用平移不变张量环近似法解决一类无限维张量特征值问题

IF 1.8 3区 数学 Q1 MATHEMATICS
Roel Van Beeumen, Lana Periša, Daniel Kressner, Chao Yang
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引用次数: 0

摘要

我们研究了一种求解无穷维张量特征值问题的方法,其中无穷维对称矩阵呈现平移不变结构。我们从数值线性代数的角度对这类问题进行了表述,并描述了如何使用幂方法获得所需的特征向量近似值。这个无穷维特征向量由一个平移不变的无限张量环(iTR)以紧凑的方式表示。低阶近似用于保持后续幂迭代的成本约束,同时保留近似特征向量的 iTR 结构。我们展示了如何高效计算 iTR 特征向量近似的平均瑞利商,并引入了投影残差来监测其收敛性。在数值示例中,我们说明了这种投影 iTR 残差的准则也可用于自动修改时间步长,以确保幂方法准确、快速地收敛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving a class of infinite‐dimensional tensor eigenvalue problems by translational invariant tensor ring approximations
We examine a method for solving an infinite‐dimensional tensor eigenvalue problem , where the infinite‐dimensional symmetric matrix exhibits a translational invariant structure. We provide a formulation of this type of problem from a numerical linear algebra point of view and describe how a power method applied to is used to obtain an approximation to the desired eigenvector. This infinite‐dimensional eigenvector is represented in a compact way by a translational invariant infinite Tensor Ring (iTR). Low rank approximation is used to keep the cost of subsequent power iterations bounded while preserving the iTR structure of the approximate eigenvector. We show how the averaged Rayleigh quotient of an iTR eigenvector approximation can be efficiently computed and introduce a projected residual to monitor its convergence. In the numerical examples, we illustrate that the norm of this projected iTR residual can also be used to automatically modify the time step to ensure accurate and rapid convergence of the power method.
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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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