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A modification on strictly positive definite RBF-DQ method based on matrix decomposition

机译:基于矩阵分解的严格正定RBF-DQ方法的修正

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The infinitely smooth RBF methods are theoretically spectrally accurate for applying on scattered data interpolation, and also partial differential equations, but the interpolation matrices of them are extremely ill-conditioned especially for strictly positive definite ones. Therefore, an efficient technique to recover this problem is too important. In this article, a general matrix decomposition method for strictly positive definite RBFs interpolation matrix has been investigated. In the current decomposition the RBFs interpolation matrix is obtained as multiplication of some well-conditioned matrices. This decomposition has been applied to RBF-DQ method and its results more accurate weight coefficients when we involve solving PDEs.
机译:无限平滑RBF方法在理论上在频谱上准确,适用于散乱数据插值,也适用于偏微分方程,但是它们的插值矩阵特别适用于严格正定的条件,因此病态极为恶劣。因此,有效的技术来解决此问题太重要了。本文研究了一种严格的正定RBFs插值矩阵的通用矩阵分解方法。在当前的分解中,RBFs内插矩阵是一些条件良好的矩阵的乘积。此分解已应用于RBF-DQ方法,当涉及到PDE求解时,其结果可得到更准确的权重系数。

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