Multiple Source Replacement Path Problem

TL;DR

This paper introduces a new randomized algorithm for the multiple source replacement path problem in graphs, generalizing previous solutions and providing a simpler approach with proven lower bounds.

Contribution

It presents a novel, simpler randomized algorithm for the multiple source replacement path problem, extending prior work to a broader setting with theoretical lower bounds.

Findings

Algorithm runs in (m\u221a{n  }) time

Generalizes previous single and multiple source solutions

Establishes matching lower bounds under the Boolean Matrix Multiplication conjecture

Abstract

One of the classical line of work in graph algorithms has been the Replacement Path Problem: given a graph $G$, $s$ and $t$, find shortest paths from $s$ to $t$ avoiding each edge $e$ on the shortest path from $s$ to $t$. These paths are called replacement paths in literature. For an undirected and unweighted graph, (Malik, Mittal, and Gupta, Operation Research Letters, 1989) and (Hershberger and Suri, FOCS 2001) designed an algorithm that solves the replacement path problem in $\tilde O(m+n)$ time. It is natural to ask whether we can generalize the replacement path problem: {\em can we find all replacement paths from a source $s$ to all vertices in $G$?} This problem is called the Single Source Replacement Path Problem. Recently (Chechik and Cohen, SODA 2019) designed a randomized combinatorial algorithm that solves the Single Source Replacement Path Problem in $\tilde O(m\sqrt n\ + n^2)$ time. One of the questions left unanswered by their work is the case when there are many sources, not one. When there are $n$ sources, the combinatorial algorithm of (Bernstein and Karger, STOC 2009) can be used to find all pair replacement path in $\tilde O(mn + n^3)$ time. However, there is no result known for any general $\sigma$. Thus, the problem we study is defined as follows: given a set of $\sigma$ sources, we want to find the replacement path from these sources to all vertices in $G$. We give a randomized combinatorial algorithm for this problem that takes $\tilde O(m\sqrt{n \sigma} +\ \sigma n^2)$ time. This result generalizes both results known for this problem. Our algorithm is much different and arguably simpler than (Chechik and Cohen, SODA 2019). Like them, we show a matching conditional lower bound using the Boolean Matrix Multiplication conjecture.