Cholesky, in my opinion, is not robust as you have discovered. When it
encounters a nonpsd matrix it gives up. Maybe that is the correct course of
action, but I still think that when you are using the getCovariance to
estimate the curvature in the neighborhood of a point it would be okay to
take the generalized inverse and not worry too much about why your matrix is
bordering on nonPSDness... If your optimization stops on that point, that
is another story and should be flagged.
> A possibly more robust option here is to use Cholesky decomposition,
> > which is known to be stable for symmetric positive definite
> > matrices, which the covariance matrix being inverted here should
> > be. The exceptions thrown will be different; but they will give
> > more specific information about what is wrong with the covariance
> > matrix.
>
> I've tried it with my problem, and it also throws an exception.
> However, I would like to obtain the covariance matrix anyway, because I've
> no other clue as to what might be wrong.
> So I think that, at least, users should be able to set the positive
> definiteness threshold in order to avoid raising an exception.
>
>
>
