive ratio breaks through the corresponding worst-case online lower bounds, and smoothly degrades as the prediction error grows. This framework directly applies to a wide range of well-studied online covering problems such as facility location, Steiner problems, set cover, parking permit, etc., and yields improved and novel bounds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning-Augmented Online Covering Problems Ameli, Afrouz Jabal Sanita, Laura Venzin, Moritz Data Structures and Algorithms We give a very general and simple framework to incorporate predictions on requests for online covering problems in a rigorous and black-box manner. Our framework turns any online algorithm with competitive ratio $ρ(k, \cdot)$ depending on $k$, the number of arriving requests, into an algorithm with competitive ratio of $ρ(η, \cdot)$, where $η$ is the prediction error. With accurate enough prediction, the resulting competitive ratio breaks through the corresponding worst-case online lower bounds, and smoothly degrades as the prediction error grows. This framework directly applies to a wide range of well-studied online covering problems such as facility location, Steiner problems, set cover, parking permit, etc., and yields improved and novel bounds. |
| title | Learning-Augmented Online Covering Problems |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2507.06032 |