On the Richardson extrapolation as applied to the sequential splitting method

I. Faragó, Ágnes Havasi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

It is known from the literature that applying the same ODE solver by using two different step sizes and combining appropriately the obtained numerical solutions at each time step we can increase the convergence order of the method. Moreover, this technique allows us to estimate the absolute error of the underlying method. In this paper we apply this procedure, widely known as Richardson extrapolation, to the sequential splitting, and investigate the performance of the obtained scheme on several test examples.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages184-191
Number of pages8
Volume4818 LNCS
DOIs
Publication statusPublished - 2008
Event6th International Conference on Large-Scale Scientific Computing, LSSC 2007 - Sozopol, Bulgaria
Duration: Jun 5 2007Jun 9 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4818 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Conference on Large-Scale Scientific Computing, LSSC 2007
CountryBulgaria
CitySozopol
Period6/5/076/9/07

Fingerprint

Richardson Extrapolation
Sequential Methods
Splitting Method
Extrapolation
Order of Convergence
Numerical Solution
Estimate

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Faragó, I., & Havasi, Á. (2008). On the Richardson extrapolation as applied to the sequential splitting method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4818 LNCS, pp. 184-191). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4818 LNCS). https://doi.org/10.1007/978-3-540-78827-0_19

On the Richardson extrapolation as applied to the sequential splitting method. / Faragó, I.; Havasi, Ágnes.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4818 LNCS 2008. p. 184-191 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4818 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Faragó, I & Havasi, Á 2008, On the Richardson extrapolation as applied to the sequential splitting method. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4818 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4818 LNCS, pp. 184-191, 6th International Conference on Large-Scale Scientific Computing, LSSC 2007, Sozopol, Bulgaria, 6/5/07. https://doi.org/10.1007/978-3-540-78827-0_19
Faragó I, Havasi Á. On the Richardson extrapolation as applied to the sequential splitting method. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4818 LNCS. 2008. p. 184-191. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-78827-0_19
Faragó, I. ; Havasi, Ágnes. / On the Richardson extrapolation as applied to the sequential splitting method. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4818 LNCS 2008. pp. 184-191 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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