# scipy.linalg.null_space¶

scipy.linalg.null_space(A, rcond=None)[source]

Construct an orthonormal basis for the null space of A using SVD

Parameters: A : (M, N) array_like Input array rcond : float, optional Relative condition number. Singular values s smaller than rcond * max(s) are considered zero. Default: floating point eps * max(M,N). Z : (N, K) ndarray Orthonormal basis for the null space of A. K = dimension of effective null space, as determined by rcond

svd
Singular value decomposition of a matrix
orth
Matrix range

Examples

One-dimensional null space:

>>> from scipy.linalg import null_space
>>> A = np.array([[1, 1], [1, 1]])
>>> ns = null_space(A)
>>> ns * np.sign(ns[0,0])  # Remove the sign ambiguity of the vector
array([[ 0.70710678],
[-0.70710678]])


Two-dimensional null space:

>>> B = np.random.rand(3, 5)
>>> Z = null_space(B)
>>> Z.shape
(5, 2)
>>> np.allclose(B.dot(Z), 0)
True


The basis vectors are orthonormal (up to rounding error):

>>> Z.T.dot(Z)
array([[  1.00000000e+00,   6.92087741e-17],
[  6.92087741e-17,   1.00000000e+00]])


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