移位LOPBiCG:基于Bi‐CGSTAB的求解非对称移位线性系统的局部正交积型方法

IF 1.8 3区 数学 Q1 MATHEMATICS Numerical Linear Algebra with Applications Pub Date : 2023-10-30 DOI:10.1002/nla.2538
Ren‐Jie Zhao, Tomohiro Sogabe, Tomoya Kemmochi, Shao‐Liang Zhang
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引用次数: 0

摘要

在用移位Krylov子空间方法求解移位线性系统时,必须选择种子系统,而一个不合适的种子可能导致许多移位系统无法求解。为了避免这个问题,我们提出了一种种子切换技术,可以在不损失构造的Krylov子空间维数的情况下,将种子系统切换到另一个线性系统作为一个新的种子系统。然而,当将Krylov子空间方法应用于种子和移位系统时,该技术需要共线残差向量。由于乘积型移位的Krylov子空间方法不能提供这样的共线性,这些方法不能使用这种技术。在本文中,我们提出了一种具有残差共线性的位移BiCGstab方法的变体,并将种子交换技术应用于该方法。一些数值实验表明,该方法避开了初始种子系统的选择问题。
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Shifted LOPBiCG: A locally orthogonal product‐type method for solving nonsymmetric shifted linear systems based on Bi‐CGSTAB
Abstract When solving shifted linear systems using shifted Krylov subspace methods, selecting a seed system is necessary, and an unsuitable seed may result in many shifted systems being unsolved. To avoid this problem, a seed‐switching technique has been proposed to help switch the seed system to another linear system as a new seed system without losing the dimension of the constructed Krylov subspace. Nevertheless, this technique requires collinear residual vectors when applying Krylov subspace methods to the seed and shifted systems. Since the product‐type shifted Krylov subspace methods cannot provide such collinearity, these methods cannot use this technique. In this article, we propose a variant of the shifted BiCGstab method, which possesses the collinearity of residuals, and apply the seed‐switching technique to it. Some numerical experiments show that the problem of choosing the initial seed system is circumvented.
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来源期刊
CiteScore
3.40
自引率
2.30%
发文量
50
审稿时长
12 months
期刊介绍: Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review. Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects. Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
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