Computing the Gromov-Hausdorff Distance for Metric Trees
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Association for Computing Machinery
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Abstract
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
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Embedded computer systems, Metric spaces, Approximation algorithms, Set theory, Hausdorff distance, Longest edge, Measure measures, Metric trees, Trees--Mathematical models, Topological spaces
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NSF grants CCF-09-40671, CCF-10-12254, CCF-11-61359, CCF– 1319406, CCF-1423230, CAREER-1453472, IIS-14-08846, and by grant 2012/229 from the U.S.-Israel Binational Science Foundation.
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