Local polynomial reproduction is a key ingredient in providing error estimates for several approximation methods. To bound the Lebesgue constants is a hard task especially in a multivariate setting. We provide a result which allows us to bound the Lebesgue constants uniformly and independently of the space dimension by oversampling. We get explicit and small bounds for the Lebesgue constants. Moreover, we use these results to establish error estimates for the moving least squares approximation scheme, also with special emphasis on the involved constants. We discuss the numerical treatment of the method and analyse its effort. Finally, we give large scale examples
We propose a fast and accurate approximation method for large sets of multivariate data using radia...
The paper deals with de la Vallée Poussin type interpolation on the square at tensor product Chebysh...
In this note we point out that polynomial least squares approximations may be unstable in coefficien...
AbstractIt is a common procedure for scattered data approximation to use local polynomial fitting in...
We investigate the uniform approximation provided by least squares polynomials on the unit Euclidean...
An algorithm is presented to compute good point sets and weights for discrete least squares polynomi...
We introduce moving least squares approximation as an approximation scheme on the sphere. We prove e...
AbstractWe study uniform approximation of differentiable or analytic functions of one or several var...
Weighted least squares polynomial approximation uses random samples to determine projections of func...
Abstract. We describe two experiments recently conducted with the approximate moving least squares (...
To analyze the absolute condition number of multivariate polynomial interpolation on Lissajous–Cheby...
Abstract. We propose a fast and accurate approximation method for large sets of multivariate data us...
Given an arbitrary function in H(div), we show that the error attained by the global-best approximat...
In the paper the polynomial mean-square approximation method was applied, where the applied criterio...
This paper is concerned with Lagrange interpolation by total degree polynomials in moderate dimensio...
We propose a fast and accurate approximation method for large sets of multivariate data using radia...
The paper deals with de la Vallée Poussin type interpolation on the square at tensor product Chebysh...
In this note we point out that polynomial least squares approximations may be unstable in coefficien...
AbstractIt is a common procedure for scattered data approximation to use local polynomial fitting in...
We investigate the uniform approximation provided by least squares polynomials on the unit Euclidean...
An algorithm is presented to compute good point sets and weights for discrete least squares polynomi...
We introduce moving least squares approximation as an approximation scheme on the sphere. We prove e...
AbstractWe study uniform approximation of differentiable or analytic functions of one or several var...
Weighted least squares polynomial approximation uses random samples to determine projections of func...
Abstract. We describe two experiments recently conducted with the approximate moving least squares (...
To analyze the absolute condition number of multivariate polynomial interpolation on Lissajous–Cheby...
Abstract. We propose a fast and accurate approximation method for large sets of multivariate data us...
Given an arbitrary function in H(div), we show that the error attained by the global-best approximat...
In the paper the polynomial mean-square approximation method was applied, where the applied criterio...
This paper is concerned with Lagrange interpolation by total degree polynomials in moderate dimensio...
We propose a fast and accurate approximation method for large sets of multivariate data using radia...
The paper deals with de la Vallée Poussin type interpolation on the square at tensor product Chebysh...
In this note we point out that polynomial least squares approximations may be unstable in coefficien...