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Solving the Linear Integral Equations Based on Radial Basis Function Interpolation

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  • Huaiqing Zhang
  • Yu Chen
  • Xin Nie

Abstract

The radial basis function (RBF) method, especially the multiquadric (MQ) function, was introduced in solving linear integral equations. The procedure of MQ method includes that the unknown function was firstly expressed in linear combination forms of RBFs, then the integral equation was transformed into collocation matrix of RBFs, and finally, solving the matrix equation and an approximation solution was obtained. Because of the superior interpolation performance of MQ, the method can acquire higher precision with fewer nodes and low computations which takes obvious advantages over thin plate splines (TPS) method. In implementation, two types of integration schemes as the Gauss quadrature formula and regional split technique were put forward. Numerical results showed that the MQ solution can achieve accuracy of . So, the MQ method is suitable and promising for integral equations.

Suggested Citation

  • Huaiqing Zhang & Yu Chen & Xin Nie, 2014. "Solving the Linear Integral Equations Based on Radial Basis Function Interpolation," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-8, June.
    • Handle: RePEc:hin:jnljam:793582
    DOI: 10.1155/2014/793582
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    Cited by:

    1. Andrey Itkin & Dmitry Muravey, 2021. "Semi-analytical pricing of barrier options in the time-dependent $\lambda$-SABR model," Papers 2109.02134, arXiv.org.
    2. Andrey Itkin & Dmitry Muravey, 2023. "American options in time-dependent one-factor models: Semi-analytic pricing, numerical methods and ML support," Papers 2307.13870, arXiv.org.

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