Eigenvalue Analysis and Applications of the Legendre Dual-Petrov-Galerkin Methods for Initial Value Problems

TL;DR

This paper analyzes the eigenvalues and eigenvectors of spectral discretisation matrices from the Legendre dual-Petrov-Galerkin method for initial value problems, linking them to generalised Bessel polynomials and developing stable algorithms and spectral methods for PDEs.

Contribution

It establishes a connection between spectral discretisation matrices and generalised Bessel polynomials, and introduces stable algorithms and a space-time spectral method for solving PDEs.

Findings

Eigenvalues related to generalised Bessel polynomials.

Stable algorithms for zeros of GBPs.

High accuracy space-time spectral methods demonstrated.

Abstract

In this paper, we show that the eigenvalues and eigenvectors of the spectral discretisation matrices resulted from the Legendre dual-Petrov-Galerkin (LDPG) method for the $m$th-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). The essential idea of the analysis is to properly construct the basis functions for the solution and its dual spaces so that the matrix of the $m$th derivative is an identity matrix, and the mass matrix is then identical or approximately equals to the Jacobi matrix of the three-term recurrence of GBPs with specific integer parameters. This allows us to characterise the eigenvalue distributions and identify the eigenvectors. As a by-product, we are able to answer some open questions related to the very limited known results on the collocation method at Legendre points (studied in 1980s) for the first-order IVP, by reformulating it into a Petrov-Galerkin formulation. Moreover, we present two stable algorithms for computing zeros of the GBPs, and develop a general space-time spectral method for evolutionary PDEs using either the matrix diagonalisation, which is restricted to a small number of unknowns in time due to the ill-conditioning but is fully parallel, or the QZ decomposition which is numerically stable for a large number of unknowns in time but involves sequential computations. We provide ample numerical results to demonstrate the high accuracy and robustness of the space-time spectral methods for some interesting examples of linear and nonlinear wave problems.