Update 5th June 2022: The PyPardiso package may now be installed simply with pip (see Installing PyPardiso and speed of Scipy spsolve):
- Install the MKL library: pip install mkl
- Install PyPardiso: pip install pypardiso
The PyPardiso package provides an interface to the Intel MKL Pardiso library to solve large sparse linear systems of equations. Trying to install this package with Conda raises the message:
To search for alternate channels that may provide the conda package you’re
looking for, navigate to https://anaconda.org and use the search bar at the top of the page.
This provides the link: https://anaconda.org/haasad/pypardiso
which provides the command line:
conda install -c haasad pypardiso
Ensure that the Intel MKL library is installed before installing pypardiso.
The pypardiso Github site is at:https://github.com/haasad/PyPardisoProject
This provides the following documentation:
I have now modified my py_spsolve function to use the PyPardiso solver by default:
@xl_func(disable_function_wizard_calc=True)
@xl_func(category = "Scipy-Linalg")
@xl_arg('v', 'numpy_array', ndim=1)
@xl_arg('i', 'numpy_array', ndim=1)
@xl_arg('j', 'numpy_array', ndim=1)
@xl_arg('ya', 'numpy_array', ndim=1)
@xl_return('numpy_array')
def py_spsolve(v, i, j, ya, out = 0, use_pardiso = True, permc_spec = 'MMD_AT_PLUS_A'):
"""
Solve a matrix equation Ax = y in sparse COO format.
:param v: value vector
:param i: row index vector
:param j: column index vector
:param ya: y vector
"""
timea = np.zeros((1,2))
stime =time.perf_counter()
n = ya.size
A = ssp.coo_matrix((v,(i,j)),shape=(n,n)).tocsc()
timea[0,0] = time.perf_counter()-stime
if use_pardiso:
res = spsolve(A, ya)
else:
res = sspla.spsolve(A, ya, permc_spec)
timea[0,1] = time.perf_counter()-stime
if out == 1:
return timea
else:
return res
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