Learning-Augmented Algorithms for Online Vertex Cover

arXiv:2606.22831v1 Announce Type: cross Abstract: This paper studies learning-augmented online weighted vertex cover with advice and a parameter lambda 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^{-lambda}}-robust and frac{lambda}{1-e^{-lambda}}-consistent. For the general graph model, we give a deterministic algorithm that is (1+frac{1}{lambda})-robust and (1+lambda)-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.

Source: arXiv cs.LG | 2026-06-23