Numerical Algorithms

, Volume 47, Issue 2, pp 109–131

Fast generalized cross validation using Krylov subspace methods

  • Roger B. Sidje
  • Alan B. Williams
  • Kevin Burrage
Original Paper

DOI: 10.1007/s11075-007-9150-y

Cite this article as:
Sidje, R.B., Williams, A.B. & Burrage, K. Numer Algor (2008) 47: 109. doi:10.1007/s11075-007-9150-y


The task of fitting smoothing spline surfaces to meteorological data such as temperature or rainfall observations is computationally intensive. The generalized cross validation (GCV) smoothing algorithm, if implemented using direct matrix techniques, is O(n3) computationally, and memory requirements are O(n2). Thus, for data sets larger than a few hundred observations, the algorithm is prohibitively slow. The core of the algorithm consists of solving series of shifted linear systems, and iterative techniques have been used to lower the computational complexity and facilitate implementation on a variety of supercomputer architectures. For large data sets though, the execution time is still quite high. In this paper we describe a Lanczos based approach that avoids explicitly solving the linear systems and dramatically reduces the amount of time required to fit surfaces to sets of data.


Lanczos Linear systems Generalized cross validation 

Mathematics Subject Classifications (2000)

65F10 65D10 

Copyright information

© Springer Science+Business Media, LLC. 2007

Authors and Affiliations

  • Roger B. Sidje
    • 1
  • Alan B. Williams
    • 2
  • Kevin Burrage
    • 1
  1. 1.Advanced Computational Modelling Centre, Department of MathematicsThe University of QueenslandBrisbaneAustralia
  2. 2.Sandia National Labs.AlbuquerqueNew Mexico

Personalised recommendations