Incomplete LU Preconditioners for Conjugate-Gradient-Type Iterative Methods

1988 ◽  
Vol 3 (01) ◽  
pp. 302-306 ◽  
Author(s):  
Horst D. Simon
Keyword(s):  
Iterative Methods ◽  
Conjugate Gradient ◽  
Gradient Type

Iterative solution of linear systems

Acta Numerica ◽  
1992 ◽  
Vol 1 ◽  
pp. 57-100 ◽  
Author(s):  
Roland W. Freund ◽  
Gene H. Golub ◽  
Noël M. Nachtigal
Keyword(s):  
Linear Systems ◽  
Iterative Methods ◽  
Conjugate Gradient ◽  
Krylov Subspace ◽  
Iterative Solution ◽  
Krylov Subspace Methods ◽  
Subspace Methods ◽  
Recent Advances ◽  
Gradient Type ◽  
Large Linear Systems

Recent advances in the field of iterative methods for solving large linear systems are reviewed. The main focus is on developments in the area of conjugate gradient-type algorithms and Krylov subspace methods for nonHermitian matrices.

scholarly journals An efficient three-term conjugate gradient-type algorithm for monotone nonlinear equations

2020 ◽  
Author(s):  
Jamilu Sabi'u ◽  
Abdullah Shah
Keyword(s):  
Global Convergence ◽  
Nonlinear Equations ◽  
Conjugate Gradient ◽  
Large Scale ◽  
Numerical Results ◽  
Search Direction ◽  
Projection Technique ◽  
Type Algorithm ◽  
Gradient Type

In this article, we proposed two Conjugate Gradient (CG) parameters using the modified Dai-{L}iao condition and the descent three-term CG search direction. Both parameters are incorporated with the projection technique for solving large-scale monotone nonlinear equations. Using the Lipschitz and monotone assumptions, the global convergence of methods has been proved. Finally, numerical results are provided to illustrate the robustness of the proposed methods.

Extended conjugate gradient squared and conjugate residual squared methods for solving the generalized coupled Sylvester tensor equations

2020 ◽  
pp. 014233122093238
Author(s):  
Eisa Khosravi Dehdezi ◽  
Saeed Karimi
Keyword(s):  
Iterative Methods ◽  
Conjugate Gradient ◽  
Numerical Examples ◽  
New Methods ◽  
Conjugate Gradient Squared ◽  
And Performance ◽  
Tensor Computations ◽  
Conjugate Residual

In this paper, two attractive iterative methods – conjugate gradient squared (CGS) and conjugate residual squared (CRS) – are extended to solve the generalized coupled Sylvester tensor equations [Formula: see text]. The proposed methods use tensor computations with no maricizations involved. Also, some properties of the new methods are presented. Finally, several numerical examples are given to compare the efficiency and performance of the proposed methods with some existing algorithms.

Accelerating Vector Iteration Methods

1986 ◽  
Vol 53 (2) ◽  
pp. 291-297 ◽  
Author(s):  
M. Papadrakakis
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Iterative Methods ◽  
Conjugate Gradient ◽  
Test Problems ◽  
Convergence Properties ◽  
Dynamic Relaxation ◽  
Element Level ◽  
Iteration Methods ◽  
Partial Elimination

This paper describes a technique for accelerating the convergence properties of iterative methods for the solution of large sparse symmetric linear systems that arise from the application of finite element method. The technique is called partial preconditioning process (PPR) and can be combined with pure vector iteration methods, such as the conjugate gradient, the dynamic relaxation, and the Chebyshev semi-iterative methods. The proposed triangular splitting preconditioner combines Evans’ SSOR preconditioner with a drop-off tolerance criterion. The (PPR) is attractive in a FE framework because it is simple and can be implemented at the element level as opposed to incomplete Cholesky preconditioners, which require a sparse assembly. The method, despite its simplicity, is shown to be more efficient on a set of test problems for certain values of the drop-off tolerance parameter than the partial elimination method.

Sign in / Sign up

Export Citation Format

Share Document