Sobolev Error Estimates and a Bernstein Inequality for Scattered Data Interpolation via Radial Basis Functions

Error estimates for scattered-data interpolation via radial basis functions (RBFs) for target functions in the associated reproducing kernel Hilbert space (RKHS) have been known for a long time. Recently, these estimates have been extended to apply to certain classes of target functions generating the data which are outside the associated RKHS. However, these classes of functions still were not "large" enough to be applicable to a number of practical situations. In this paper we obtain Sobolev-type error estimates on compact regions of Rⁿ when the RBFs have Fourier transforms that decay algebraically. In addition, we derive a Bernstein inequality for spaces of finite shifts of an RBF in terms of the minimal separation parameter.     

L p Error Estimates for Scattered Data Interpolation On Spheres

In this article, working with the sphere 𝕊 ^d embedded in the (d + 1)-dimensional Euclidean space ? ^d+1 as the underlying manifold, we obtain an error estimate for interpolating functions f ∈ H _μ from shifts of a smooth positive definite function defined on 𝕊 ^d , where H _μ is a Sobolev space. We also get an L ^p error estimate for f by using a method of Duchon framework.     

Refined Error Estimates for Radial Basis Function Interpolation

We discuss new and refined error estimates for radial-function scattered-data interpolants and their derivatives. These estimates hold on R ^d , the d-torus, and the 2-sphere. We employ a new technique, involving norming sets, that enables us to obtain error estimates, which in many cases give bounds orders of magnitude smaller than those previously known.     

球面上径向基函数最小范数插值逼近的误差估计

研究了球面径向基插值对球面函数的逼近问题,给出了一致逼近的上界估计式．文中结果说明,球面径向基插值的逼近阶会随函数光滑性的提高而增加．     

Spherical Scattered Data Quasi-interpolation by Gaussian Radial Basis Function

Since the spherical Gaussian radial function is strictly positive definite, the authors use the linear combinations of translations of the Gaussian kernel to interpolate the scattered data on spheres in this article. Seeing that target functions are usually outside the native spaces, and that one has to solve a large scaled system of linear equations to obtain combinatorial coefficients of interpolant functions, the authors first probe into some problems about interpolation with Gaussian radial functions. Then they construct quasiinterpolation operators by Gaussian radial function, and get the degrees of approximation. Moreover, they show the error relations between quasi-interpolation and interpolation when they have the same basis functions. Finally, the authors discuss the construction and approximation of the quasi-interpolant with a local support function.     

基于二元连分式的散乱数据递推插值格式

本文首先基于新的非张量积型偏逆差商递推算法,分别构造奇数与偶数个插值节点上的二元连分式散乱数据插值格式,进而得到被插函数与二元连分式间的恒等式.接着,利用连分式三项递推关系式,提出特征定理来研究插值连分式的分子分母次数.然后,数值算例表明新的递推格式可行有效,同时,通过比较二元Thiele型插值连分式的分子分母次数,发现新的二元插值连分式的分子分母次数较低,这主要归功于节省了冗余的插值节点. 最后,计算此有理函数插值所需要的四则运算次数少于计算径向基函数插值.     

Bivariate Polynomial Natural Spline Interpolation Algorithms with Local Basis for Scattered Data

Because of its importance in both theory and applications, multivariate splines have attracted special attention in many fields. Based on the theory of spline functions in Hilbert spaces, bivariate polynomial natural splines for interpolating, smoothing or generalized interpolating of scattered data over an arbitrary domain are constructed with one-sided functions. However, this method is not well suited for large scale numerical applications. In this paper, a new locally supported basis for the bivariate polynomial natural spline space is constructed. Some properties of this basis are also discussed. Methods to order scattered data are shown and algorithms for bivariate polynomial natural spline interpolating are constructed. The interpolating coefficient matrix is sparse, and thus, the algorithms can be easily implemented in a computer.     

单位球上散乱数据核正则化回归的误差分析

给出基于二次损失的单位球盖(单位球)上确定型散乱数据核正则化回归误差的上界估计,将学习误差估计转化为核函数积分的误差分析,借助于学习理论中的K-泛函与光滑模的等价性刻画了学习速度.研究结果表明学习速度由网格范数所控制.     

Preconditioned Iterative Methods for Scattered Data Interpolation

The scattered data interpolation problem in two space dimensions is formulated as a partial differential equation with interpolating side conditions. The system is discretized by the Morley finite element space. The focus of this paper is to study preconditioned iterative methods for the corresponding discrete systems. We introduce block diagonal preconditioners, where a multigrid operator is used for the differential equation part of the system, while we propose an operator constructed from thin plate radial basis functions for the equations corresponding to the interpolation conditions. The effect of the preconditioners are documented by numerical experiments.     

几种基于散乱数据拟合的局部插值方法

本文首先针对散乱数据拟合的Shepard方法,结合截断多项式、B样条基函数和指数函数来构造其权函数,使新的权函数具有更高的光滑度和更好的衰减性,并且其光滑性和衰减性可以根据实际需要自由调节,从而提高了曲面的拟合质量．同时还给出一种类似的局部插值方法．另外,本文还基于多重二次插值,结合多元样条的思想,给出了两个局部插值算法．该算法较好地继承了多重二次插值曲面的性质,从而保证了拟合曲面具有好地光顺性和拟合精度．曲面整体也具有较高的光滑性．     

Direct and Inverse Estimates for Bernstein Polynomials

Direct estimates for the Bernstein operator are presented by the Ditzian—Totik modulus of smoothness , whereby the step-weight φ is a function such that φ ² is concave. The inverse direction will be established for those step-weights φ for which φ ² and , are concave functions. This combines the classical estimate (φ=1 ) and the estimate developed by Ditzian and Totik ( ). In particular, the cases , λ∈[0,1] , are included. August 2, 1996. Date revised: March 28, 1997.     

Surface Reconstruction of 3D Scattered Data with Radial Basis Functions

We use Radial Basis Functions （RBFs） to reconstruct smooth surfaces from 3D scattered data. An object＇s surface is defined implicitly as the zero set of an RBF fitted to the given surface data. We propose improvements on the methods of surface reconstruction with radial basis functions. A sparse approximation set of scattered data is constructed by reducing the number of interpolating points on the surface. We present an adaptive method for finding the off-surface normal points. The order of the equation decreases greatly as the number of the off-surface constraints reduces gradually. Experimental results are provided to illustrate that the proposed method is robust and may draw beautiful graphics.     

二元分形插值的拟合误差估计

A given bivariate continuous function is fitted by using a bivariate fractal interpolation function, and the error of fitting is studied in this paper. The results of error estimates are obtained in two metric cases. This provides a theoretical basis for the algorithms of fractal surface reconstruction.     

Error Estimates for Mixed Finite Element Methods for Sobolev Equation

1 IntroductionLet fl be a bounded domain in R2 with Lipschitz continuous boundaxy 0fl. For thed0 < T < co, we consider the fo1lowing initial-boun'lar}-ralue problem for thc Sobolevequation:where ut denotes the time derivative of the function (1. Vu denotes the gradient of thefunction u, and divv denotes the divergence of the vect{Jr tulued function v, a1 b1, f, anduo are known functions.The standard finite element method for (1.1) (1.3) llas received considerable attentionand is well studied…     

L 1 Spline Methods for Scattered Data Interpolation and Approximation

We generalize the L ₁ spline methods proposed in [4, 5] for scattered data interpolation and fitting using bivariate spline spaces of any degree d and any smoothness r (of course, r<d) over any triangulation. Some numerical experiments are presented to illustrate the better performance of the L ₁ spline methods as compared to the minimal energy method. We include some extensions for dealing with other surface design problems.     

散乱数据曲面拟合的局部加权最小二乘插值方法及权函数的选择讨论

本文首先用局部加权最小二乘法将三维空间内任意散乱数据点集均匀,再估计出立方体网格点上的偏导数值及混合偏导数值,最后仅用网格点数据进行快速光滑插值加密计算,从而可得到任意点处的函数值。通过对已知函数的随机数据点集进行计算,取得了令人满意的效果。同时,在最小二乘逼近过程中,本文提供了一种权函数,并与其它二种权函数进行分析比较,给出了各种情况下的误差。     

Multivariate Interpolation by Polynomials and Radial Basis Functions

In many cases, multivariate interpolation by smooth radial basis functions converges toward polynomial interpolants, when the basis functions are scaled to become flat. In particular, examples show and this paper proves that interpolation by scaled Gaussians converges toward the de Boor/Ron least polynomial interpolant. To arrive at this result, a few new tools are necessary. The link between radial basis functions and multivariate polynomials is provided by radial polynomials ||x-y||₂^2l\|x-y\|_2^{2\ell} that already occur in the seminal paper by C.A. Micchelli of 1986. We study the polynomial spaces spanned by linear combinations of shifts of radial polynomials and introduce the notion of a discrete moment basis to define a new well-posed multivariate polynomial interpolation process which is of minimal degree and also least and degree-reducing in the sense of de Boor and Ron. With these tools at hand, we generalize the de Boor/Ron interpolation process and show that it occurs as the limit of interpolation by Gaussian radial basis functions. As a byproduct, we get a stable method for preconditioning the matrices arising with interpolation by smooth radial basis functions.     

Trivariate Polynomial Natural Spline for 3D Scattered Data Hermit Interpolation

Consider a kind of Hermit interpolation for scattered data of 3D by trivariate polynomial natural spline,such that the objective energy functional (with natural boundary conditions) is minimal.By the s...     

Hermite型多元样本定理及Sobolev类上混淆误差的估计

本文证明了Hermite型多元样本定理,并由此确定了Sobolev类上混淆误差阶的精确估计.