Optimizing Bipartite Matching in Real-World Applications by Incremental Cost Computation
Summary: Incremental edge-cost computation for min-cost bipartite matching via KM/Hungarian, guided by a cheap lower bound to evaluate edges on demand. Empirical results show strong runtime savings on real data; broad applicability where lower bounds exist. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Tenindra Abeywickrama (Grab; National University of Singapore)
- 2. Victor Liang (Grab; National University of Singapore)
- 3. Kian-Lee Tan (National University of Singapore)
BibTeX Citation
@article{abeywickrama_vldb21,
title = {{Optimizing Bipartite Matching in Real-World Applications by Incremental Cost Computation}},
author = {Abeywickrama, Tenindra and Liang, Victor and Tan, Kian-Lee},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {7},
pages = {1150--1158},
doi = {10.14778/3450980.3450983},
url = {https://doi.org/10.14778/3450980.3450983},
year = {2021}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 949 | Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation | 2012 | VLDB | 0.00013028107 |
| 3,084 | Order Dispatch in Price-aware Ridesharing | 2018 | VLDB | 7.7713936e-05 |
| 5,407 | Earth Mover’s Distance based Similarity Search at Scale | 2014 | VLDB | 6.2281449e-05 |
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