Lower and upper bounds of condition number for Vandermonde‐wise matrices and method of fundamental solutions using pseudo radial‐lines

IF 1.8 3区 数学 Q1 MATHEMATICS Numerical Linear Algebra with Applications Pub Date : 2022-09-06 DOI:10.1002/nla.2466
Li-Ping Zhang, Zi-Cai Li, Ming-Gong Lee, Hung-Tsai Huang
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Abstract

Consider the method of fundamental solutions (MFS) for 2D Laplace's equation in a bounded simply connected domain S$$ S $$ . In the standard MFS, the source nodes are located on a closed contour outside the domain boundary Γ(=∂S)$$ \Gamma \left(=\partial S\right) $$ , which is called pseudo‐boundary. For circular, elliptic, and general closed pseudo‐boundaries, analysis and computation have been studied extensively. New locations of source nodes are proposed along two pseudo radial‐lines outside Γ$$ \Gamma $$ . Numerical results are very encouraging and promising. Since the success of the MFS mainly depends on stability, our efforts are focused on deriving the lower and upper bounds of condition number (Cond). The study finds stability properties of new Vandermonde‐wise matrices on nodes xi∈[a,b]$$ {x}_i\in \left[a,b\right] $$ with 0
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Vandermonde矩阵条件数的下界和上界以及使用伪径向线的基本解方法
考虑有界单连通域S $$ S $$中二维拉普拉斯方程的基本解方法。在标准的MFS中,源节点位于域边界Γ(=∂S) $$ \Gamma \left(=\partial S\right) $$外的封闭轮廓上,称为伪边界。对于圆形、椭圆形和一般闭伪边界,分析和计算已经得到了广泛的研究。沿着Γ $$ \Gamma $$外的两条伪径向线提出了源节点的新位置。数值结果令人鼓舞和鼓舞。由于MFS的成功主要取决于稳定性,因此我们的工作重点是推导条件数(Cond)的下界和上界。研究了节点xi∈[a,b] $$ {x}_i\in \left[a,b\right] $$上具有0的新Vandermonde - wise矩阵的稳定性
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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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