Luc,
> However, it is also possible to set a nondiagonal weight matrix, and
> one class (AbstractLeastSquaresOptimizer) performs an eigen dcomposition
> on the matrix to extract its square root. I don't know any use case for
> this, but it has been set up this way, so I guess someone has a use for
> nondiagonal weights.
Such a situation occurs when observations are correlated. That is
actually the most general expression for a least square problem.
> I wonder if I should simply add this as is or if we should rather remove
> the nondiagonal weights feature and support only vector weights.
Even if a vector of weights is convenient, it would only cover a subset
of situations. However, even a vector of weights is not needed if both
the models and the observations are premultiplied by the square root
of their weight. By the way, I remind you that those weights already
caused some bugs in the 2.0 release.
Personnally, I could live with a vector form.
As a more general comment, I find it amazing that all the +1 for the
release were only concerned by the compliance with (commons) rules,
configuration files, ... Just 4 days after the release, you suddenly
figure out that a user is in trouble and you want a quick fix. Maybe
such a test would have been need BEFORE the release!
Regards,
Dim.

Dimitri Pourbaix * Don't worry, be happy
Institut d'Astronomie et d'Astrophysique * and CARPE DIEM.
CP 226, office 2.N4.211, building NO *
Universite Libre de Bruxelles * Tel : +322650.35.71
Boulevard du Triomphe * Fax : +322650.42.26
B1050 Bruxelles * NAC: HBZSC RG2Z6
http://sb9.astro.ulb.ac.be/~pourbaix * mailto:pourbaix@astro.ulb.ac.be

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