### Abstract

Original language | English |
---|---|

Number of pages | 14 |

Journal | arXiv.org e-Print archive |

Publication status | Published - 25 Jan 2019 |

### Fingerprint

### Keywords

- cs.CG

### Cite this

**Metric Spaces with Expensive Distances.** / Kerber, Michael; Nigmetov, Arnur.

Research output: Contribution to journal › Article › Research

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TY - JOUR

T1 - Metric Spaces with Expensive Distances

AU - Kerber, Michael

AU - Nigmetov, Arnur

N1 - 14 pages, 10 figures

PY - 2019/1/25

Y1 - 2019/1/25

N2 - In algorithms for finite metric spaces, it is common to assume that the distance between two points can be computed in constant time, and complexity bounds are expressed only in terms of the number of points of the metric space. We introduce a different model where we assume that the computation of a single distance is an expensive operation and consequently, the goal is to minimize the number of such distance queries. This model is motivated by metric spaces that appear in the context of topological data analysis. We consider two standard operations on metric spaces, namely the construction of a $1+\varepsilon$-spanner and the computation of an approximate nearest neighbor for a given query point. In both cases, we partially explore the metric space through distance queries and infer lower and upper bounds for yet unexplored distances through triangle inequality. For spanners, we evaluate several exploration strategies through extensive experimental evaluation. For approximate nearest neighbors, we prove that our strategy returns an approximate nearest neighbor after a logarithmic number of distance queries.

AB - In algorithms for finite metric spaces, it is common to assume that the distance between two points can be computed in constant time, and complexity bounds are expressed only in terms of the number of points of the metric space. We introduce a different model where we assume that the computation of a single distance is an expensive operation and consequently, the goal is to minimize the number of such distance queries. This model is motivated by metric spaces that appear in the context of topological data analysis. We consider two standard operations on metric spaces, namely the construction of a $1+\varepsilon$-spanner and the computation of an approximate nearest neighbor for a given query point. In both cases, we partially explore the metric space through distance queries and infer lower and upper bounds for yet unexplored distances through triangle inequality. For spanners, we evaluate several exploration strategies through extensive experimental evaluation. For approximate nearest neighbors, we prove that our strategy returns an approximate nearest neighbor after a logarithmic number of distance queries.

KW - cs.CG

M3 - Article

JO - arXiv.org e-Print archive

JF - arXiv.org e-Print archive

ER -