Year: 2009
Journal of Computational Mathematics, Vol. 27 (2009), Iss. 6 : pp. 802–811
Abstract
Given a set of scattered data with derivative values. If the data is noisy or there is an extremely large number of data, we use an extension of the penalized least squares method of von Golitschek and Schumaker [Serdica, 18 (2002), pp.1001-1020] to fit the data. We show that the extension of the penalized least squares method produces a unique spline to fit the data. Also we give the error bound for the extension method. Some numerical examples are presented to demonstrate the effectiveness of the proposed method.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208//jcm.2009.09-m2540
Journal of Computational Mathematics, Vol. 27 (2009), Iss. 6 : pp. 802–811
Published online: 2009-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 10
Keywords: Bivariate splines Scattered data fitting Extension of penalized least squares method.