On 5 February 2024 05:36:45 GMT, 'Animesh Shree' via sage-devel
<sage-devel@googlegroups.com> wrote:
>I tried to use ctypes pythonapi to extract PyCapsule_New objects using
>PyCapsule_GetPointer. But got erorr :
> *ArgumentError: argument 1: TypeError: Don't know how to
>convert parameter 1*
>Attaching file
>
>---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
>Also, please tell me about the implementations for accessing opaque C
>objects I will go through it
check out the source code of cvxopt.
>On Tuesday, January 30, 2024 at 8:58:43 PM UTC+5:30 Dima Pasechnik wrote:
>
>> IMHO opaque C objects may be accessed from Python, it just needs an
>> implementation
>>
>>
>> On 29 January 2024 22:51:48 GMT, Nils Bruin <nbr...@sfu.ca> 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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