English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We provide a general framework for getting expected linear time constantfactor approximations (and in many cases FPTAS's) to several well knownproblems in Computational Geometry, such as $k$-center clustering and farthestnearest neighbor. The new approach is robust to variations in the inputproblem, and yet it is simple, elegant and practical. In particular, many ofthese well studied problems which fit easily into our framework, eitherpreviously had no linear time approximation algorithm, or required ratherinvolved algorithms and analysis. A short list of the problems we considerinclude farthest nearest neighbor, $k$-center clustering, smallest diskenclosing $k$ points, $k$th largest distance, $k$th smallest $m$-nearestneighbor distance, $k$th heaviest edge in the MST and other spanning foresttype problems, problems involving upward closed set systems, and more. Finally,we show how to extend our framework such that the linear running time boundholds with high probability.

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