[SciPy-user] LU decomposition of a sparse matrix ?

Nils Wagner dr.nwagner at web.de
Sat Jan 17 07:20:01 CST 2004


SciPy Users List <scipy-user at scipy.net> schrieb am 16.01.04 23:47:06: 
 
Nils Wagner wrote: 
> Hi all, 
>  
> from scipy import * 
> from scipy.sparse import * 
> def sylvester(n): 
>     d = zeros((2, n), Float) 
>     d[0, :] = arange(n, 0, -1) 
>     d[1, :] = arange(1, n+1) 
>     print d 
>     A = spdiags(d, [-1, 1], n, n) 
>     return A 
> n = 6 
> S = sylvester(n) 
> print S 
>  
> Is it possible to convert a sparse matrix into a full matrix in scipy ? 
 
There is not a canned function yet I don't think (but check the class  
definition). 
 
>  
> How about LU decomposition in case of sparse matrices ? 
>  
 
Look at the example of solving a sparse linear system (it does an LU  
decomposition first, I believe). 
 
def sparse_linear_solve(A,b): 
    assert isspmatrix(A) 
    assert A.storage=='CSR' 
    gssv = eval('_superlu.' + A.ftype + 'gssv') 
    return gssv(A.shape[0],A.shape[1],A.laste+1,A.data,A.index[0]-1,A.index[1]-1,b) 
 
 
splinsolve = sparse_linear_solve 
 
What is the meaning of gssv ? 
 
Nils 
 
-Travis 
 
 
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