Gromov's Approximating Tree and the All-Pairs Bottleneck Paths Problem

TL;DR

This paper explores the relationship between Gromov's approximating tree for metric spaces and the all-pairs bottleneck paths problem, providing reductions and algorithms that connect these concepts.

Contribution

It establishes reductions between computing Gromov's approximating tree and solving the APBP problem, and presents an explicit quadratic-time algorithm for the tree construction.

Findings

Computing Gromov's approximating tree reduces to solving APBP.

An explicit quadratic-time algorithm for Gromov's tree construction exists.

APBP problem reduces to finding Gromov's approximating tree.

Abstract

Given a pointed metric space $(X,\mathsf{dist}, w)$ on $n$ points, its Gromov's approximating tree is a 0-hyperbolic pseudo-metric space $(X,\mathsf{dist}_T)$ such that $\mathsf{dist}(x,w)=\mathsf{dist}_T(x,w)$ and $\mathsf{dist}(x, y)-2 \delta \log_2n \leq \mathsf{dist}_T (x, y) \leq \mathsf{dist}(x, y)$ for all $x, y \in X$ where $\delta$ is the Gromov hyperbolicity of $X$. On the other hand, the all pairs bottleneck paths (APBP) problem asks, given an undirected graph with some capacities on its edges, to find the maximal path capacity between each pair of vertices. In this note, we prove: $\bullet$ Computing Gromov's approximating tree for a metric space with $n+1$ points from its matrix of distances reduces to solving the APBP problem on an connected graph with $n$ vertices. $\bullet$ There is an explicit algorithm that computes Gromov's approximating tree for a graph from its adjacency matrix in quadratic time. $\bullet$ Solving the APBP problem on a weighted graph with $n$ vertices reduces to finding Gromov's approximating tree for a metric space with $n+1$ points from its distance matrix.