scipy.linalg.ldl(A, lower=True, hermitian=True, overwrite_a=False, check_finite=True)[source]

Computes the LDLt or Bunch-Kaufman factorization of a symmetric/ hermitian matrix.

This function returns a block diagonal matrix D consisting blocks of size at most 2x2 and also a possibly permuted unit lower triangular matrix L such that the factorization A = L D L^H or A = L D L^T holds. If lower is False then (again possibly permuted) upper triangular matrices are returned as outer factors.

The permutation array can be used to triangularize the outer factors simply by a row shuffle, i.e., lu[perm, :] is an upper/lower triangular matrix. This is also equivalent to multiplication with a permutation matrix where P is a column-permuted identity matrix I[:, perm].

Depending on the value of the boolean lower, only upper or lower triangular part of the input array is referenced. Hence a triangular matrix on entry would give the same result as if the full matrix is supplied.

a : array_like

Square input array

lower : bool, optional

This switches between the lower and upper triangular outer factors of the factorization. Lower triangular (lower=True) is the default.

hermitian : bool, optional

For complex-valued arrays, this defines whether a = a.conj().T or a = a.T is assumed. For real-valued arrays, this switch has no effect.

overwrite_a : bool, optional

Allow overwriting data in a (may enhance performance). The default is False.

check_finite : bool, optional

Whether to check that the input matrices contain 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.

lu : ndarray

The (possibly) permuted upper/lower triangular outer factor of the factorization.

d : ndarray

The block diagonal multiplier of the factorization.

perm : ndarray

The row-permutation index array that brings lu into triangular form.


If input array is not square.


If a complex-valued array with nonzero imaginary parts on the diagonal is given and hermitian is set to True.

See also

cholesky, lu


This function uses ?SYTRF routines for symmetric matrices and ?HETRF routines for Hermitian matrices from LAPACK. See [1] for the algorithm details.

Depending on the lower keyword value, only lower or upper triangular part of the input array is referenced. Moreover, this keyword also defines the structure of the outer factors of the factorization.

New in version 1.1.0.


[1](1, 2) J.R. Bunch, L. Kaufman, Some stable methods for calculating inertia and solving symmetric linear systems, Math. Comput. Vol.31, 1977. DOI: 10.2307/2005787


Given an upper triangular array a that represents the full symmetric array with its entries, obtain l, ‘d’ and the permutation vector perm:

>>> import numpy as np
>>> from scipy.linalg import ldl
>>> a = np.array([[2, -1, 3], [0, 2, 0], [0, 0, 1]])
>>> lu, d, perm = ldl(a, lower=0) # Use the upper part
>>> lu
array([[ 0. ,  0. ,  1. ],
       [ 0. ,  1. , -0.5],
       [ 1. ,  1. ,  1.5]])
>>> d
array([[-5. ,  0. ,  0. ],
       [ 0. ,  1.5,  0. ],
       [ 0. ,  0. ,  2. ]])
>>> perm
array([2, 1, 0])
>>> lu[perm, :]
array([[ 1. ,  1. ,  1.5],
       [ 0. ,  1. , -0.5],
       [ 0. ,  0. ,  1. ]])
array([[ 2., -1.,  3.],
       [-1.,  2.,  0.],
       [ 3.,  0.,  1.]])

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