Gilles,
Handling weighted observations must take correlations into account, i.e. use a _matrix_. There is the _practical_ problem of memory. Solving it correctly is by using a sparse implementation (and this is actually an implementation _detail_).
The problem is where something becomes a detail! You are right that the general least square problem copes with a matrix of weights ... but the way it is implemented is a detail. As already pointed out, even the vector of weights API allows for a complicated matrix of weights. The user premultiplies by the 'square root' of that matrix and sets all the compo- nents of the weight vector to 1. So, your enthusiasm to generalise the vector of weights to a matrix was a detail to make the life of very few users easier ... without adding any functionality. There are so many different configurations (e.g. block diagonal, ...), I doubt you can handle all of them in the most efficient way so it is likely preferable to have the user taking care of them. It is however true that simple weights (i.e. vector form) are a very usual situation ... which is also very common in fitting tools. So, I think CM should offer that approach as well. In conclusion: the old CM 3.0 API was enough! :) 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
