spsolve_triangular(A, b, lower=True, overwrite_A=False, overwrite_b=False, unit_diagonal=False)
A sparse square triangular matrix. Should be in CSR format.
Right-hand side matrix in A x = b
Whether A is a lower or upper triangular matrix. Default is lower triangular matrix.
Allow changing A. The indices of A are going to be sorted and zero entries are going to be removed. Enabling gives a performance gain. Default is False.
Allow overwriting data in b. Enabling gives a performance gain. Default is False. If :None:None:`overwrite_b` is True, it should be ensured that b has an appropriate dtype to be able to store the result.
If True, diagonal elements of a are assumed to be 1 and will not be referenced.
If A is singular or not triangular.
Solve the equation A x = b
for x, assuming A is a triangular matrix.
>>> from scipy.sparse import csr_matrixSee :
... from scipy.sparse.linalg import spsolve_triangular
... A = csr_matrix([[3, 0, 0], [1, -1, 0], [2, 0, 1]], dtype=float)
... B = np.array([[2, 0], [-1, 0], [2, 0]], dtype=float)
... x = spsolve_triangular(A, B)
... np.allclose(A.dot(x), B) True
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.sparse.linalg._dsolve.linsolve.spsolve_triangular
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