Multiscale radial kernels with high-order generalized Strang-Fix conditions

  • Wenwu Gao
  • Xuan ZhouEmail author
Original Paper


The paper provides a general and simple approach for explicitly constructing multiscale radial kernels with high-order generalized Strang-Fix conditions from a given univariate generator. The resulting kernels are constructed by taking a linear functional to the scaled f -form of the generator with respect to the scale variable. Equivalent divided difference forms of the kernels are also derived; based on which, a pyramid-like algorithm for fast and stable computation of multiscale radial kernels is proposed. In addition, characterizations of the kernels in both the spatial and frequency domains are given, which show that the generalized Strang-Fix condition, the moment condition, and the condition of polynomial reproduction in the convolution sense are equivalent to each other. Hence, as a byproduct, the paper provides a unified view of these three classical concepts. These kernels can be used to construct quasi-interpolation with high approximation accuracy and construct convolution operators with high approximation orders, to name a few. As an example, we construct a quasi-interpolation scheme for irregularly spaced data and derived its error estimates and choices of scale parameters of multiscale radial kernels. Numerical results of approximating a bivariate Franke function using our quasi-interpolation are presented at the end of the paper. Both theoretical and numerical results show that quasi-interpolation with multiscale radial kernels satisfying high-order generalized Strang-Fix conditions usually provides high approximation orders.


Radial function Multiscale radial kernel Generalized Strang-Fix condition Generator Fourier transform 

Mathematics Subject Classification (2010)

41A05 41065 65D05 65D10 65D15 



The authors acknowledge the associate editor and the anonymous referee for insightful comments and valuable suggestions.

Funding information

This work is financially supported by NSFC (11871074, 11501006, 61672032), NSFC Key Project (91330201,11631015), SGST (12DZ 2272800), Joint Research Fund by the National Natural Science Foundation of China and Research Grants Council of Hong Kong (11461161006), Fund of Introducing Leaders of Science and Technology of Anhui University (J10117700057) and the 4th Project of Cultivating Backbone of Young Teachers of Anhui University (J01005138), and Anhui Provincial Science and Technology Major Project (16030701091).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Statistics, School of EconomicsAnhui UniversityHefeiPeople’s Republic of China
  2. 2.Anhui Engineering Laboratory of Agro-Ecological Big DataAnhui UniversityHefeiPeople’s Republic of China
  3. 3.Shanghai Key Laboratory for Contemporary Applied MathematicsFudan UniversityShanghaiPeople’s Republic of China
  4. 4.School of Mathematical SciencesFudan UniversityShanghaiPeople’s Republic of China

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