Database Paper Browser

Back to papers

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)

Paper ID
12305
Venue
VLDB
Year
2021
Pagerank
4.1945683e-05
Overall Rank
11,494 | 20.04%
DOI
10.14778/3450980.3450983

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,170 Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation 2012 VLDB 0.00013511856
2,332 Order Dispatch in Price-aware Ridesharing 2018 VLDB 9.0171784e-05
5,326 Earth Mover's Distance based Similarity Search at Scale 2014 VLDB 5.5680074e-05
Previous Page 1 / 1 Next

Semantically Similar Papers