TY - JOUR
T1 - Hermite Scattered Data Fitting by the Penalized Least Squares Method
AU - Tianhe Zhou & Danfu Han
JO - Journal of Computational Mathematics
VL - 6
SP - 802
EP - 811
PY - 2009
DA - 2009/12
SN - 27
DO - http://doi.org/10.4208//jcm.2009.09-m2540
UR - https://global-sci.org/intro/article_detail/jcm/8604.html
KW - Bivariate splines, Scattered data fitting, Extension of penalized least squares method.
AB - <p style="text-align: justify;">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. &nbsp;</p>