Radial basis function methods are modern ways to approximate multivariate functions,
especially in the absence of grid data. They have been known, tested and analysed for …

Many practical applications require the reconstruction of a multivariate function from
discrete, unstructured data. This book gives a self-contained, complete introduction into this …

In our previous work, an effective preconditioning scheme that is based upon constructing
least-squares approximation cardinal basis functions (ACBFs) from linear combinations of …

Although meshless radial basis function (RBF) methods applied to partial differential
equations (PDEs) are not only simple to implement and enjoy exponential convergence …

A review of interpolation to values of a function f (x), x 2 Rd, by radial basis function methods
is given. It addresses the nonsingularity of the interpolation equations, the inclusion of …

We consider the version of multiquadric interpolation where the interpolation conditions are
the equations s (xi)= fi, i= 1, 2,…, n, and where the interpolant has the form s (x)=∑ j= 1 n λ j …

The inverse matrix of radial basis function (RBF) interpolation systems can be stated
concisely in terms of an inverse with respect to the semi-inner product induced by the …

Based on the NEWUOA algorithm, a new derivative-free algorithm is developed, named
LCOBYQA. The main aim of the algorithm is to find a minimizer x^*∈R^n of a non-linear …

Radial basis functions (RBFs) have originally been used as an effective tool to interpolate
data. The basis functions (all translations of one radial symmetric function) can be centered …