William Hunter wrote:

I've been bugging Robert C. and Ed S. (thanks guys) for a while about this now, but I think it might be good if I share my experiences with other users.

When converting a sparse matrix to CSC format, one of the quickest ways to get there is to give <sparse.csc_matrix> three vectors containing;

(1) [values]: the values, (2) [rowind]: the row indices and (3) [colptr]: the column pointer.

If one times how long it takes to get your CSC matrix by doing sparse.csc_matrix((values, rowind, colptr)) compared to the other ways to get it, e.g., csc_matrix(dense_mtx), I've found that the first method is faster (enough to be significant for me).

One can solve a sparse system with <linsolve.spsolve>, if your matrix is in CSC format. And here's my question: Let's say I have a way (and I might :-)) to construct those 3 vectors very quickly, how can I use them directly as arguments in <linsolve.spsolve>? For example:

solution = linsolve.spsolve([values],[rowind],[colptr],[RHS])

If you have already (values, rowind, colptr) triplet, creating a CSC matrix is virtually a no-operation (besides some sanity checks). Is it really so inconvenient to write: solution = linsolve.spsolve( csc_matrix( (values, rowind, colptr), shape ),[RHS])? On the other hand, the syntax solution = linsolve.spsolve( (values,rowind,colptr,'csc'), [RHS]) might be useful as a syntactic sugar, so I am not against adding it. Note however, that you must indicate somehow if your triplet is CSR or CSC or ... - it is not much shorter then writing the full CSC constructor. r.