
doi: 10.15480/882.61
We discuss the state of the art in numerical solution methods for large scale polynomial or rational eigenvalue problems. We present the currently available solution methods such as the Jacobi-Davidson, Arnoldi or the rational Krylov method and analyze their properties. We briefly introduce a new linearization technique and demonstrate how it can be used to improve structure preservation and with this the accuracy and efficiency of linearization based methods. We present several recent applications where structured and unstructured nonlinear eigenvalue problems arise and some numerical results.
Nichtlineares Eigenwertproblem, Eigenvalues, singular values, and eigenvectors, Krylov-Verfahren, projection method, Matrizenpolynom, linearization, Krylov-subspace method, structure preservation, Arnoldi method, Projektionsverfahren, Mathematik, rational-Krylov method, Eigenvalues, eigenvectors, matrix polynomial
Nichtlineares Eigenwertproblem, Eigenvalues, singular values, and eigenvectors, Krylov-Verfahren, projection method, Matrizenpolynom, linearization, Krylov-subspace method, structure preservation, Arnoldi method, Projektionsverfahren, Mathematik, rational-Krylov method, Eigenvalues, eigenvectors, matrix polynomial
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
