scipy.linalg.orth#
- scipy.linalg.orth(A, rcond=None)[source]#
Construct an orthonormal basis for the range of A using SVD
- Parameters
- A(M, N) array_like
Input array
- rcondfloat, optional
Relative condition number. Singular values
s
smaller thanrcond * max(s)
are considered zero. Default: floating point eps * max(M,N).
- Returns
- Q(M, K) ndarray
Orthonormal basis for the range of A. K = effective rank of A, as determined by rcond
See also
svd
Singular value decomposition of a matrix
null_space
Matrix null space
Examples
>>> from scipy.linalg import orth >>> A = np.array([[2, 0, 0], [0, 5, 0]]) # rank 2 array >>> orth(A) array([[0., 1.], [1., 0.]]) >>> orth(A.T) array([[0., 1.], [1., 0.], [0., 0.]])