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Anderson accelerated preconditioning iterative method for RBF interpolation
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AI
DOI：10.1016/j.enganabound.2024.105970.png)
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Abstract
En 中文
Traditional RBF interpolation involves solving a linear system, making it computationally expensive for large datasets. Iterative-based quasi-interpolation combines RBF interpolation with iterative methods to enhance accuracy and convergence. To enhance efficiency and accuracy, we in this paper propose a novel method for RBF quasi-interpolation that combines Anderson acceleration with the asynchronous DCPI, termed Anderson-DCPI. The method alternates between the preconditioning iterative method and Anderson extrapolation, aiming to improve convergence rates. We demonstrate the convergence of Anderson-DCPI for positive definite RBF kernel functions and validate its effectiveness through a series of numerical examples.
Keywords:
DCPI
Preconditioning technique
Anderson acceleration
Radial basis function
Quasi-interpolation
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