scipy.sparse.linalg.spsolve¶

scipy.sparse.linalg.
spsolve
(A, b, permc_spec=None, use_umfpack=True)[source]¶ 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
use_umfpack : bool, optional
if True (default) then use umfpack for the solution. This is only referenced if b is a vector and
scikitumfpack
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.