A fast and accurate algorithm for solving linear systems associated with a class of negative matrix

IF 1.8 3区 数学 Q1 MATHEMATICS
Zhao Yang
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引用次数: 2

Abstract

A class of negative matrices including Vandermonde‐like matrices tends to be extremely ill‐conditioned, and linear systems associated with this class of matrices appear in the polynomial interpolation problems. In this article, we present a fast and accurate algorithm with O(n2)$$ O\left({n}^2\right) $$ complexity to solve the linear systems whose coefficient matrices belong to the class of negative matrix. We show that the inverse of any such matrix is generated in a subtraction‐free manner. Consequently, the solutions of linear systems associated with the class of negative matrix are accurately determined by parameterization matrices of coefficient matrices, and a pleasantly componentwise forward error is provided to illustrate that each component of the solution is computed to high accuracy. Numerical experiments are performed to confirm the claimed high accuracy.
一类负矩阵线性方程组的快速精确求解算法
包括类范德蒙德矩阵在内的一类负矩阵往往是极端病态的,与这类矩阵相关的线性系统出现在多项式插值问题中。在本文中,我们提出了一个具有O(n2)$$O\left({n}^2 \right)$$复杂度的快速精确算法来求解系数矩阵属于负矩阵类的线性系统。我们证明了任何这样的矩阵的逆都是以无减法的方式生成的。因此,与负矩阵类相关的线性系统的解由系数矩阵的参数化矩阵精确地确定,并且提供了令人愉快的分量前向误差,以说明解的每个分量都是高精度计算的。进行了数值实验以证实所声称的高精度。
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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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