Convergence and round-off errors in a two-dimensional eigenvalue problem using spectral methods and Arnoldi-Chebyshev algorithm

An efficient way of solving 2D stability problems in fluid mechanics is to use, after discretization of the equations that cast the problem in the form of a generalized eigenvalue problem, the incomplete Arnoldi-Chebyshev method. This method preserves the banded structure sparsity of matrices of the algebraic eigenvalue problem and thus decreases memory use and CPU-time consumption. The errors that affect computed eigenvalues and eigenvectors are due to the truncation in the discretization and to finite precision in the computation of the discretized problem. In this paper we analyze those two errors and the interplay between them. We use as a test case the two-dimensional eigenvalue problem yielded by the computation of inertial modes in a spherical shell. This problem contains many difficulties that make it a very good test case. It turns out that that single modes (especially most-damped modes i.e. with high spatial frequency) can be very sensitive to round-off errors, even when apparently good spectral convergence is achieved. The influence of round-off errors is analyzed using the spectral portrait technique and by comparison of double precision and extended precision computations. Through the analysis we give practical recipes to control the truncation and round-off errors on eigenvalues and eigenvectors.Comment: 15 pages, 9 figure

Numerical Solution of Linear and Nonlinear Eigenvalue Problems

Given a real parameter-dependent matrix, we obtain an algorithm for computing the value of the parameter and corresponding eigenvalue for which two eigenvalues of the matrix coalesce to form a 2-dimensional Jordan block. Our algorithms are based on extended versions of the implicit determinant method of Spence and Poulton [55]. We consider when the eigenvalue is both real and complex, which results in solving systems of nonlinear equations by Newton’s or the Gauss-Newton method. Our algorithms rely on good initial guesses, but if these are available, we obtain quadratic convergence. Next, we describe two quadratically convergent algorithms for computing a nearby defective matrix which are cheaper than already known ones. The first approach extends the implicit determinant method in [55] to find parameter values for which a certain Hermitian matrix is singular subject to a constraint. This results in using Newton’s method to solve a real system of three nonlinear equations. The second approach involves simply writing down all the nonlinear equations and solving a real over-determined system using the Gauss-Newton method. We only consider the case where the nearest defective matrix is real. Finally, we consider the computation of an algebraically simple complex eigenpair of a nonsymmetric matrix where the eigenvector is normalised using the natural 2-norm, which produces only a single real normalising equation. We obtain an under-determined system of nonlinear equations which is solved by the Gauss-Newton method. We show how to obtain an equivalent square linear system of equations for the computation of the desired eigenpairs. This square system is exactly what would have been obtained if we had ignored the non uniqueness and nondifferentiability of the normalisation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo