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A Faster Maximum Cardinality Matching Algorithm with Applications in Machine Learning

Maximum cardinality bipartite matching is an important graph optimization problem with several applications. For instance, maximum cardinality matching in a $\delta$-disc graph can be used in the computation of the bottleneck matching as well as the $\infty$-Wasserstein and the Lévy-Prokhorov distances between probability distributions. For any point sets $A, B \subset \mathbb{R}^2$, the $\delta$-disc graph is a bipartite graph formed by connecting every pair of points $(a,b) \in A\times B$ by an edge if the Euclidean distance between them is at most $\delta$. Using the classical Hopcroft-Karp algorithm, a maximum-cardinality matching on any $\delta$-disc graph can be found in $\tilde{O}(n^{3/2})$ time.~\footnote{We use $\tilde{O}(\cdot)$ to suppress poly-logarithmic terms in the complexity.} In this paper, we present a simplification of a recent algorithm (Lahn and Raghvendra, JoCG 2021) for the maximum cardinality matching problem and describe how a maximum cardinality matching in a $\delta$-disc graph can be computed asymptotically faster than $O(n^{3/2})$ time for any moderately dense point set. As applications, we show that if $A$ and $B$ are point sets drawn uniformly at random from a unit square, an exact bottleneck matching can be computed in $\tilde{O}(n^{4/3})$ time. On the other hand, experiments suggest that the Hopcroft-Karp algorithm seems to take roughly $\Theta (n^{3/2})$ time for this case. This translates to substantial improvements in execution time for larger inputs.

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