Gabow's Cardinality Matching Algorithm in General Graphs: Implementation and Experiments

TL;DR

This paper presents an implementation and experimental analysis of Gabow's algorithm for maximum cardinality matching in general graphs, demonstrating significant improvements on worst-case graphs and near-linear performance on random graphs.

Contribution

The paper provides the first practical implementation of Gabow's algorithm with experimental results, highlighting its efficiency in worst-case scenarios.

Findings

Significant speedup on worst-case graphs.

Near-linear performance observed on random graphs.

Implementation is open-source and available for use.

Abstract

It is known since 1975 (\cite{HK75}) that maximum cardinality matchings in bipartite graphs with $n$ nodes and $m$ edges can be computed in time $O(\sqrt{n} m)$. Asymptotically faster algorithms were found in the last decade and maximum cardinality bipartite matchings can now be computed in near-linear time~\cite{NearlyLinearTimeBipartiteMatching, AlmostLinearTimeMaxFlow,AlmostLinearTimeMinCostFlow}. For general graphs, the problem seems harder. Algorithms with running time $O(\sqrt{n} m)$ were given in~\cite{MV80,Vazirani94,Vazirani12,Vazirani20,Vazirani23,Goldberg-Karzanov,GT91,Gabow:GeneralMatching}. Mattingly and Ritchey~\cite{Mattingly-Ritchey} and Huang and Stein~\cite{Huang-Stein} discuss implementations of the Micali-Vazirani Algorithm. We describe an implementation of Gabow's algorithm~\cite{Gabow:GeneralMatching} in C++ based on LEDA~\cite{LEDAsystem,LEDAbook} and report on running time experiments. On worst-case graphs, the asymptotic improvement pays off dramatically. On random graphs, there is no improvement with respect to algorithms that have a worst-case running time of $O(n m)$. The performance seems to be near-linear. The implementation is available open-source.