Skip to main content
`Ctrl`+`K`

# scipy.sparse.linalg.spsolve

scipy.sparse.linalg.spsolve(A, b, permc_spec=None, use_umfpack=True)

  Solve the sparse linear system Ax=b, where b may be a vector or a matrix.

  Parameters:
    A ndarray or sparse matrix

      The square matrix A will be converted into CSC or CSR form

    b ndarray or sparse matrix

      The matrix or vector representing the right hand side of the equation. If a vector, b.shape must be (n,) or (n, 1).

    permc_spec str, optional

      How to permute the columns of the matrix for sparsity preservation. (default: ‘COLAMD’)

      - `NATURAL`: natural ordering.

      - `MMD_ATA`: minimum degree ordering on the structure of A^T A.

      - `MMD_AT_PLUS_A`: minimum degree ordering on the structure of A^T+A.

      - `COLAMD`: approximate minimum degree column ordering [1], [2].

    use_umfpack bool, optional

      if True (default) then use UMFPACK for the solution [3], [4], [5], [6] . This is only referenced if b is a vector and `scikits.umfpack` is installed.

  Returns:
    x ndarray or sparse matrix

      the solution of the sparse linear equation. If b is a vector, then x is a vector of size A.shape[1] If b is a matrix, then x is a matrix of size (A.shape[1], b.shape[1])

  Notes

  For solving the matrix expression AX = B, this solver assumes the resulting matrix X is sparse, as is often the case for very sparse inputs. If the resulting X is dense, the construction of this sparse result will be relatively expensive. In that case, consider converting A to a dense matrix and using scipy.linalg.solve or its variants.

  References

  [1]

  T. A. Davis, J. R. Gilbert, S. Larimore, E. Ng, Algorithm 836: COLAMD, an approximate column minimum degree ordering algorithm, ACM Trans. on Mathematical Software, 30(3), 2004, pp. 377–380. DOI:10.1145/1024074.1024080

  [2]

  T. A. Davis, J. R. Gilbert, S. Larimore, E. Ng, A column approximate minimum degree ordering algorithm, ACM Trans. on Mathematical Software, 30(3), 2004, pp. 353–376. DOI:10.1145/1024074.1024079

  [3]

  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

  [4]

  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

  [5]

  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

  [6]

  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 import csc_matrix
      >>> from scipy.sparse.linalg import spsolve
      >>> A = csc_matrix([[3, 2, 0], [1, -1, 0], [0, 5, 1]], dtype=float)
      >>> B = csc_matrix([[2, 0], [-1, 0], [2, 0]], dtype=float)
      >>> x = spsolve(A, B)
      >>> np.allclose(A.dot(x).toarray(), B.toarray())
      True