Abstract
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.
Similar content being viewed by others
References
P. Bloomfield and W.L. Steiger, Least Absolute Deviations: Theory, Applications, and Algorithms (Birkhäuser, Boston, 1983).
G. Farin, Triangular Bernstein-Bézier patches, Comput. Aided Geom. Design 3 (1986) 83–127.
G. Fasshauer and L.L. Schumaker, Multi-patch parametric surfaces with minimal energy, Comput. Aided Geom. Design 13 (1996) 45–79.
J.E. Lavery, Shape-preserving, multiscale fitting of univariate data by cubic L 1 smoothing splines, Comput. Aided Geom. Design 17 (2000) 715–727.
J.E. Lavery, Shape-preserving, multiscale interpolation by bi-and multivariate cubic L 1 splines, Comput. Aided Geom. Design 18 (2001) 321–343.
M. Meketon, Least absolute value regression, Unpublished manuscript.
R.J. Vanderbei, M.J. Meketon and B.A. Freedman, A modification of Karmarkar's linear programming algorithm, Algorithmica 1 (1986) 395–407.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Lai, MJ., Wenston, P. L 1 Spline Methods for Scattered Data Interpolation and Approximation. Advances in Computational Mathematics 21, 293–315 (2004). https://doi.org/10.1023/B:ACOM.0000032042.35918.c2
Issue Date:
DOI: https://doi.org/10.1023/B:ACOM.0000032042.35918.c2