Fast Compact Algorithms and Software for Spline Smoothing

Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.

All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. Some chapters also include function examples of the Curve Fitting Toolbox.