Fast generalized cross validation using Krylov subspace methods |
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Authors: | Roger B Sidje Alan B Williams Kevin Burrage |
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Institution: | (1) Advanced Computational Modelling Centre, Department of Mathematics, The University of Queensland, Brisbane, QLD, 4072, Australia;(2) Sandia National Labs., Albuquerque, NM, 87185, New Mexico |
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Abstract: | 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(n
3) computationally, and memory requirements are O(n
2). 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.
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Keywords: | Lanczos Linear systems Generalized cross validation |
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