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arXiv · 2507.06032

Learning-Augmented Online Covering Problems

Afrouz Jabal Ameli·Laura Sanita·Moritz Venzin
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Abstract

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 $\rho(k, \cdot)$ depending on $k$, the number of arriving requests, into an algorithm with competitive ratio of $\rho(\eta, \cdot)$, where $\eta$ 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.

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BibTeXRIS

Afrouz Jabal Ameli, Laura Sanita, Moritz Venzin. 2025-07-08. Learning-Augmented Online Covering Problems. https://arxiv.org/abs/2507.06032

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