• Journal of applied mathematics & informatics
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• v.28 no.5_6
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• pp.1409-1418
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• 2010

In this paper, we present a preconditioned iterative method for solving linear systems Ax = b, where A is a Z-matrix. We give some comparison theorems to show that the rate of convergence of the new preconditioned iterative method is faster than the rate of convergence of the previous preconditioned iterative method. Finally, we give one numerical example to show that our results are true.

• Honam Mathematical Journal
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• v.32 no.3
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• pp.399-412
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• 2010

In this paper, we consider a preconditioned accelerated overrelaxation (PAOR) method to solve systems of linear equations. We show the convergence of the PAOR method. We also give com-parison results when the coefficient matrix is an L- or H-matrix. Finally, we provide some numerical experiments to show efficiency of PAOR method.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.1319-1330
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• 2009

In [2] A.Hadjidimos proposed the generalized accelerated over-relaxation (GAOR) methods which generalize the basic iterative method for the solution of linear systems. In this paper we consider the convergence of the two parallel accelerated generalized AOR iterative methods and obtain some convergence theorems for the case when the coefficient matrix A is a block diagonally dominant matrix or a generalized block diagonally dominant matrix.

• Journal of applied mathematics & informatics
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• v.39 no.3_4
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• pp.587-600
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• 2021

Some problems in engineering and science can be equivalently transformed into solving multi-linear systems. In this paper, we propose two preconditioned AOR iteration methods to solve multi-linear systems with -tensor. Based on these methods, the general conditions of preconditioners are given. We give the convergence theorem and comparison theorem of the two methods. The results of numerical examples show that methods we propose are more effective.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1067-1074
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• 2011

In this paper, a preconditioned mixed-type splitting iterative method for solving the linear systems Ax = b is presented, where A is a Z-matrix. Then we also obtain some results to show that the rate of convergence of our method is faster than that of the preconditioned AOR (PAOR) iterative method and preconditioned SOR (PSOR) iterative method. Finally, we give one numerical example to illustrate our results.

• Journal of applied mathematics & informatics
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• v.32 no.3_4
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• pp.389-403
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• 2014

In this paper, we first provide comparison results of preconditioned AOR methods with direct preconditioners $I+{\beta}L$, $I+{\beta}U$ and $I+{\beta}(L+U)$ for solving a linear system whose coefficient matrix is a large sparse irreducible L-matrix, where ${\beta}$ > 0. Next we propose how to find a near optimal parameter ${\beta}$ for which Krylov subspace method with these direct preconditioners performs nearly best. Lastly numerical experiments are provided to compare the performance of preconditioned iterative methods and to illustrate the theoretical results.