Learning-Augmented Algorithms for Online Vertex Cover
This paper studies learning-augmented online weighted vertex cover with local advice and a tradeoff parameter $λ\in (0,1)$. We consider two graph settings: 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 ar...
| Authors | Tianhang Lu, Runtian Ren, Shengcai Liu |
|---|---|
| Primary category | cs.CC |
| Published | 22 June 2026 |
| Last updated at source | 30 July 2026 |
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