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Volume 2, Issue 0
Preconditioned Hybrid Conjugate Gradient Algorithm for P-Laplacian

G. Zhou, Y. Huang & C. Feng

Int. J. Numer. Anal. Mod., 2 (2005), pp. 123-130.

Published online: 2005-11

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  • Abstract

In this paper, a hybrid conjugate gradient algorithm with weighted preconditioner is proposed. The algorithm can efficiently solve the minimizing problem of general function deriving from finite element discretization of the p-Laplacian. The algorithm is efficient, and its convergence rate is mesh-independent. Numerical experiments show that the hybrid conjugate gradient direction of the algorithm is superior to the steepest descent one when $p$ is large.

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@Article{IJNAM-2-123, author = {Zhou , G.Huang , Y. and Feng , C.}, title = {Preconditioned Hybrid Conjugate Gradient Algorithm for P-Laplacian}, journal = {International Journal of Numerical Analysis and Modeling}, year = {2005}, volume = {2}, number = {0}, pages = {123--130}, abstract = {

In this paper, a hybrid conjugate gradient algorithm with weighted preconditioner is proposed. The algorithm can efficiently solve the minimizing problem of general function deriving from finite element discretization of the p-Laplacian. The algorithm is efficient, and its convergence rate is mesh-independent. Numerical experiments show that the hybrid conjugate gradient direction of the algorithm is superior to the steepest descent one when $p$ is large.

}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/952.html} }
TY - JOUR T1 - Preconditioned Hybrid Conjugate Gradient Algorithm for P-Laplacian AU - Zhou , G. AU - Huang , Y. AU - Feng , C. JO - International Journal of Numerical Analysis and Modeling VL - 0 SP - 123 EP - 130 PY - 2005 DA - 2005/11 SN - 2 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/ijnam/952.html KW - AB -

In this paper, a hybrid conjugate gradient algorithm with weighted preconditioner is proposed. The algorithm can efficiently solve the minimizing problem of general function deriving from finite element discretization of the p-Laplacian. The algorithm is efficient, and its convergence rate is mesh-independent. Numerical experiments show that the hybrid conjugate gradient direction of the algorithm is superior to the steepest descent one when $p$ is large.

Zhou , G.Huang , Y. and Feng , C.. (2005). Preconditioned Hybrid Conjugate Gradient Algorithm for P-Laplacian. International Journal of Numerical Analysis and Modeling. 2 (0). 123-130. doi:
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