Computing the Gromov-Hausdorff Distance for Metric Trees
Вычисление расстояния Громова—Хаусдорфа для метрических деревьев
2018-04-16
SCID: 54.1/945z3ke8
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Gromov-Hausdorff distanceNP-hardnessapproximation algorithmgeodesic metricsmetric trees
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Abstract (AI)
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 GH 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 (min n , √ 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 1 .
Key Findings
1
A polynomial-time O(min(n, √(rn))) approximation algorithm is provided for computing the Gromov–Hausdorff distance between metric trees.
2
Approximating the Gromov–Hausdorff distance within a factor better than 3 is NP-hard for geodesic metrics on pairs of trees.
3
For metric trees with unit-length edges, the algorithm achieves an O(√n) approximation.
4
The approximation factor depends on n and the edge-length ratio r between the longest and shortest edges across both trees.
Research Object
a pair of metric trees
Research Subject
the computational complexity and approximation of the Gromov–Hausdorff distance between metric trees
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2018-04-16
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