Deprecate native dependency on ``umfpack``

[jinningwang]

Is your feature request related to a problem? Please describe. The native dependency on umfpack via kvxopt can introduce license issue. The involved implementation in ANDES is in: https://github.com/CURENT/andes/blob/5ab784b75347a98d07c004e32d7c4b39d407ea2e/andes/linsolvers/suitesparse.py#L7-L8

Further, it can be traced back to SuiteSparse via kvxopt: https://github.com/sanurielf/kvxopt/blob/cc4cd9a4f2f27c2f5cac1363ba0f36574b16b0bd/setup.py#L76-L93

Describe the solution you'd like

For reference, package SciPy provides wrappers of umfpack (via a BSD licensed packagescikit-umfpack), but doesn't include it as a dependency.

Code: https://github.com/scipy/scipy/blob/9e9d534b7afac90e8d2fa23be1d0ab1201c1bde1/scipy/sparse/linalg/_dsolve/linsolve.py#L13-L19

Documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.use_solver.html

Dependency: In the dependency, the package scikit-umfpack is included in [test] rather than in [build-system]. https://github.com/scipy/scipy/blob/9e9d534b7afac90e8d2fa23be1d0ab1201c1bde1/pyproject.toml#L18-L32

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Additional context

[cuihantao]

Jinning,

Thanks! UNFPACK support comes for free with KVXOPT or cvxopt. Once we replace the sparse matrices handling with SciPy, we will automatically switch to using UMFPACK from there.

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From: Jinning Wang @.> Sent: Thursday, August 15, 2024 9:59:25 AM To: CURENT/andes @.> Cc: Hantao Cui @.>; Assign @.> Subject: Re: [CURENT/andes] Deprecate native dependency on umfpack (Issue #562)

Assigned #562https://github.com/CURENT/andes/issues/562 to @cuihantaohttps://github.com/cuihantao.

— Reply to this email directly, view it on GitHubhttps://github.com/CURENT/andes/issues/562#event-13896180517, or unsubscribehttps://github.com/notifications/unsubscribe-auth/ABSNZA3I7LJUYTXCKOSCQBTZRSX33AVCNFSM6AAAAABMSIHBJ2VHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJTHA4TMMJYGA2TCNY. You are receiving this because you were assigned.Message ID: @.***>

[jinningwang]

I'm thinking that, maybe we can benchmark the performance of involved methods? Here is a code to my mind.

Feel free to let me know if you have any other comments.

scipy.sparse.linalg import spsolve

# assume `A` is a 2D matrix and `b` is a vector

# method1
x1 = spsolve(A, b, use_umfpack=True)

# method2
x2 = spsolve(A, b, use_umfpack=False)

[cuihantao]

Sure. This is benchmarking UMFPACK and SuperLU performance. And remember the default solver is KLU, which is probably 30% faster than UMFPACK. And.. using KVXOPT to handle sparse matrices can be 20%-50% faster than using SciPy for systems with, say, 10,000 buses.

On Aug 15, 2024, at 11:12 AM, Jinning Wang @.***> wrote:

I'm thinking that, maybe we can benchmark the performance of involved methods? Here is a code to my mind.

Feel free to let me know if you have any other comments.

scipy.sparse.linalg import spsolve

assume A is a 2D matrix and b is a vector

method1

x1 = spsolve(A, b, use_umfpack=True)

method2

x2 = spsolve(A, b, use_umfpack=False) — Reply to this email directly, view it on GitHub https://github.com/CURENT/andes/issues/562#issuecomment-2291495059, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABSNZA72HTK7S2KU3GAU5PDZRTANDAVCNFSM6AAAAABMSIHBJ2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEOJRGQ4TKMBVHE. You are receiving this because you were mentioned.