mdh.sePublications
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Minimum Enclosing Balls and Ellipsoids in General Dimensions
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-6969-6793
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this doctoral thesis, we study the problem of computing the ball of smallest radius enclosing a given set of points in any number of dimensions. Variations of this problem arise in several branches of computer science, such as computer graphics, artificial intelligence, and operations research. Applications range from collision detection for three-dimensional models in video games and computer-aided design, to high-dimensional clustering and classification in machine learning and data mining. We also consider the related and more challenging problem of finding the enclosing ellipsoid of minimum volume. Such ellipsoids can provide more descriptive data representations in the aforementioned applications, and they find further utility in, for example, optimal design of experiments and trimming of outliers in statistics.

The contributions of this thesis consist of practical methods for the efficient solution of these two problems, with a primary focus on problem instances involving a large number of points. We introduce new algorithms to compute arbitrarily fine approximations of the minimum enclosing ball or ellipsoid in general dimensions. In our experimental evaluations, these algorithms exhibit running times that are highly competitive with, and often markedly superior to, those of earlier algorithms from the literature. Moreover, we present a new out-of-core algorithm to compute the exact minimum enclosing ball for massive, low-dimensional point sets residing in secondary storage. In addition to these solution methods, we develop acceleration techniques that can further improve their performance, either by using pruning heuristics to reduce the amount of work performed in each iteration, or by utilizing parallel hardware features of modern processors and graphics processing units. These techniques are also applicable to several existing algorithms.

Place, publisher, year, edition, pages
Västerås: Mälardalen University , 2019.
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 300
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-45863ISBN: 978-91-7485-448-0 (print)OAI: oai:DiVA.org:mdh-45863DiVA, id: diva2:1366471
Public defence
2020-01-10, Beta, Mälardalens högskola, Västerås, 13:15
Opponent
Available from: 2019-11-07 Created: 2019-10-29 Last updated: 2019-11-21Bibliographically approved

Open Access in DiVA

fulltext(1610 kB)113 downloads
File information
File name FULLTEXT01.pdfFile size 1610 kBChecksum SHA-512
4b3c9682ca916b9d62e821c13831182e38150132b4148a27c7966037bed844c3b10f4e2120aada85f9d69f97abf76698ce695d0ebeab3ccd7f4f22cbf2b33bad
Type fulltextMimetype application/pdf

Authority records BETA

Källberg, Linus

Search in DiVA

By author/editor
Källberg, Linus
By organisation
Embedded Systems
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 113 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 100 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf