Online Bipartite Matching with Reusable Resources

  title={Online Bipartite Matching with Reusable Resources},
  author={Steven Delong and Alireza Farhadi and Rad Niazadeh and Balasubramanian Sivan},
  journal={Proceedings of the 23rd ACM Conference on Economics and Computation},
We study the classic online bipartite matching problem with a twist: offline nodes are reusable any number of times. Every offline node i becomes available d steps after it was assigned to. Nothing better than a 0.5-approximation, obtained by the trivial deterministic greedy algorithm, was known for this problem. We give the first approximation factor beating 0.5, namely a 0.505 approximation, by suitably adapting and interpreting the powerful technique of Online Correlated Selection. 

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