Computing Euclidean bottleneck matchings in higher dimensions

Alon Efrat; Matthew J. Katz

doi:10.1016/S0020-0190(00)00096-X

Computing Euclidean bottleneck matchings in higher dimensions

Alon Efrat, Matthew J. Katz

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

We extend the planar results of Chang et al. (1992) to higher dimensions, and show that given a set A of 2n points in d-space it is possible to compute a Euclidean bottleneck matching of A in roughly O(n^1.5) time, for d≤6, and in subquadratic time, for any constant d>6. If the underlying norm is L_∞, then it is possible to compute a bottleneck matching of A in O(n^1.5 log^0.5 n) time, for any constant d≥2.

Original language	English (US)
Pages (from-to)	169-174
Number of pages	6
Journal	Information Processing Letters
Volume	75
Issue number	4
DOIs	https://doi.org/10.1016/S0020-0190(00)00096-X
State	Published - Sep 30 2000
Externally published	Yes

ASJC Scopus subject areas

Theoretical Computer Science
Signal Processing
Information Systems
Computer Science Applications

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10.1016/S0020-0190(00)00096-X

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AB - We extend the planar results of Chang et al. (1992) to higher dimensions, and show that given a set A of 2n points in d-space it is possible to compute a Euclidean bottleneck matching of A in roughly O(n1.5) time, for d≤6, and in subquadratic time, for any constant d>6. If the underlying norm is L∞, then it is possible to compute a bottleneck matching of A in O(n1.5 log0.5 n) time, for any constant d≥2.

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Computing Euclidean bottleneck matchings in higher dimensions

Abstract

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