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Τίτλος: Efficient algorithms for estimating the general linear model
Συγγραφείς: Yanev, Petko I. 
Kontoghiorghes, Erricos John 
Major Field of Science: Natural Sciences
Λέξεις-κλειδιά: Parallel algorithms;Computational complexity;Estimation;Linear systems;Mathematical models;Problem solving
Ημερομηνία Έκδοσης: Φεβ-2006
Πηγή: Parallel Computing, 2006, vol. 32, no. 2, pp. 195-204
Volume: 32
Issue: 2
Start page: 195
End page: 204
Περιοδικό: Parallel Computing 
Περίληψη: Computationally efficient serial and parallel algorithms for estimating the general linear model are proposed. The sequential block-recursive algorithm is an adaptation of a known Givens strategy that has as a main component the Generalized QR decomposition. The proposed algorithm is based on orthogonal transformations and exploits the triangular structure of the Cholesky QRD factor of the variance-covariance matrix. Specifically, it computes the estimator of the general linear model by solving recursively a series of smaller and smaller generalized linear least squares problems. The new algorithm is found to outperform significantly the corresponding LAPACK routine. A parallel version of the new sequential algorithm which utilizes an efficient distribution of the matrices over the processors and has low inter-processor communication is developed. The theoretical computational complexity of the parallel algorithms is derived and analyzed. Experimental results are presented which confirm the theoretical analysis. The parallel strategy is found to be scalable and highly efficient for estimating large-scale general linear estimation problems.
URI: https://hdl.handle.net/20.500.14279/2053
ISSN: 01678191
DOI: 10.1016/j.parco.2005.06.007
Rights: © Elsevier
Type: Article
Affiliation: University of Cyprus 
University of London 
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