English

Computing the Gromov-Hausdorff Distance for Metric Trees

Computational Geometry 2017-06-14 v2

Abstract

The Gromov-Hausdorff (GH) distance is a natural way to measure distance between two metric spaces. We prove that it is NP\mathrm{NP}-hard to approximate the Gromov-Hausdorff distance better than a factor of 33 for geodesic metrics on a pair of trees. We complement this result by providing a polynomial time O(min{n,rn})O(\min\{n, \sqrt{rn}\})-approximation algorithm for computing the GH distance between a pair of metric trees, where rr is the ratio of the longest edge length in both trees to the shortest edge length. For metric trees with unit length edges, this yields an O(n)O(\sqrt{n})-approximation algorithm.

Keywords

Cite

@article{arxiv.1509.05751,
  title  = {Computing the Gromov-Hausdorff Distance for Metric Trees},
  author = {Pankaj K. Agarwal and Kyle Fox and Abhinandan Nath and Anastasios Sidiropoulos and Yusu Wang},
  journal= {arXiv preprint arXiv:1509.05751},
  year   = {2017}
}

Comments

Appeared in Proceedings of the 26th International Symposium on Algorithms and Computation

R2 v1 2026-06-22T11:00:11.609Z