Select default sparse direct solver to be used.

useUmfpackbool, optional

Use UMFPACK [1], [2], [3], [4]. over SuperLU. Has effect only if scikits.umfpack is installed. Default: True

assumeSortedIndicesbool, optional

Allow UMFPACK to skip the step of sorting indices for a CSR/CSC matrix. Has effect only if useUmfpack is True and scikits.umfpack is installed. Default: False


The default sparse solver is UMFPACK when available (scikits.umfpack is installed). This can be changed by passing useUmfpack = False, which then causes the always present SuperLU based solver to be used.

UMFPACK requires a CSR/CSC matrix to have sorted column/row indices. If sure that the matrix fulfills this, pass assumeSortedIndices=True to gain some speed.



T. A. Davis, Algorithm 832: UMFPACK - an unsymmetric-pattern multifrontal method with a column pre-ordering strategy, ACM Trans. on Mathematical Software, 30(2), 2004, pp. 196–199.


T. A. Davis, A column pre-ordering strategy for the unsymmetric-pattern multifrontal method, ACM Trans. on Mathematical Software, 30(2), 2004, pp. 165–195.


T. A. Davis and I. S. Duff, A combined unifrontal/multifrontal method for unsymmetric sparse matrices, ACM Trans. on Mathematical Software, 25(1), 1999, pp. 1–19.


T. A. Davis and I. S. Duff, An unsymmetric-pattern multifrontal method for sparse LU factorization, SIAM J. Matrix Analysis and Computations, 18(1), 1997, pp. 140–158.


>>> import numpy as np
>>> from scipy.sparse.linalg import use_solver, spsolve
>>> from scipy.sparse import csc_matrix
>>> R = np.random.randn(5, 5)
>>> A = csc_matrix(R)
>>> b = np.random.randn(5)
>>> use_solver(useUmfpack=False) # enforce superLU over UMFPACK
>>> x = spsolve(A, b)
>>> np.allclose(, b)
>>> use_solver(useUmfpack=True) # reset umfPack usage to default