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Title: Some new preconditioned generalized AOR methods for solving weighted linear least squares problems

Abstract

In this paper, we propose some new preconditioned GAOR methods for solving weighted linear least squares problems and discuss their comparison results. Comparison results show that the convergence rates of the new preconditioned GAOR methods are better than those of the preconditioned GAOR methods presented by Shen et al. (Appl Math Mech Engl Ed 33(3):375–384, 2012) and Wang et al. (J Appl Math, doi: 10.1155/2012/563586 , 2012) whenever these methods are convergent. Finally, numerical experiments are provided to confirm the theoretical results obtained in this paper.

Authors:
; ; ;  [1]
  1. Northwestern Polytechnical University, Department of Applied Mathematics (China)
Publication Date:
OSTI Identifier:
22769381
Resource Type:
Journal Article
Journal Name:
Computational and Applied Mathematics
Additional Journal Information:
Journal Volume: 37; Journal Issue: 1; Other Information: Copyright (c) 2018 SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0101-8205
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; COMPARATIVE EVALUATIONS; CONVERGENCE; LEAST SQUARE FIT

Citation Formats

Huang, Zheng-Ge, Wang, Li-Gong, Xu, Zhong, and Cui, Jing-Jing. Some new preconditioned generalized AOR methods for solving weighted linear least squares problems. United States: N. p., 2018. Web. doi:10.1007/S40314-016-0350-8.
Huang, Zheng-Ge, Wang, Li-Gong, Xu, Zhong, & Cui, Jing-Jing. Some new preconditioned generalized AOR methods for solving weighted linear least squares problems. United States. doi:10.1007/S40314-016-0350-8.
Huang, Zheng-Ge, Wang, Li-Gong, Xu, Zhong, and Cui, Jing-Jing. Thu . "Some new preconditioned generalized AOR methods for solving weighted linear least squares problems". United States. doi:10.1007/S40314-016-0350-8.
@article{osti_22769381,
title = {Some new preconditioned generalized AOR methods for solving weighted linear least squares problems},
author = {Huang, Zheng-Ge and Wang, Li-Gong and Xu, Zhong and Cui, Jing-Jing},
abstractNote = {In this paper, we propose some new preconditioned GAOR methods for solving weighted linear least squares problems and discuss their comparison results. Comparison results show that the convergence rates of the new preconditioned GAOR methods are better than those of the preconditioned GAOR methods presented by Shen et al. (Appl Math Mech Engl Ed 33(3):375–384, 2012) and Wang et al. (J Appl Math, doi: 10.1155/2012/563586 , 2012) whenever these methods are convergent. Finally, numerical experiments are provided to confirm the theoretical results obtained in this paper.},
doi = {10.1007/S40314-016-0350-8},
journal = {Computational and Applied Mathematics},
issn = {0101-8205},
number = 1,
volume = 37,
place = {United States},
year = {2018},
month = {3}
}