Year: 2002
Author: Zhong-Zhi Bai, Shao-Liang Zhang
Journal of Computational Mathematics, Vol. 20 (2002), Iss. 4 : pp. 437–448
Abstract
A class of regularized conjugate gradient methods is presented for solving the large sparse system of linear equations of which the coefficient matrix is an ill-conditioned symmetric positive definite matrix. The convergence properties of these methods are discussed in depth, and the best possible choices of the parameters invoved in the new methods are investigated in detail. Numerical computations show that the new methods are more efficient and robust than both classical relaxation methods and classical conjugate direction methods.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2002-JCM-8930
Journal of Computational Mathematics, Vol. 20 (2002), Iss. 4 : pp. 437–448
Published online: 2002-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 12
Keywords: Conjugate gradient method Symmetric positive definite matrix Regularization Ill-conditioned linear system.