Fast fitting of radial basis functions: Methods based on preconditioned GMRES iteration

Abstract

Solving large radial basis function (RBF) interpolation problems with non‐customised methods is computationally expensive and the matrices that occur are typically badly conditioned. For example, using the usual direct methods to fit an RBF with N centres requires O(N ²) storage and O(N ³) flops. Thus such an approach is not viable for large problems with N ≥10,000.

In this paper we present preconditioning strategies which, in combination with fast matrix–vector multiplication and GMRES iteration, make the solution of large RBF interpolation problems orders of magnitude less expensive in storage and operations. In numerical experiments with thin‐plate spline and multiquadric RBFs the preconditioning typically results in dramatic clustering of eigenvalues and improves the condition numbers of the interpolation problem by several orders of magnitude. As a result of the eigenvalue clustering the number of GMRES iterations required to solve the preconditioned problem is of the order of 10-20. Taken together, the combination of a suitable approximate cardinal function preconditioner, the GMRES iterative method, and existing fast matrix–vector algorithms for RBFs [4,5] reduce the computational cost of solving an RBF interpolation problem to O(N) storage, and O(N \log N) operations.