2014•TU/e Research PortalOpen access

Distribution-sensitive construction of the greedy spanner (extended abstract)

Spa Sander Alewijnse, QW Quirijn Bouts, ten Ap Alex Brink, Kevin Buchin

Open full text 1 citations

Abstract

The greedy spanner is the highest quality geometric spanner (in e.g. edge count and weight, both in theory and practice) known to be computable in polynomial time. Unfortunately, all known algorithms for computing it take O(n^2) time, limiting its applicability on large data sets.\nWe observe that for many point sets, the greedy spanner has many ‘short’ edges that can be determined locally and usually quickly, and few or no ‘long’ edges that can usually be determined quickly using local information and the well-separated pair decomposition. We give experimental results showing large to massive performance increases over the state-of-the-art on nearly all tests and real-life data sets. On the theoretical side we prove a near-linear expected time bound on uniform point sets and a near-quadratic worst-case bound.\nOur bound for point sets drawn uniformly and independently at random in a square follows from a local characterization of t-spanners we give on such point sets.\nThis characterization gives a O(n log^2 n log^2 log n) expected time bound on our greedy spanner algorithm, making it the first subquadratic time algorithm for this problem on any interesting class of points.

Open-access reader

About this research paper

What this paper is about

The greedy spanner is the highest quality geometric spanner (in e.g. edge count and weight, both in theory and practice) known to be computable in polynomial time. Unfortunately, all known algorithms for computing it take O(n^2) time, limiting its applicability on large data sets.\nWe observe that for many point sets, the greedy spanner has many ‘short’ edges that can be determined locally and usually quickly, and few or no ‘long’ edges that can usually be determined quickly using local information and the well-separated pair decomposition. We give experimental results showing large to massive performance increases over the state-of-the-art on nearly all tests and real-life data sets. On the theoretical side we prove a near-linear expected time bound on uniform point sets and a near-quadratic worst-case bound.\nOur bound for point sets drawn uniformly and independently at random in a square follows from a local characterization of t-spanners we give on such point sets.\nThis characterization gives a O(n log^2 n log^2 log n) expected time bound on our greedy spanner algorithm, making it the first subquadratic time algorithm for this problem on any interesting class of points.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

The greedy spanner is the highest quality geometric spanner (in e.g. edge count and weight, both in theory and practice) known to be computable in polynomial time. Unfortunately, all known algorithms for computing it take O(n^2) time, limiting its applicability on large data sets.\nWe observe that for many point sets, the greedy spanner has many ‘short’ edges that can be determined locally and usually quickly, and few or no ‘long’ edges that can usually be determined quickly using local information and the well-separated pair decomposition. We give experimental results showing large to massive performance increases over the state-of-the-art on nearly all tests and real-life data sets. On the theoretical side we prove a near-linear expected time bound on uniform point sets and a near-quadratic worst-case bound.\nOur bound for point sets drawn uniformly and independently at random in a square follows from a local characterization of t-spanners we give on such point sets.\nThis characterization gives a O(n log^2 n log^2 log n) expected time bound on our greedy spanner algorithm, making it the first subquadratic time algorithm for this problem on any interesting class of points.

Key concepts: Spanner, Upper and lower bounds, Combinatorics, Time complexity, Greedy algorithm, Ovoid, Mathematics, Characterization (materials science)

Related papers

Back to paper searchBrowse research topicsOriginal source
Distribution-sensitive construction of the greedy spanner (extended abstract) — Research Paper | ScholarLens