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Extra info for Algorithms for a partially regularized least squares problem
2] O. Axelsson. Iterative Solution Methods. Cambridge University Press, Cambridge, 1994.  Å. Björck. Numerical Methods for Least Squares Problems. SIAM, Philadelphia, 1996.  Å. Björck T. Elfving and Z. Strakos. Stability of conjugate gradient and Lanczos methods for linear least squares problems. SIAM Journal on Matrix Analysis and Applications, 19:720–736, 1998.  Å. Björck. The calculation of linear least squares problems. Acta Numerica, 13:1–53, 2004. A. Girard. A fast ’Monte-Carlo cross-validation’ procedure for large least squares problems with noisy data.
Journal of Research of the National Bureau of Standards, B49:409–436, 1952. H. Reinsch. Smoothing by spline functions. 10:177–183, 1967. Numerische Mathematik, 19 “lic” — 2007/4/23 — 11:29 — page 20 — #32  P. Stålnacke and A. Grimvall. Semiparametric approaches to flownormalisation and source apportionment of substances transport in rivers. Environmetrics, 12:233–250, 2001.
H. F. Van Loan. Matrix Computaions. , Johns Hopkins University Press, Baltimore, 1996. J. W. Silverman. Nonparametric regression and generalized linear models - a roughness penalty approach. Chapman & Hill, London, 1994. C. Hansen. Rank-Deficient and Discrete Ill-Posed Problems. Numerical Aspects of Linear Inversion. SIAM, Philadelphia, 1998. R. Hestenes and E. Stiefel. Methods of conjugate gradients for solving linear stystem. Journal of Research of the National Bureau of Standards, B49:409–436, 1952.