T-systems: a theory of orthonormal functions with a tridiagonal differentiation matrix
This work addresses the need for efficient spectral methods in computational mathematics, offering incremental improvements in theoretical characterization and algorithm design.
The paper tackles the problem of characterizing orthonormal systems with skew-symmetric tridiagonal differentiation matrices for spectral methods in PDEs, providing an alternative constructive characterization using the differential Lanczos algorithm that applies to inner products like L2 and Sobolev norms, and extends preliminary results to sesquilinear forms for Hamiltonian energy conservation.
The starting point of this paper is that a spectral method is essentially a combination of an orthonormal basis of the underlying Hilbert space with Galerkin conditions. The choice of an orthonormal basis depends on a number of desirable features which we explore in the context of spectral methods for time-dependent partial differential equations in a single space dimension. A central role in ensuring many of the above features is played by the differentiation matrix of the underlying orthonormal system. In particular, it is beneficial if this matrix is skew-symmetric and tridiagonal. While orthonormal systems with this feature have been characterised in A. Iserles & M. Webb, ``Orthogonal systems with a skew-symmetric differentiation matrix'', Found. Comput. Maths, 19 (2019), 1191--1221, employing Fourier transforms, in this paper we provide an alternative characterisation using the differential Lanczos algorithm, which can be implemented constructively. It is valid for inner products that obey an `integration-by-parts condition', inclusive of $L_2$ and Sobolev norms on the real line. Motivated by quest for integration methods that conserve Hamiltonian energy, we conclude the paper replacing inner products by more general sesquilinear forms and presenting preliminary results. Here the Fourier transform characterisation generalises to spectral theory of Schrödinger operators and the differential Lanczos algorithm generalises to the differential Arnoldi algorithm.