Computing the Gromov-Hausdorff Distance for Metric Trees
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Abstract (arXiv)
The Gromov-Hausdorff (GH) distance is a natural way to measure distance between two metric spaces. We prove that it is NP-hard to approximate the Gromov-Hausdorff distance better than a factor of 3 for geodesic metrics on a pair of trees. We complement this result by providing a polynomial time O(minn, √rn)-approximation algorithm for computing the GH distance between a pair of metric trees, where r 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)-approximation algorithm.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Sep 2015.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2024Rapid and Precise Topological Comparison with Merge Tree Neural Networkscited by 2
- 2021The Universal ℓp-Metric on Merge Treescited by 0
- 2024Fitting trees to ℓ1-hyperbolic distancescited by 0
- 2026Quotient Geometry and Persistence-Stable Metrics for Swarm Configurationscited by 0
- 2026Quotient Geometry and Persistence-Stable Metrics for Swarm Configurations14 Mar 2026
- 2024Fitting trees to ℓ1-hyperbolic distances2 Sep 2024
- 2024Rapid and Precise Topological Comparison with Merge Tree Neural Networks8 Apr 2024
- 2021The Universal ℓp-Metric on Merge Trees22 Dec 2021