[SciPy-user] Rank-Revealing QR factorization of dense matrices
nwagner at mecha.uni-stuttgart.de
Tue Oct 12 09:31:29 CDT 2004
Robert Kern wrote:
> Nils Wagner wrote:
>> Hi all,
>> Has someone written a function for Rank-Revealing QR factorization of
>> dense matrices in scipy ?
> No. The relevant LAPACK function set is xGEQP3, I believe.
> Contributions are welcome.
BTW, a good source is
C. H. Bischof, G. Quintana-Orti
Algorithm 782: Codes for Rank-Revealing QR Factorizations of Dense
ACM Transactions on Mathematical software, Vol.24 No. 2 pp. 254-257
>> Is there a function to determine the numerical rank of a matrix in
>> scipy ?
> Tip: grep the source before asking here.
> Offhand, I don't see one standing alone.
> If your arrays aren't too large, you can use linalg.svdvals to compute
> the singular values. The number of values greater than the largest
> value times machine epsilon is a good estimate of the rank.
> Alternatively, the rank* of A can be computed by linalg.lstsq(A,
> * It's documented as the "effective rank." I think we're talking about
> the same thing, though.
It would be nice to have a built-in function (e.g. rank(A)) for this
Maybe linalg.orth can be used in this context.
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