FPT-algorithms for computing Gromov-Hausdorff and interleaving distances between trees
Elena Farahbakhsh Touli, Yusu Wang

TL;DR
This paper introduces a fixed-parameter tractable algorithm to approximate the Gromov-Hausdorff distance between metric trees within a factor of 14, leveraging a novel connection to interleaving distances of merge trees.
Contribution
It develops an FPT algorithm for approximating GH distance between trees by redefining interleaving distance and computing it exactly, improving previous approximation bounds.
Findings
FPT algorithm approximates GH distance within factor 14
Exact FPT algorithm for interleaving distance between merge trees
Improved approximation bounds over previous methods
Abstract
Gromov-Hausdorff (GH) distance is a natural way to measure the distortion between two metric spaces. However, there has been only limited algorithmic development to compute or approximate this distance. We focus on computing the Gromov-Hausdorff distance between two metric trees. Roughly speaking, a metric tree is a metric space that can be realized by the shortest path metric on a tree. Previously, Agarwal et al. showed that even for trees with unit edge length, it is NP hard to approximate the GH distance between them within a factor of 3. In this paper, we present a fixed-parameter tractable (FPT) algorithm that can approximate the GH distance between two general metric trees within a factor of 14. Interestingly, the development of our algorithm is made possible by a connection between the GH distance for metric trees and the interleaving distance for the so-called merge trees. The…
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Taxonomy
TopicsTopological and Geometric Data Analysis · Digital Image Processing Techniques · Data Management and Algorithms
