A local selection rule based on a fractional solution of the expected instance preserves the expected maximum matching size under sufficient spread and yields near-optimal global matchings with small local budgets on ride-hailing data.
Proceedings of the 25th annual ACM-SIAM Symposium on Discrete Algorithms (SODA) , pages =
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Stochastic Matching via Local Sparsification
A local selection rule based on a fractional solution of the expected instance preserves the expected maximum matching size under sufficient spread and yields near-optimal global matchings with small local budgets on ride-hailing data.
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