At Sat, 21 Mar 2009 18:10:07 +0100, Christophe Dehais wrote: > > I'm trying to compute the null space of a matrix A with M < N, using > the residual singular vectors of the SVD decomposition of A. > However, as the SVD code doesn't allow M < N, I have to work on A tanspose. > I could use the fact that if A' = U.S.V' then A = V.S.U' and return > the last vectors of U, but U is actually truncated because we compute > a thin SVD, > so some (or all in case M is full rank) vectors of M's null space are lost. > > I'm guessing that either the SVD code should support the case M < N or > there should be an option to compute the full SVD decomposition.
I agree that it would be good to add support for M<N to the SVD. Any help appreciated. I believe it's possible to also get the null space from a QR decomposition. -- Brian Gough (GSL Maintainer) Support freedom by joining the FSF! http://www.fsf.org/associate/support_freedom/join_fsf?referrer=37 _______________________________________________ Help-gsl mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-gsl
