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An Examination of New Paradigms for Spline Approximations
Christoph Witzgall1, David E Gilsinn1, Marjorie A McClain1
1National Institute of Standards and Technology, Gaithersburg, MD 20899-8910.
This study proposes relaxation-based algorithms for calculating Lavery splines in univariate and bivariate cases. The bivariate spline approach handles irregularly spaced data using Hseih-Clough-Tocher elements for improved approximation.
Area of Science:
- Numerical Analysis
- Computer-Aided Geometric Design (CAGD)
Background:
- Lavery splines offer a method for curve and surface approximation.
- Existing methods for bivariate splines may not optimally handle irregularly spaced data.
Purpose of the Study:
- To develop and analyze relaxation-based algorithms for approximate calculation of Lavery splines.
- To investigate the univariate case in detail to enhance understanding of the bivariate case.
- To propose a bivariate spline method suitable for irregularly spaced data.
Main Methods:
- Development of relaxation-based algorithms for univariate and bivariate Lavery splines.
- Application of Hseih-Clough-Tocher elements based on the triangulated irregular network (TIN) concept for bivariate splines.
- Assumption of a rotationally invariant functional for the bivariate case, following prior work.
Main Results:
- Proposed algorithms provide approximate calculations for Lavery splines.
- The bivariate spline method is designed to accommodate irregularly spaced data.
- Detailed analysis of the univariate case provides foundational insights.
Conclusions:
- The proposed relaxation-based algorithms are effective for approximating Lavery splines.
- The novel bivariate spline approach using TIN and Hseih-Clough-Tocher elements shows promise for irregularly spaced data.
- Further investigation into the univariate case supports the advancement of bivariate spline techniques.
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