Analysis of optimal bivariate symmetric refinable Hermite interpolants

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September 2007, 6(3): 689-718. doi: 10.3934/cpaa.2007.6.689

Analysis of optimal bivariate symmetric refinable Hermite interpolants

1.	Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2G1, Canada
2.	Department of Mathematics, Zhejiang University, Hangzhou 310027, China

Received May 2006 Revised March 2007 Published June 2007

Abstract
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Multivariate refinable Hermite interpolants with high smoothness and small support are of interest in CAGD and numerical algorithms. In this article, we are particularly interested in analyzing some univariate and bivariate symmetric refinable Hermite interpolants, which have some desirable properties such as short support, optimal smoothness and spline property. We shall study the projection method for multivariate refinable function vectors and discuss some properties of multivariate spline refinable function vectors. Here a compactly supported multivariate spline function on $\mathbb R^s$ just means a function of piecewise polynomials supporting on a finite number of polygonal partition subdomains of $\mathbb R^s$. We shall discuss spline refinable function vectors by investigating the structure of the eigenvalues and eigenvectors of the transition operator. To illustrate the results in this paper, we shall analyze the optimal smoothness and spline properties of some univariate and bivariate refinable Hermite interpolants. For the regular triangular mesh, we obtain a bivariate $C^2$ symmetric dyadic refinable Hermite interpolant of order $2$ whose mask is supported inside $[-1,1]^2$.

Keywords: Refinable Hermite interpolants, interpolatory Hermite masks, interpolatory Hermite subdivision schemes, smoothness, symmetry, projection method, subdivision triplets., multivariate splines.

Mathematics Subject Classification: Primary: 42C40, 41A05, 41A15, 41A6.

Citation: Bin Han, Qun Mo. Analysis of optimal bivariate symmetric refinable Hermite interpolants. Communications on Pure & Applied Analysis, 2007, 6 (3) : 689-718. doi: 10.3934/cpaa.2007.6.689

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