[SciPy-Dev] Making lobpcg support complex matrix
Wed Oct 17 07:35:46 CDT 2012
that is already a ticket
On 10/11/12, Gregory Crosswhite <firstname.lastname@example.org> wrote:
> Dear all,
> At the moment lobpcg seems to have been designed with only real numbers
> in mind; this is unfortunate because I have been having some trouble
> with eigs and was hoping to try it out as an alternative.
> Fortunately, it looks to me like the solution is rather simple. I was
> able to get it to work by replacing all instances of ".T" with
> ".T.conj()" --- which meant that all of the dot products were now
> correct for complex numbers --- and by replacing the cast to "float64"
> with a cast to "complex128". Changing the second cast is of course less
> than ideal in the cases where "float64" is indeed what is being used,
> but something like it is needed to make the answers not be garbage for
> complex input matrices.
> With these changes, I generated 10x10 complex Hermitian matrices A and B
> to serve as respectively the problem matrix and the normalization/metric
> matrix, and ran lobpcg with a tolerance of 1e-10. For the standard
> eigenvalue problem with k=2 (and random X) lobpcg got essentially the
> same answers as eigh for the lowest two eigenvalue/eigenvector pairs,
> and for the generalized eigenvalue problem it got the same eigenvalues
> as eigh and it got eigenvectors whose entries were within ~10^-4 of the
> eigenvectors returned by eigh (after dividing by the first entry to make
> the vectors proportionately the same) and such that the normalized
> overlap between the eigenvectors of lobpcg and eigh (using B as the
> metric) was within ~ 10^-8 of 1 (not surprising as this is the square of
> the first number). I ran a quick check where I dropped the imaginary
> parts of A and B and re-ran this analysis and say the errors fall to
> respectively ~ 10^-6 and 10^-12, so the algorithm gets less accurate
> results for complex numbers than for real numbers, though I don't know
> enough about how it works to speculate on which this would be.
> Anyway, I don't know much about lobpcg so there might be some issue that
> I've missed in simply adding ".conj()" everywhere a ".T" appeared to fix
> the dot products. I do think it would be very nice, though, to be able
> to use lobpcg for complex matrices, and so I would be willing to submit
> a patch towards this end.
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