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.
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 Fürtinger 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
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
--user means a user installation):
pip install --upgrade --user .
You can check the latest sources with the command:
git clone https://github.com/scikit-umfpack/scikit-umfpack.git
or if you have write privileges:
git clone firstname.lastname@example.org:scikit-umfpack/scikit-umfpack.git
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