Large scale least squares scattered data fitting Original Research Article

The least squares approximation by tensor products of B-splines of large sets of scattered data is considered. This ill-conditioned or even singular problem requires special techniques, some of which are described in this paper. The performance of a Block Truncated Singular Value Decomposition (BTSVD) algorithm and two Lanczos based algorithms for sparse LSQ is compared. Several regularization methods are discussed.