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scipy.sparse.linalg.

# use_solver

scipy.sparse.linalg.use_solver(**kwargs)

  Select default sparse direct solver to be used.

  Parameters:
    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

  Notes

  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.

  References

  [1]

  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. https://dl.acm.org/doi/abs/10.1145/992200.992206

  [2]

  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. https://dl.acm.org/doi/abs/10.1145/992200.992205

  [3]

  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. https://doi.org/10.1145/305658.287640

  [4]

  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. https://doi.org/10.1137/S0895479894246905T.

  Examples

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