[SciPy-user] coo_matrix, lapack, cvxopt
Tue Mar 24 07:37:24 CDT 2009
On Mon, Mar 23, 2009 at 8:59 PM, Eric Friedman <firstname.lastname@example.org> wrote:
> Hi, a couple of questions:
> 1) how do I look inside a coo_matrix -- I'd like to extract the i,j, data?
If A is your coo_matrix, you can grab the COO arrays using:
I = A.rows
J = A.cols
V = A.data
> 2) I need to solve large sparse rectangular matrix equation which doesn't have
> full rank, but is solvable. spsolve (dsolve) doesn't work. Can I do my own LUP
> decomposition using scipy.sparse?
I believe the UMFPACK scikit supports that functionality.
> 3) How does lapack compare to umfpack? (I'm thinking about using cvxopt which
> has lapack.) Also more generally, how does cvxopt's sparse routines do compared
> to scipy?
Unknown. There are not so many sparse factorization methods (SuperLU,
UMFPACK, Taucs), so it's likely that you'll get the one of them.
Nathan Bell email@example.com
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