Fix orthgonality loss in Davidson using Gram-Schmidt reorthogonalisation

[AdrianLDempwolff]

This pull request covers several issues related to the Davidson solver:

• The Davidson solver occasionally lost subspace orthogonality, sometimes leading to linear dependence within the subspace, resulting in instant solver convergence towards zero eigenpairs.
  With this pull request, subspace orhogonality loss is fixed using a conventional Gram-Schmidt reorthogonalisation in case this is needed, i.e., whenever the vector to be added to the subspace has non-negligible overlap with the previous subspace vectors. This comes at the expense of, at least, computing n_ss_vec addtional scalar products in each step, which in small systems may amount up to 5% of the total computational time spent for the Davidson solver. The overhead scales $N^4$, that is, for larger systems, the relative penalty can be expected to be negligible.

• In the Davidson solver setup, it was not differentiated between the initial subspace size, that is, the number of guesses provided, and the Davidson block size n_block. In cases where the number of roots to be converged is considerably smaller than the number of guesses, large n_block values lead to a considerable overhead in computational costs compared to a conventional strategy in which the Davidson block size is set to be equal to the number of roots to be converged.
  With this pull request, the default is changed such that the Davidson block size is set to be equal to the number of roots to be converged.