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 : +322650.35.71
B1050 Bruxelles * Fax : +322650.42.26
http://sb9.astro.ulb.ac.be/~pourbaix * mailto:pourbaix@astro.ulb.ac.be

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