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 - ssmaller than- rcond * 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 - >>> import numpy as np >>> 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.]])