Using implicitly filtered RKS for generalised eigenvalue problems

The rational Krylov sequence (RKS) method can be seen as a generalisation of Arnoldiʹs method. It projects a matrix pencil onto a smaller subspace; this projection results in a small upper Hessenberg pencil. As for the Arnoldi method, RKS can be restarted implicitly, using the QR decomposition of a Hessenberg matrix. This restart comes with a projection of the subspace using a rational function. In this paper, it is shown how the restart can be worked out in practice. In a second part, it is shown when the filtering of the subspace basis can fail and how this failure can be handled by deflating a converged eigenvector from the subspace, using a Schur-decomposition.