Learning-Augmented Algorithms for Online Vertex Cover
- URL: http://arxiv.org/abs/2606.22831v1
- Date: Mon, 22 Jun 2026 04:14:12 GMT
- Title: Learning-Augmented Algorithms for Online Vertex Cover
- Abstract summary: We consider two graph cases: bipartite graphs and general graphs.<n>We show that these problems admit the same robustness--consistency tradeoffs as learning-augmented ski rental.
- Score: 2.162745596012608
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper studies learning-augmented online weighted vertex cover with advice and a parameter $λ\in (0,1)$. We consider two graph cases: bipartite graphs and general graphs. In both settings, the online algorithm must maintain a feasible vertex cover under irrevocable decisions. We show that these problems admit the same robustness--consistency tradeoffs as learning-augmented ski rental. For the bipartite graph model, we give a randomized algorithm that is $\frac{1}{1-e^{-λ}}$-robust and $\fracλ{1-e^{-λ}}$-consistent. For the general graph model, we give a deterministic algorithm that is $(1+\frac{1}λ)$-robust and $(1+λ)$-consistent. We prove that the tradeoffs above are optimal in both settings. We also validate the proposed algorithms through experiments on synthetic and real-world datasets.
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