Preconditioned conjugate gradient method for generalized least squares problems
type: Item
instance of: scholarly article
title: Preconditioned conjugate gradient method for generalized least squares problems
zbMATH Open document ID: 0861.65036
DOI: 10.1016/0377-0427(95)00239-1
author: J. Y. Yuan
author: Alfredo Iusem
published in: Journal of Computational and Applied Mathematics
publication date: 1997-04-29T00:00:00Z
review text: \textit{C. C. Paige} [SIAM J. Numer. Anal. 16, 165-171 (1979; Zbl 0402.65006); Math. Comput. 33, 171-183 (1979; Zbl 0405.65018)] transformed the generalized least squares problem into an equality-constrained least squares problem and proposed a direct method to solve it. However, as Paige's method is based on some matrix decompositions, for large sparse problems it is not appropriate. This paper presents a preconditioned conjugate gradient method. Theoretical comparison results show that the new method is better than SOR-type methods. By the way, the new method needs only a preconditioner, but not the inverse of the weight matrix or of any of its submatrices.
Mathematics Subject Classification ID: 65F20
Mathematics Subject Classification ID: 65F35
zbMATH DE Number: 910317
zbMATH Keywords: generalized least squares problem
zbMATH Keywords: preconditioned conjugate gradient method
reviewed by: Liu Xinguo
MaRDI profile type: Publication
full work available at URL: 0377-0427(95)00239-1
OpenAlex ID: W2057816826
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description: scientific article; zbMATH DE number 910317