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Python interface to UMFPACK sparse direct solver.

Project description


scikit-umfpack provides wrapper of UMFPACK sparse direct solver to SciPy.


>>> from scikits.umfpack import spsolve, splu
>>> lu = splu(A)
>>> x = spsolve(A, b)

Installing scikits.umfpack also enables using UMFPACK solver via some of the scipy.sparse.linalg functions, for SciPy >= 0.14.0. Note you will need to have installed UMFPACK before hand. UMFPACK is parse of SuiteSparse.


scikit-umfpack depends on NumPy, SciPy, SuiteSparse, and swig is a build-time dependency.

Building SuiteSparse

SuiteSparse may be available from your package manager or as a prebuilt shared library. If that is the case use that if possible. Installation on Ubuntu 14.04 can be achieved with

sudo apt-get install libsuitesparse-dev

Otherwise, you will need to build from source. Unfortunately, SuiteSparse’s makefiles do not support building a shared library out of the box. You may find Stefan Fuertinger instructions helpful.

Furthmore, building METIS-4.0, an optional but important compile time dependency of SuiteSparse, has problems on newer GCCs. This patch and instructions from Nadir Soualem are helpful for getting a working METIS build.

Otherwise, I commend you to the documentation.


Releases of scikit-umfpack can be installed using pip. For a system-wide installation run:

pip install --upgrade scikit-umfpack

or for a user installation run

pip install --upgrade --user scikit-umfpack

To install scikit-umfpack from its source code directory, run in that directory (--user means a user installation):

pip install --upgrade --user .



You can check the latest sources with the command:

git clone

or if you have write privileges:

git clone


After installation, you can launch the test suite from outside the source directory (you will need to have the nose package installed):

nosetests -v scikits.umfpack

Project details

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scikit_umfpack-0.3.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (919.7 kB) Copy SHA256 hash SHA256 Wheel cp27
scikit_umfpack-0.3.2-cp27-cp27m-manylinux1_i686.whl (6.2 MB) Copy SHA256 hash SHA256 Wheel cp27
scikit_umfpack-0.3.2-cp27-cp27m-manylinux1_x86_64.whl (9.5 MB) Copy SHA256 hash SHA256 Wheel cp27
scikit_umfpack-0.3.2-cp27-cp27mu-manylinux1_i686.whl (6.2 MB) Copy SHA256 hash SHA256 Wheel cp27
scikit_umfpack-0.3.2-cp27-cp27mu-manylinux1_x86_64.whl (9.5 MB) Copy SHA256 hash SHA256 Wheel cp27
scikit_umfpack-0.3.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (917.9 kB) Copy SHA256 hash SHA256 Wheel cp34
scikit_umfpack-0.3.2-cp34-cp34m-manylinux1_i686.whl (6.2 MB) Copy SHA256 hash SHA256 Wheel cp34
scikit_umfpack-0.3.2-cp34-cp34m-manylinux1_x86_64.whl (9.5 MB) Copy SHA256 hash SHA256 Wheel cp34
scikit_umfpack-0.3.2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (917.9 kB) Copy SHA256 hash SHA256 Wheel cp35
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scikit_umfpack-0.3.2-cp35-cp35m-manylinux1_x86_64.whl (9.5 MB) Copy SHA256 hash SHA256 Wheel cp35
scikit_umfpack-0.3.2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (917.9 kB) Copy SHA256 hash SHA256 Wheel cp36
scikit_umfpack-0.3.2-cp36-cp36m-manylinux1_i686.whl (6.2 MB) Copy SHA256 hash SHA256 Wheel cp36
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