Net and Prune: A Linear Time Algorithm for Euclidean Distance Problems

TL;DR

This paper introduces a simple, robust, and practical linear-time framework for approximating various computational geometry problems, significantly improving efficiency over previous methods.

Contribution

The authors present a novel general framework that achieves expected linear time approximations for multiple geometry problems, many of which lacked such efficient algorithms before.

Findings

Achieved expected linear time approximations for several geometry problems.

Framework is robust, simple, and practical for various problem variations.

Extended the framework to ensure high-probability linear time performance.

Abstract

We provide a general framework for getting expected linear time constant factor approximations (and in many cases FPTASs) to several well-known problems in Computational Geometry, such as $k$-center clustering and farthest nearest neighbor. The new approach is robust to variations in the input problem, and yet it is simple, elegant, and practical. In particular, many of these well-studied problems, which fit easily into our framework, either previously had no linear time approximation algorithm, or required rather involved algorithms and analysis. A short list of the problems we consider includes farthest nearest neighbor, $k$-center clustering, smallest disk enclosing $k$ points, Hausdorff distance, $k$th largest distance, $k$th smallest $m$-nearest neighbor distance, $k$th heaviest edge in the MST, and other spanning-forest type problems, problems involving upward closed set systems, and more. Finally, we show how to extend our framework such that the linear running time bound holds with high probability.