Hermite Scattered Data Fitting by the Penalized Least Squares Method
نویسندگان
چکیده
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. Mathematics subject classification: 41A15, 65M60, 65N30.
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