pypardiso-mopt 0.1.0

Python interface to the Intel MKL Pardiso library to solve large sparse linear systems of equations

PyPardiso

PyPardiso is a python package to solve large sparse linear systems of equations with the Intel oneAPI Math Kernel Library PARDISO solver, a shared-memory multiprocessing parallel direct sparse solver.

PyPardiso provides the same functionality as SciPy's scipy.sparse.linalg.spsolve for solving the sparse linear system Ax=b. However in many cases it is significantly faster than SciPy's built-in single-threaded SuperLU solver.

PyPardiso is not a python interface to the PARDISO Solver from the PARDISO 7.2 Solver Project and it also doesn't currently support complex numbers. Check out JuliaSparse/Pardiso.jl for these more advanced use cases.

Installation

PyPardiso runs on Linux, Windows and MacOS. It can be installed with conda or pip. It is recommended to install PyPardiso using a virtual environment.

conda-forge	PyPI
conda install -c conda-forge pypardiso	pip install pypardiso

Test complex numbers

import pypardiso
import numpy as np
import scipy.sparse as sp
from scipy.sparse.linalg import spsolve

A = sp.rand(10, 10, density=0.5, format='csr')
b = np.random.rand(10)
#x = pypardiso.spsolve(A, b)
x = pypardiso.spsolve(A, b, solver=pypardiso.PyPardisoSolver(mtype=pypardiso.Matrix_type.RNS))
print(x)
A = A.astype(np.cdouble)
b=b.astype(np.cdouble)
x = pypardiso.spsolve(A, b, solver=pypardiso.PyPardisoSolver(mtype=pypardiso.Matrix_type.CNS))
print(x)


A = sp.rand(10, 10, density=0.5, format='csr',dtype=np.cdouble)
x = pypardiso.spsolve(A, b, solver=pypardiso.PyPardisoSolver(mtype=pypardiso.Matrix_type.CNS))
print(x)

x1 = spsolve(A, b)
print(x1)

print(np.max(np.abs(x-x1)))

Basic usage

How to solve the sparse linear system Ax=b for x, where A is a square, sparse matrix in CSR (or CSC) format and b is a vector (or matrix):

In [1]: import pypardiso

In [2]: import numpy as np

In [3]: import scipy.sparse as sp

In [4]: A = sp.rand(10, 10, density=0.5, format='csr')

In [5]: A
Out[5]:
<10x10 sparse matrix of type '<class 'numpy.float64'>'
	with 50 stored elements in Compressed Sparse Row format>

In [6]: b = np.random.rand(10)

In [7]: x = pypardiso.spsolve(A, b)

In [8]: x
Out[8]:
array([ 0.02918389,  0.59629935,  0.33407289, -0.48788966,  3.44508841,
        0.52565687, -0.48420646,  0.22136413, -0.95464127,  0.58297397])