scipy.linalg.cholesky_banded¶
-
scipy.linalg.
cholesky_banded
(ab, overwrite_ab=False, lower=False, check_finite=True)[source]¶ Cholesky decompose a banded Hermitian positive-definite matrix
The matrix a is stored in ab either in lower diagonal or upper diagonal ordered form:
ab[u + i - j, j] == a[i,j] (if upper form; i <= j) ab[ i - j, j] == a[i,j] (if lower form; i >= j)
Example of ab (shape of a is (6,6), u=2):
upper form: * * a02 a13 a24 a35 * a01 a12 a23 a34 a45 a00 a11 a22 a33 a44 a55 lower form: a00 a11 a22 a33 a44 a55 a10 a21 a32 a43 a54 * a20 a31 a42 a53 * *
- Parameters
- ab(u + 1, M) array_like
Banded matrix
- overwrite_abbool, optional
Discard data in ab (may enhance performance)
- lowerbool, optional
Is the matrix in the lower form. (Default is upper form)
- check_finitebool, optional
Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.
- Returns
- c(u + 1, M) ndarray
Cholesky factorization of a, in the same banded format as ab
See also
cho_solve_banded
Solve a linear set equations, given the Cholesky factorization of a banded hermitian.
Examples
>>> from scipy.linalg import cholesky_banded >>> from numpy import allclose, zeros, diag >>> Ab = np.array([[0, 0, 1j, 2, 3j], [0, -1, -2, 3, 4], [9, 8, 7, 6, 9]]) >>> A = np.diag(Ab[0,2:], k=2) + np.diag(Ab[1,1:], k=1) >>> A = A + A.conj().T + np.diag(Ab[2, :]) >>> c = cholesky_banded(Ab) >>> C = np.diag(c[0, 2:], k=2) + np.diag(c[1, 1:], k=1) + np.diag(c[2, :]) >>> np.allclose(C.conj().T @ C - A, np.zeros((5, 5))) True