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When the data are unevenly distributed and the behaviour of a function changes abruptly, the approximant can present undue oscillations. We present an algorithm to identify a domain decomposition, such that on each subdomain the behaviour of the function is sufficiently homogeneous in order to calculate separate approximants and to blend them together. © 2013 IMACS. Published by Elsevier B.V. All rights reserved.

In this paper our concern is the recovery of a highly regular function by a discrete set of data with arbitrary distribution. We consider the case of a nonstationary multiquadric interpolant that presents numerical breakdown. Therefore we propose a global least squares multiquadric approximant with a center set of maximal size and obtained by a new thinning technique. The new thinning scheme removes the local bad conditions in order to obtain a well conditioned matrix. The choice of working on local subsets of the data set $X$ provides an effective solution. Some numerical examples to validate the goodness of our proposal are given.

We propose an adaptive local procedure, which uses the modified Shepard's method with local polyharmonic interpolants. The aim is to reconstruct, in a faithful way, a function known by a large and highly irregularly distributed sample. Such a problem is generally related to the recovering of geophysical surfaces, where the sample is measured according to the behaviour of the surface. The adaptive local procedure is used to calculate, by an efficient algorithm, an interpolating polyharmonic function, when a very large sample is assigned. When we consider a sample of size $N<104$, we propose an approximating polyharmonic function obtained by combining adaptively a global interpolant, relevant to a subset of the data, with local adaptive interpolants. The goodness of the approximating functions in two different cases is shown by real examples.

Many papers discuss the problem of recovering a function when a set of data is known on a domain. Most of these papers assume that the size of the data set is large. At our knowledge, little or practically nothing is said about the case in which the sample has a size that is moderate. In this paper we tackle this problem. We indicate a way for its solution and hence we give a concrete example. Some numerical experiments are shown

The aim of the paper is to provide a method for approximating non regular surfaces from a set of scattered data in a faithful way. The method we propose is effective and particularly well-suited for recovering geophysical surfaces with faults or drainage patterns. Some real examples will be presented. © Springer-Verlag 2014.

The aim of the paper is to provide a computationally effective way to construct stable bases on general non-degenerate lattices. In particular, we define new stable bases on hexagonal lattices and we give some numerical examples which show their usefulness in applications.

The aim of this paper is to provide a fast method, with a good quality of reproduction, to recover functions from very large and irregularly scattered samples of noisy data, which may present outliers. To the given sample of size N, we associate a uniform grid and, around each grid point, we condense the local information given by the noisy data by a suitable estimator. The recovering is then performed by a stable interpolation based on isotropic polyharmonic B-splines. Due to the good approximation rate, we need only M ≪ N degrees of freedom to recover the phenomenon faithfully. © 2010 Elsevier Inc. All rights reserved.

This paper applies difference operators to conditionally positive definite kernels in order to generate kernel B-splines that have fast decay towards infinity. Interpolation by these new kernels provides better condition of the linear system, while the kernel B-spline inherits the approximation orders from its native kernel. We proceed in two different ways: either the kernel B -spline is constructed adaptively on the data knot set X , or we use a fixed difference scheme and shift its associated kernel B-spline around. In the latter case, the kernel B-spline so obtained is strictly positive in general. Furthermore, special kernel B-splines obtained by hexagonal second finite differences of multiquadrics are studied in more detail. We give suggestions in order to get a consistent improvement of the condition of the interpolation matrix in applications. © Springer Science 2006.

Under very mild additional assumptions, translates of conditionally positive definite radial basis functions allow unique interpolation to scattered multivariate data, because the interpolation matrices have a symmetric and positive definite dominant part. In many applications, the data density varies locally according to the signal behaviour, and then the translates should get different scalings that match the local data density. Furthermore, if there is a local anisotropy in the data, the radial basis functions should possibly be distorted into functions with ellipsoids as level sets. In such cases, the symmetry and the definiteness of the matrices are no longer guaranteed. However, this brief note is the first paper to provide sufficient conditions for the unique solvability of such interpolation processes. The basic technique is a simple matrix perturbation argument combined with the Ball-Narcowich-Ward stability results. © Springer-Verlag 2004.

We present an adaptive method to extract shape-preserving information from a univariate data sample. The behavior of the signal is obtained by interpolating at adaptively selected few data points by a linear combination of multiquadrics with variable scaling parameters. On the theoretical side, we give a sufficient condition for existence of the scaled multiquadric interpolant. On the practical side, we give various examples to show the applicability of the method

In this paper a local type method is proposed to smooth noisy data. Criteria of convergence and error bounds are given. An applciation is also presented for a biomedical problem in R3, which is usually solved only in R2. © 1984 Springer-Verlag.

The problem of the numerical approximation of multivariable functions has been solved by the Monte Carlo method when the data points are assumed to be given on discrete lattice points [5, 8, 2]. When the data points are randomly distributed and very numerous there are some results in the literature [3, 6] but if the number of the points is less than 2k, where k is the dimension of the space, it is very difficult to develop approximation formulas. This paper gives a solution to this problem by local approximations. © 1981 Springer-Verlag.

For clinical application of heart potential mapping, a reduced set of leads is determined, yet suitable to represent all possible phenomenologies. The analysis of the reliability of the model is done and some numerical results are presented. © 1985.

In this paper the authors develope a new Montecarlo method for many variables interpolation when the values of the function are known on a lattice. This technique uses cardinal splines functions. Numerical results are also reported for some problems. © 1975 Instituto di Elaborazione della Informazione del CNR