On 5 February 2024 06:30:45 GMT, 'Animesh Shree' via sage-devel
<sage-devel@googlegroups.com> wrote:
>The other library that scipy uses is SuperLU :
>https://portal.nersc.gov/project/sparse/superlu/
>for the function scipy.sparse.linalg.splu :
>https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.splu.html#scipy-sparse-linalg-splu
>
>Scipy supports only factorization for square matrices
>the developer asks whether its a limitation or not :
>https://github.com/scipy/scipy/blob/4edfcaa3ce8a387450b6efce968572def71be089/scipy/sparse/linalg/_dsolve/linsolve.py#L424
>
>In reference manual of SuperLU :
>https://portal.nersc.gov/project/sparse/superlu/ug.pdf
>In Section 2.4, page 23 we can see code where *"nrow = 5; ncol = 5; *" are
>defined separately. ( The example intended to solved a 5x5 system )
it is the matter of adding extra zero rows or columns to the matrix you want to
decompose. This could be a quick fix.
A good implementation of LU decomposition ought actually to take non-square
matrix as input, and have the indices adjusted appropriately in the algorithm,
so it's indeed a bit strange that superLU only takes square matrices (?).
Perhaps it's a good idea to look at its docs and the source code.
>
>On Tuesday, January 30, 2024 at 4:21:48 AM UTC+5:30 Nils Bruin wrote:
>
>> By the looks of it, the routines you'd be using would be coming from
>> umfpack. cvxopt has chosen to package the details of LU factorization in
>> opaque objects and instead offer routines to use these decompositions (via
>> taking the opaque object as input). In scipy, umfpack is also used:
>> https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.spsolve.html#scipy.sparse.linalg.spsolve
>>
>> . Scipy also offers LU decomposition routines:
>> https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.splu.html#scipy.sparse.linalg.splu
>>
>> but that uses a different library. It looks suspicious that two packages
>> offer umfpack for solving sparse problems, but don't give explicit access
>> to LU factorizations produced in the process, when using UMFPACK. Perhaps
>> UMFPACK isn't suited to provide the explicit factorization (but it may be
>> very good at using its internally computed data).
>>
>>
>>
>>
>
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