Independent eigenvector computation for a given set of eigenvalues of typical engineering
eigenvalue problems still is a big challenge for established subspace solution methods. The
inverse vector iteration as the standard solution method often is not capable of reliably computing
the eigenvectors of a cluster of bad separated eigenvalues.
The following contribution presents a stable and reliable solution method for independent
and selective eigenvector computation of large symmetric profile matrices. The method
is an extension of the well-known and well-understood QR-method for full matrices thus
having all its good numerical properties. The effects of finite arithmetic precision of
computer representations of eigenvalue/eigenvector solution methods are analysed and it is
shown that the numerical behavior of the new method is superior to subspace solution methods
