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Len Bos

Bio: Len Bos is an academic researcher from University of Verona. The author has contributed to research in topics: Interpolation & Polynomial. The author has an hindex of 19, co-authored 86 publications receiving 1176 citations. Previous affiliations of Len Bos include University of Calgary & University of Toronto.


Papers
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Journal ArticleDOI
TL;DR: To the best of the knowledge, this is the first complete, explicit example of near optimal bivariate interpolation points.

129 citations

Journal ArticleDOI
TL;DR: Theorems of Jackson type are given, for the simultaneous approximation of a function of class Cm and its partial derivatives, by a polynomial and the corresponding partial derivatives as mentioned in this paper.
Abstract: Theorems of Jackson type are given, for the simultaneous approximation of a function of class Cm and its partial derivatives, by a polynomial and the corresponding partial derivatives.

89 citations

Journal ArticleDOI
TL;DR: Using the concept of Geometric Weakly Admissible Meshes (see §2 below) together with an algorithm based on the classical QR factorization of matrices, the authors compute efficient points for discrete multivariate least squares approximation and Lagrange interpolation.
Abstract: Using the concept of Geometric Weakly Admissible Meshes (see §2 below) together with an algorithm based on the classical QR factorization of matrices, we compute efficient points for discrete multivariate least squares approximation and Lagrange interpolation.

82 citations

Journal ArticleDOI
Len Bos1
TL;DR: In this paper, the authors describe how points may be placed on collections of algebraic varieties so that the resulting system is unisolvent for polynomial interpolation, and give formulas for the corresponding Vandermonde determinants.

67 citations


Cited by
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Book
02 Jan 1991

1,377 citations

Journal ArticleDOI
TL;DR: In this article, the authors consider the problem of finding the best approximation operator for a given function, and the uniqueness of best approximations and the existence of best approximation operators.
Abstract: Preface 1. The approximation problem and existence of best approximations 2. The uniqueness of best approximations 3. Approximation operators and some approximating functions 4. Polynomial interpolation 5. Divided differences 6. The uniform convergence of polynomial approximations 7. The theory of minimax approximation 8. The exchange algorithm 9. The convergence of the exchange algorithm 10. Rational approximation by the exchange algorithm 11. Least squares approximation 12. Properties of orthogonal polynomials 13. Approximation of periodic functions 14. The theory of best L1 approximation 15. An example of L1 approximation and the discrete case 16. The order of convergence of polynomial approximations 17. The uniform boundedness theorem 18. Interpolation by piecewise polynomials 19. B-splines 20. Convergence properties of spline approximations 21. Knot positions and the calculation of spline approximations 22. The Peano kernel theorem 23. Natural and perfect splines 24. Optimal interpolation Appendices Index.

841 citations

Journal ArticleDOI
TL;DR: This is a survey of the main results on multivariate polynomial interpolation in the last twenty-five years, a period of time when the subject experienced its most rapid development.
Abstract: This is a survey of the main results on multivariate polynomial interpolation in the last twenty-five years, a period of time when the subject experienced its most rapid development. The problem is considered from two different points of view: the construction of data points which allow unique interpolation for given interpolation spaces as well as the converse. In addition, one section is devoted to error formulas and another to connections with computer algebra. An extensive list of references is also included.

783 citations

Journal ArticleDOI
David Levin1
TL;DR: The interpolation approximation in R d is shown to be a C∞ function, and an approximation order result is proven for quasi-uniform sets of data points.
Abstract: A general method for near-best approximations to functionals on R d , using scattered-data information is discussed. The method is actually the moving least-squares method, presented by the Backus-Gilbert approach. It is shown that the method works very well for interpolation, smoothing and derivatives' approximations. For the interpolation problem this approach gives Mclain's method. The method is near-best in the sense that the local error is bounded in terms of the error of a local best polynomial approximation. The interpolation approximation in R d is shown to be a C∞ function, and an approximation order result is proven for quasi-uniform sets of data points.

778 citations

01 Jan 2007
TL;DR: This work considers the numerical calculation of several matrix eigenvalue problems which require some manipulation before the standard algorithms may be used, and studies several eigen value problems which arise in least squares.
Abstract: We consider the numerical calculation of several matrix eigenvalue problems which require some manipulation before the standard algorithms may be used. This includes finding the stationary values of a quadratic form subject to linear constraints and determining the eigenvalues of a matrix which is modified by a matrix of rank one. We also consider several inverse eigenvalue problems. This includes the problem of determining the coefficients for the Gauss–Radau and Gauss–Lobatto quadrature rules. In addition, we study several eigenvalue problems which arise in least squares.

435 citations