Tight Competitive Ratios of Classic Matching Algorithms in the Fully Online Model
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Abstract (arXiv)
Huang et al.~(STOC 2018) introduced the fully online matching problem, a generalization of the classic online bipartite matching problem in that it allows all vertices to arrive online and considers general graphs. They showed that the ranking algorithm by Karp et al.~(STOC 1990) is strictly better than 0.5-competitive and the problem is strictly harder than the online bipartite matching problem in that no algorithms can be (1-1/e)-competitive. This paper pins down two tight competitive ratios of classic algorithms for the fully online matching problem. For the fractional version of the problem, we show that a natural instantiation of the water-filling algorithm is 2-√2 ≈ 0.585-competitive, together with a matching hardness result. Interestingly, our hardness result applies to arbitrary algorithms in the edge-arrival models of the online matching problem, improving the state-of-art frac11+ln 2 ≈ 0.5906 upper bound. For integral algorithms, we show a tight competitive ratio of ≈ 0.567 for the ranking algorithm on bipartite graphs, matching a hardness result by Huang et al. (STOC 2018).
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 0; 2025: 1.
4 papers.
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- 2022Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimizationcited by 2
- 2025Learning-Augmented Online Bipartite Fractional Matching25 May 2025
- 2022Sublinear Time Algorithm for Online Weighted Bipartite Matching5 Aug 2022
- 2022Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization11 Feb 2022
- 2021Subgroup Fairness in Two-Sided Markets4 Jun 2021