article

Efficient Implementation of a Class of Preconditioned Conjugate Gradient Methods

Author:

Stanley C. EisenstatAuthors Info & Claims

SIAM Journal on Scientific and Statistical Computing, Volume 2, Issue 1

Pages 1 - 4

https://doi.org/10.1137/0902001

Published: 01 March 1981 Publication History

Abstract

The preconditioned conjugate gradient (PCG) method is an effective means for solving systems of linear equations where the coefficient matrix is symmetric and positive definite. The incomplete $LDL^t $ factorizations are a widely used class of preconditionings, including the SSOR, Dupont-Kendall-Rachford, generalized SSOR, ICCG(0), and MICCG(0) preconditionings. The efficient implementation of PCG with a preconditioning from this class is discussed.

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Cited By

Kronbichler MSashko DMunch P(2023)Enhancing data locality of the conjugate gradient method for high-order matrix-free finite-element implementationsInternational Journal of High Performance Computing Applications10.1177/1094342022110788037:2(61-81)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1177/10943420221107880
Li JZhang YZheng HWang KSolihin YHeinrich M(2023)FDMAX: An Elastic Accelerator Architecture for Solving Partial Differential EquationsProceedings of the 50th Annual International Symposium on Computer Architecture10.1145/3579371.3589083(1-12)Online publication date: 17-Jun-2023
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cover image SIAM Journal on Scientific and Statistical Computing

SIAM Journal on Scientific and Statistical Computing Volume 2, Issue 1

1981

120 pages

ISSN:0196-5204

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Society for Industrial and Applied Mathematics

United States

Publication History

Published: 01 March 1981

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Cited By

Kronbichler MSashko DMunch P(2023)Enhancing data locality of the conjugate gradient method for high-order matrix-free finite-element implementationsInternational Journal of High Performance Computing Applications10.1177/1094342022110788037:2(61-81)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1177/10943420221107880
Li JZhang YZheng HWang KSolihin YHeinrich M(2023)FDMAX: An Elastic Accelerator Architecture for Solving Partial Differential EquationsProceedings of the 50th Annual International Symposium on Computer Architecture10.1145/3579371.3589083(1-12)Online publication date: 17-Jun-2023
https://dl.acm.org/doi/10.1145/3579371.3589083
Ren JLiang WYan RMai LLiu SLiu X(2022)MegBA: A GPU-Based Distributed Library for Large-Scale Bundle AdjustmentComputer Vision – ECCV 202210.1007/978-3-031-19836-6_40(715-731)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-19836-6_40
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