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orth(A, rcond=None)

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).

Returns

Q : (M, K) ndarray

Orthonormal basis for the range of A. K = effective rank of A, as determined by rcond

Construct an orthonormal basis for the range of A using SVD

See Also

null_space

Matrix null space

svd

Singular value decomposition of a matrix

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.]])
See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.linalg._decomp_svd.null_space scipy.linalg._decomp_svd.orth scipy.linalg._decomp_svd.subspace_angles

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GitHub : /scipy/linalg/_decomp_svd.py#284
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