A Randomized Algorithm for Single-Source Shortest Path on Undirected Real-Weighted Graphs

TL;DR

This paper introduces a novel randomized algorithm for the single-source shortest path problem in undirected, real-weighted graphs that outperforms traditional algorithms in certain cases, with simpler implementation.

Contribution

The paper presents the first randomized algorithm that surpasses the $O(m+n ext{log}n)$ bound for undirected real-weighted graphs, breaking previous complexity barriers.

Findings

Achieves $O(m oot ext{log}n ext{log} ext{log}n)$ runtime

Simpler structure compared to hierarchy-based algorithms

Easier to implement in practice

Abstract

In undirected graphs with real non-negative weights, we give a new randomized algorithm for the single-source shortest path (SSSP) problem with running time $O(m\sqrt{\log n \cdot \log\log n})$ in the comparison-addition model. This is the first algorithm to break the $O(m+n\log n)$ time bound for real-weighted sparse graphs by Dijkstra's algorithm with Fibonacci heaps. Previous undirected non-negative SSSP algorithms give time bound of $O(m\alpha(m,n)+\min\{n\log n, n\log\log r\})$ in comparison-addition model, where $\alpha$ is the inverse-Ackermann function and $r$ is the ratio of the maximum-to-minimum edge weight [Pettie & Ramachandran 2005], and linear time for integer edge weights in RAM model [Thorup 1999]. Note that there is a proposed complexity lower bound of $\Omega(m+\min\{n\log n, n\log\log r\})$ for hierarchy-based algorithms for undirected real-weighted SSSP [Pettie & Ramachandran 2005], but our algorithm does not obey the properties required for that lower bound. As a non-hierarchy-based approach, our algorithm shows great advantage with much simpler structure, and is much easier to implement.