A kind of multiquadric quasi-interpolation operator satisfying any degree polynomial reproduction property to scattered data

In this paper, by virtue of using the linear combinations of the shifts of f(x) to approximate the derivatives of f(x) and Waldron’s superposition idea (2009), we modify a multiquadric quasi-interpolation with the property of linear reproducing to scattered data on one-dimensional space, such that a kind of quasi-interpolation operator L_r+1f has the property of r+1(r∈Z,r≥0) degree polynomial reproducing and converges up to a rate of r+2. There is no demand for the derivatives of f in the proposed quasi-interpolation L_r+1f, so it does not increase the orders of smoothness of f. Finally, some numerical experiments are shown to compare the approximation capacity of our quasi-interpolation operators with that of Wu-Schaback’s quasi-interpolation scheme and Feng-Li’s quasi-interpolation scheme.     

Detection of discontinuities in scattered data approximation

A Detection Algorithm for the localisation of unknown fault lines of a surface from scattered data is given. The method is based on a local approximation scheme using thin plate splines, and we show that this yields approximation of second order accuracy instead of first order as in the global case. Furthermore, the Detection Algorithm works with triangulation methods, and we show their utility for the approximation of the fault lines. The output of our method provides polygonal curves which can be used for the purpose of constrained surface approximation. This revised version was published online in June 2006 with corrections to the Cover Date.     

Least squares surface approximation to scattered data using multiquadratic functions

The paper documents an investigation into some methods for fitting surfaces to scattered data. The form of the fitting function is a multiquadratic function with the criteria for the fit being the least mean squared residual for the data points. The principal problem is the selection of knot points (or base points for the multiquadratic basis functions), although the selection of the multiquadric parameter also plays a nontrivial role in the process. We first describe a greedy algorithm for knot selection, and this procedure is used as an initial step in what follows. The minimization including knot locations and the multiquadric parameter is explored, with some unexpected results in terms of “near repeated” knots. This phenomenon is explored, and leads us to consider variable parameter values for the basis functions. Examples and results are given throughout.     

Multilevel scattered data approximation by adaptive domain decomposition

A new multilevel approximation scheme for scattered data is proposed. The scheme relies on an adaptive domain decomposition strategy using quadtree techniques (and their higher-dimensional generalizations). It is shown in the numerical examples that the new method achieves an improvement on the approximation quality of previous well-established multilevel interpolation schemes. AMS subject classification 65D15, 65D05, 65D07, 65D17     

Quasi-interpolation and approximation via nonseparable scaling function

Quasi-interpolation has been audied in many papers, e.g. , [5]. Here we introduce nonseparable scal-ing function quasi-interpolation and show that its approximation can provide similar convergence propertiesas scalar wavelet system. Several equivalent statements of accuracy of nonseparable scaling function are alsogien. In the numerical experiments, it appears that nonseparable scaling function interpolation has betterconvergonce results than scalar wavelet systems in some cases.     

Quasi-interpolation for linear functional data

Quasi-interpolation has been studied extensively in the literature. However, most studies of quasi-interpolation are usually only for discrete function values (or a finite linear combination of discrete function values). Note that in practical applications, more commonly, we can sample the linear functional data (the discrete values of the right-hand side of some differential equations) rather than the discrete function values (e.g., remote sensing, seismic data, etc). Therefore, it is more meaningful to study quasi-interpolation for the linear functional data. The main result of this paper is to propose such a quasi-interpolation scheme. Error estimate of the scheme is also given in the paper. Based on the error estimate, one can find a quasi-interpolant that provides an optimal approximation order with respect to the smoothness of the right-hand side of the differential equation. The scheme can be applied in many situations such as the numerical solution of the differential equation, construction of the Lyapunov function and so on. Respective examples are presented in the end of this paper.     

Generalization bounds for function approximation from scattered noisy data

We consider the problem of approximating functions from scattered data using linear superpositions of non-linearly parameterized functions. We show how the total error (generalization error) can be decomposed into two parts: an approximation part that is due to the finite number of parameters of the approximation scheme used; and an estimation part that is due to the finite number of data available. We bound each of these two parts under certain assumptions and prove a general bound for a class of approximation schemes that include radial basis functions and multilayer perceptrons. This revised version was published online in June 2006 with corrections to the Cover Date.     

Polyharmonic splines: An approximation method for noisy scattered data of extra-large size

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.     

Univariate hyperbolic tangent neural network approximation

Here we study the univariate quantitative approximation of real and complex valued continuous functions on a compact interval or all the real line by quasi-interpolation hyperbolic tangent neural network operators. This approximation is derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its high order derivative. Our operators are defined by using a density function induced by the hyperbolic tangent function. The approximations are pointwise and with respect to the uniform norm. The related feed-forward neural network is with one hidden layer.     

The shape preserving ang high accuracy approximation with multiquadric

This paper discusses the sufficient conditions for the shape preserving quasi-interpolation with multiquadric. Some quasi-interpolation schema is given such that the interpolation as well as its high derivatives is convergent. Supported by the National Natural Science Foundation of China.     

Progressive scattered data filtering

Given a finite point set $\text{Z⊂}\text{R}{}^{\mathrm{d}}$, the covering radius of a nonempty subset X⊂Z is the minimum distance r_X,Z such that every point in Z is at a distance of at most r_X,Z from some point in X. This paper concerns the construction of a sequence of subsets of decreasing sizes, such that their covering radii are small. To this end, a method for progressive data reduction, referred to as scattered data filtering, is proposed. The resulting scheme is a composition of greedy thinning, a recursive point removal strategy, and exchange, a postprocessing local optimization procedure. The paper proves adaptive a priori lower bounds on the minimal covering radii, which allows us to control for any current subset the deviation of its covering radius from the optimal value at run time. Important computational aspects of greedy thinning and exchange are discussed. The good performance of the proposed filtering scheme is finally shown by numerical examples.     

Approximating scattered data with discontinuities

A method is presented for approximating scattered data by a function defined on a regular two-dimensional grid. It is required that the approximation is discontinuous across given curves in the parameter domain known as faults. The method has three phases: regularisation, local approximation and extrapolation. The main emphasis is put on the extrapolation which is based on a matrix equation which minimises second order differences. By approximating each fault by a set of line segments parallel with one of the axes, it is simple to introduce natural boundary conditions across the faults. The resulting approximation has, as expected, discontinuities across faults and is smooth elsewhere. The method is stable even for large data sets.This research was supported by the Royal Norwegian Council for Scientific and Industrial Research.     

Approximate approximations from scattered data

The aim of this paper is to extend the approximate quasi-interpolation on a uniform grid by dilated shifts of a smooth and rapidly decaying function to scattered data quasi-interpolation. It is shown that high order approximation of smooth functions up to some prescribed accuracy is possible, if the basis functions, which are centered at the scattered nodes, are multiplied by suitable polynomials such that their sum is an approximate partition of unity. For Gaussian functions we propose a method to construct the approximate partition of unity and describe an application of the new quasi-interpolation approach to the cubature of multi-dimensional integral operators.     

Adaptive thinning for bivariate scattered data

This paper studies adaptive thinning strategies for approximating a large set of scattered data by piecewise linear functions over triangulated subsets. Our strategies depend on both the locations of the data points in the plane, and the values of the sampled function at these points—adaptive thinning. All our thinning strategies remove data points one by one, so as to minimize an estimate of the error that results by the removal of a point from the current set of points (this estimate is termed “anticipated error”). The thinning process generates subsets of “most significant” points, such that the piecewise linear interpolants over the Delaunay triangulations of these subsets approximate progressively the function values sampled at the original scattered points, and such that the approximation errors are small relative to the number of points in the subsets. We design various methods for computing the anticipated error at reasonable cost, and compare and test the performance of the methods. It is proved that for data sampled from a convex function, with the strategy of convex triangulation, the actual error is minimized by minimizing the best performing measure of anticipated error. It is also shown that for data sampled from certain quadratic polynomials, adaptive thinning is equivalent to thinning which depends only on the locations of the data points—nonadaptive thinning. Based on our numerical tests and comparisons, two practical adaptive thinning algorithms are proposed for thinning large data sets, one which is more accurate and another which is faster.     

Thinning algorithms for scattered data interpolation

Multistep interpolation of scattered data by compactly supported radial basis functions requires hierarchical subsets of the data. This paper analyzes thinning algorithms for generating evenly distributed subsets of scattered data in a given domain in ℝ ^d .     

Numerical differentiation for two-dimensional scattered data

In this paper, we propose a regularization method for numerical differentiation of two-dimensional mildly scattered input data. A regularized solution is constructed based on the Green's function. The existence and uniqueness of the regularized solution are proved and the convergence estimates are provided under a simple choice of regularization parameter. Numerical results show that our method is quite effective. One of the advantages for our proposed method is that the basis functions are independent of input data.     

Piecewise linear interpolants to Lagrange and Hermite convex scattered data

This paper concerns two fundamental interpolants to convex bivariate scattered data. The first,u, is the supremum over all convex Lagrange interpolants and is piecewise linear on a triangulation. The other,l, is the infimum over all convex Hermite interpolants and is piecewise linear on a tessellation. We discuss the existence, uniqueness, and numerical computation ofu andl and the associated triangulation and tessellation. We also describe how to generate convex Hermite data from convex Lagrange data.Research partially supported by the EU Project FAIRSHAPE, CHRX-CT94-0522. The first author was also partially supported by DGICYT PB93-0310 Research Grant.     

Chebyshev approximation to data by geometric elements

An algorithm is presented and proved correct, for the efficient approximation of finite point sets in ² and ³ by geometric elements such as circles, spheres and cylinders. It is shown that the approximation criterion used, viz. minimising the maximum orthogonal deviation, is best modelled mathematically through the concept of aparallel body. This notion, besides being a valuable tool for form assessment in metrology, contributes to approximation theory by introducing a new kind of approximation, here called geometric or orthogonal. This approach is closely related to but different from Chebyshev approximation.The work described is part of a Commission of the European Communities project (Contract 3327/1/0/158/89/9-BCR-UK(30)).     

A unified approach to scattered data approximation on \mathbb{S}^{\bf 3} and SO(3)

In this paper we use the connection between the rotation group SO(3) and the three-dimensional Euclidean sphere $\mathbb{S}^{3}$ in order to carry over results on the sphere $\mathbb{S}^{3}$ directly to the rotation group SO(3) and vice versa. More precisely, these results connect properties of sampling sets and quadrature formulae on SO(3) and $\mathbb{S}^{3}$ respectively. Furthermore we relate Marcinkiewicz–Zygmund inequalities and conditions for the existence of positive quadrature formulae on the rotation group SO(3) to those on the sphere $\mathbb{S}^{3}$ , respectively.