Eigenvalue analysis and applications of the Legendre dual-Petrov-Galerkin methods for initial value problems

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
Desong Kong, Jie Shen, Li-Lian Wang, Shuhuang Xiang
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引用次数: 0

Abstract

In this paper, we show that the eigenvalues and eigenvectors of the spectral discretisation matrices resulting from the Legendre dual-Petrov-Galerkin (LDPG) method for the mth-order initial value problem (IVP): \(u^{(m)}(t)=\sigma u(t),\, t\in (-1,1)\) with constant \(\sigma \not =0\) and usual initial conditions at t\(=-1,\) are associated with the generalised Bessel polynomials (GBPs). In particular, we derive analytical formulae for the eigenvalues and eigenvectors in the cases m\(=1,2\). As a by-product, we are able to answer some open questions related to the collocation method at Legendre points (extensively studied in the 1980s) for the first-order IVP, by reformulating it into a Petrov-Galerkin formulation. Our results have direct bearing on the CFL conditions of time-stepping schemes with spectral or spectral-element discretisation in space. Moreover, we present two stable algorithms for computing zeros of the GBPs and develop a general space-time method for evolutionary PDEs. We provide ample numerical results to demonstrate the high accuracy and robustness of the space-time methods for some interesting examples of linear and nonlinear wave problems.

初值问题的特征值分析和 Legendre 双-Petrov-Galerkin 方法的应用
在本文中,我们证明了用 Legendre dual-Petrov-Galerkin (LDPG) 方法求 mth 阶初值问题(IVP)的谱离散化矩阵的特征值和特征向量:\(u^{(m)}(t)=\sigma u(t),\, t\in (-1,1)\) with constant \(\sigma \not =0\) and usual initial conditions at t\(=-1,\) are associated with the generalised Bessel polynomials (GBPs).特别是,我们推导出了 m\(=1,2\) 情况下的特征值和特征向量的解析公式。作为副产品,我们能够回答一些与一阶 IVP 的 Legendre 点配位法(20 世纪 80 年代进行了广泛研究)有关的未决问题,并将其重新表述为 Petrov-Galerkin 公式。我们的研究结果对空间谱或谱元离散化时间步进方案的 CFL 条件有直接影响。此外,我们还提出了两种计算 GBP 的零点的稳定算法,并开发了一种用于演化 PDE 的通用时空方法。我们提供了大量的数值结果,证明了时空方法在一些有趣的线性和非线性波问题实例中的高精度和鲁棒性。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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