537 research outputs found
Large-scale computation of pseudospectra using ARPACK and eigs
ARPACK and its MATLAB counterpart, eigs, are software packages that calculate some eigenvalues of a large non-symmetric matrix by Arnoldi iteration with implicit restarts. We show that at a small additional cost, which diminishes relatively as the matrix dimension increases, good estimates of pseudospectra in addition to eigenvalues can be obtained as a by-product. Thus in large-scale eigenvalue calculations it is feasible to obtain routinely not just eigenvalue approximations, but also information as to whether or not the eigenvalues are likely to be physically significant. Examples are presented for matrices with dimension up to 200,000
A Hamiltonian Krylov-Schur-type method based on the symplectic Lanczos process
We discuss a Krylov-Schur like restarting technique applied within the symplectic Lanczos algorithm for the Hamiltonian eigenvalue problem. This allows to easily implement a purging and locking strategy in order to improve the convergence properties of the symplectic Lanczos algorithm. The Krylov-Schur-like restarting is based on the SR algorithm. Some ingredients of the latter need to be adapted to the structure of the symplectic Lanczos recursion. We demonstrate the efficiency of the new method for several Hamiltonian eigenproblems
Recursive Integral Method with Cayley Transformation
Recently, a non-classical eigenvalue solver, called RIM, was proposed to
compute (all) eigenvalues in a region on the complex plane. Without solving any
eigenvalue problem, it tests if a region contains eigenvalues using an
approximate spectral projection. Regions that contain eigenvalues are
subdivided and tested recursively until eigenvalues are isolated with a
specified precision. This makes RIM an eigensolver distinct from all existing
methods. Furthermore, it requires no a priori spectral information. In this
paper, we propose an improved version of {\bf RIM} for non-Hermitian eigenvalue
problems. Using Cayley transformation and Arnoldi's method, the computation
cost is reduced significantly. Effectiveness and efficiency of the new method
are demonstrated by numerical examples and compared with 'eigs' in Matlab
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