Experimental Evaluation of Modified Decomposition Algorithm for Maximum Weight Bipartite Matching

TL;DR

This paper refines a decomposition theorem to create a more efficient algorithm for maximum weight bipartite matching, demonstrating significant practical improvements through experimental evaluation.

Contribution

It introduces an improved decomposition algorithm with a refined time complexity for maximum weight bipartite matching, utilizing a scaled parameter $W'$ and experimental validation.

Findings

Significant performance improvements in practical scenarios.

The refined algorithm has better bounds on the parameter $W'$.

Experimental results confirm the efficiency of the proposed method.

Abstract

Let $G$ be an undirected bipartite graph with positive integer weights on the edges. We refine the existing decomposition theorem originally proposed by Kao et al., for computing maximum weight bipartite matching. We apply it to design an efficient version of the decomposition algorithm to compute the weight of a maximum weight bipartite matching of $G$ in $O(\sqrt{|V|}W'/k(|V|,W'/N))$-time by employing an algorithm designed by Feder and Motwani as a subroutine, where $|V|$ and $N$ denote the number of nodes and the maximum edge weight of $G$, respectively and $k(x,y)=\log x /\log(x^2/y)$. The parameter $W'$ is smaller than the total edge weight $W,$ essentially when the largest edge weight differs by more than one from the second largest edge weight in the current working graph in any decomposition step of the algorithm. In best case $W'=O(|E|)$ where $|E|$ be the number of edges of $G$ and in worst case $W'=W,$ that is, $|E| \leq W' \leq W.$ In addition, we talk about a scaling property of the algorithm and research a better bound of the parameter $W'$. An experimental evaluation on randomly generated data shows that the proposed improvement is significant in general.