Luc,
However, it is also possible to set a non-diagonal 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 non-diagonal 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 non-diagonal 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 pre-multiplied 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 : +32-2-650.35.71 Boulevard du Triomphe * Fax : +32-2-650.42.26 B-1050 Bruxelles * NAC: HBZSC RG2Z6 http://sb9.astro.ulb.ac.be/~pourbaix * mailto:pourb...@astro.ulb.ac.be --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
