Dimension-splitting data points redistribution for meshless approximation

Ting On Kwok, Leevan LING*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)


To better approximate nearly singular functions with meshless methods, we propose a data points redistribution method extended from the well-known one-dimensional equidistribution principle. With properly distributed data points, nearly singular functions can be well approximated by linear combinations of global radial basis functions. The proposed method is coupled with an adaptive trial subspace selection algorithm in order to reduce computational cost. In our numerical examples, clear exponential convergence (with respect to the numbers of data points) can be observed.

Original languageEnglish
Pages (from-to)736-746
Number of pages11
JournalJournal of Computational and Applied Mathematics
Issue number3
Publication statusPublished - 1 Dec 2010

Scopus Subject Areas

  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Adaptive greedy algorithm
  • Dual reciprocity method
  • Exponential convergence
  • Meshless interpolation
  • Radial basis function


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