Hi All,
I've been attempting to use the GSL (specifically, the svd function) to
compute the pseudo-inverse of a potentially singular matrix. Unfortunately,
the smaller singular values that I am getting (with both
gsl_linalg_sv_decomp() or gsl_linalg_sv_decomp_jacobi()) are orders of
magnitude off from the true result (computed and verified in octave).
I do understand the issues with numerical computing, and yet this still
appears to be a strange issue (especially when you consider that most svd
algorithms are quite robust). For example, even if the matrix is not rank
deficient, I still get poor results from the svd, enough to cause the
pseudo-inverse to deviate significantly from the true inverse. I was going
to try calling svd with long double matrices to see whether this would
improve things any, but the gsl does not seem to support this (am I wrong?).
Any suggestions?
Thanks,
Evan
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