Luc,
I think there are two parts in the isse. The first one is related to sparse matrix and we don't have an answer yet. The second part is related to compute a partial set of singular values. This is used for example in image compression or to find a matrix with reduce rank that is the closest possible to an input matrix. for this part, we may have an answer.
What do you mean by partial set of singular values? If you mean setting all the singular values which are either below some threshold, of index above some value (rank), ... to zero and to compute the resulting product as an approximation of the original matrix, this is no longer the business of SVD but rather the user business as (s)he decides what (s)he does with the decomposition. However, one could add a method to SVD which would return such a 'product'. Regards, Dim. ---------------------------------------------------------------------------- Dimitri Pourbaix * Institut d'Astronomie et d'Astrophysique * Don't worry, be happy CP 226, office 2.N4.211, building NO * and CARPE DIEM. Universite Libre de Bruxelles * Boulevard du Triomphe * Tel : +32-2-650.35.71 B-1050 Bruxelles * Fax : +32-2-650.42.26 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
