Minimum Path Cover in Parameterized Linear Time

TL;DR

This paper introduces a novel parameterized linear-time algorithm for computing the minimum path cover in DAGs, significantly improving efficiency for small-width graphs and providing an edge sparsification method that preserves width.

Contribution

The paper presents the first parameterized linear-time algorithm for minimum path cover in DAGs and an edge sparsification technique that maintains the graph's width.

Findings

First linear-time parameterized algorithm for MPC in DAGs

Edge sparsification algorithm preserves width with fewer edges

Total running time matches the MPC algorithm's complexity

Abstract

A minimum path cover (MPC) of a directed acyclic graph (DAG) $G = (V,E)$ is a minimum-size set of paths that together cover all the vertices of the DAG. Computing an MPC is a basic polynomial problem, dating back to Dilworth's and Fulkerson's results in the 1950s. Since the size $k$ of an MPC (also known as the width) can be small in practical applications, research has also studied algorithms whose running time is parameterized on $k$. We obtain a new MPC parameterized algorithm for DAGs running in time $O(k^2|V| + |E|)$. Our algorithm is the first solving the problem in parameterized linear time. Additionally, we obtain an edge sparsification algorithm preserving the width of a DAG but reducing $|E|$ to less than $2|V|$. This algorithm runs in time $O(k^2|V|)$ and requires an MPC of a DAG as input, thus its total running time is the same as the running time of our MPC algorithm.