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首页> 外文期刊>Linear Algebra and its Applications >On inexact Hermitian and skew-Hermitian splitting methods for non-Hermitian positive definite linear systems
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On inexact Hermitian and skew-Hermitian splitting methods for non-Hermitian positive definite linear systems

机译:关于非Hermitian正定线性系统的不精确Hermitian和Skew-Hermitian分裂方法

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摘要

We study theoretical properties of two inexact Hermitian/skew-Hermitian splitting (IHSS) iteration methods for the large sparse non-Hermitian positive definite system of linear equations. In the inner iteration processes, we employ the conjugate gradient (CG) method to solve the linear systems associated with the Hermitian part, and the Lanczos or conjugate gradient for normal equations (CGNE) method to solve the linear systems associated with the skew-Hermitian part, respectively, resulting in IHSS(CG, Lanczos) and IHSS(CG, CGNE) iteration methods, correspondingly. Theoretical analyses show that both IHSS(CG, Lanczos) and IHSS(CG, CGNE) converge unconditionally to the exact solution of the non-Hermitian positive definite linear system. Moreover, their contraction factors and asymptotic convergence rates are dominantly dependent on the spectrum of the Hermitian part, but are less dependent on the spectrum of the skew-Hermitian part, and are independent of the eigenvectors of the matrices involved. Optimal choices of the inner iteration steps in the IHSS(CG, Lanczos) and IHSS(CG, CGNE) iterations are discussed in detail by considering both global convergence speed and overall computation workload, and computational efficiencies of both inexact iterations are analyzed and compared deliberately. (c) 2007 Elsevier Inc. All rights reserved.
机译:我们研究线性稀疏非埃尔米特正定系统的两种不精确的埃尔米特/斜埃尔米特分裂(IHSS)迭代方法的理论特性。在内部迭代过程中,我们采用共轭梯度(CG)方法来求解与Hermitian零件相关的线性系统,并使用Lanczos或共轭梯度法则方程(CGNE)方法来求解与偏斜的Hermitian相关的线性系统分别产生IHSS(CG,Lanczos)和IHSS(CG,CGNE)迭代方法。理论分析表明,IHSS(CG,Lanczos)和IHSS(CG,CGNE)都无条件收敛于非Hermitian正定线性系统的精确解。此外,它们的收缩因子和渐近收敛率主要取决于Hermitian部分的谱,而较少依赖于Skew-Hermitian部分的谱,并且与所涉及矩阵的特征向量无关。考虑全局收敛速度和整体计算量,详细讨论了IHSS(CG,Lanczos)和IHSS(CG,CGNE)迭代中内部迭代步骤的最佳选择,并仔细分析和比较了两个不精确迭代的计算效率。 (c)2007 Elsevier Inc.保留所有权利。

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