Smallest enclosing ball for probabilistic data

Alexander Munteanu, Christian Sohler, Dan Feldman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper deals with computing the smallest enclosing ball of a set of points subject to probabilistic data. In our setting, any of the n points may not or may occur at one of finitely many locations, following its own discrete probability distribution. The objective is therefore considered to be a random variable and we aim at finding a center minimizing the expected maximum distance to the points according to their distributions. Our main contribution presented in this paper is the first polynomial time (1 + ε)-approximation algorithm for the probabilistic smallest enclosing ball problem with extensions to the streaming setting.

Original languageEnglish
Title of host publicationProceedings of the 30th Annual Symposium on Computational Geometry, SoCG 2014
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Print)9781450325943
StatePublished - 2014
Externally publishedYes
Event30th Annual Symposium on Computational Geometry, SoCG 2014 - Kyoto, Japan
Duration: 8 Jun 201411 Jun 2014

Publication series

NameProceedings of the Annual Symposium on Computational Geometry


Conference30th Annual Symposium on Computational Geometry, SoCG 2014


  • 1-median
  • Probabilistic data
  • Sampling
  • Smallest enclosing ball

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Geometry and Topology
  • Computational Mathematics


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