Preconditioning of Radial Basis Function Interpolation Systems via Accelerated Iterated Approximate Moving Least Squares Approximation

DOI10.1007/978-1-4020-8821-6_4zbMath1159.65320OpenAlexW2146303224MaRDI QIDQ3613536

Gregory E. Fasshauer, Jack G. Zhang

Publication date: 12 March 2009

Published in: Progress on Meshless Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-1-4020-8821-6_4

zbMATH Keywords

radial basis functionspreconditioning methodsaccelerated iterated least squares

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Numerical interpolation (65D05) Numerical computation of matrix norms, conditioning, scaling (65F35)

Related Items (12)

An improved radial basis-pseudospectral method with hybrid Gaussian-cubic kernels ⋮ Moving Least Squares (MLS) Method for the Nonlinear Hyperbolic Telegraph Equation with Variable Coefficients ⋮ Unconditional convergence of iterative approximation methods ⋮ Application of Hilbert-Schmidt SVD approach to solve linear two-dimensional Fredholm integral equations of the second kind ⋮ Reprint of: A forward-backward greedy approach for sparse multiscale learning ⋮ Spectral methods for solving elliptic PDEs on unknown manifolds ⋮ Hierarchical regularization networks for sparsification based learning on noisy datasets ⋮ A Fully Implicit Method Using Nodal Radial Basis Functions to Solve the Linear Advection Equation ⋮ Hybrid Gaussian-cubic radial basis functions for scattered data interpolation ⋮ A new stable basis for radial basis function interpolation ⋮ A boundary integral method for modelling vibroacoustic energy distributions in uncertain built up structures ⋮ A forward-backward greedy approach for sparse multiscale learning

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