scipy.linalg.svdvals¶

scipy.linalg.svdvals(a, overwrite_a=False, check_finite=True)[source]¶

Compute singular values of a matrix.

Parameters:

Parameters:	a : (M, N) array_like Matrix to decompose. overwrite_a : bool, optional Whether to overwrite a; may improve performance. Default is False. check_finite : bool, optional Whether to check that the input matrix contains 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.
Returns:	s : (min(M, N),) ndarray The singular values, sorted in decreasing order.
Raises:	LinAlgError If SVD computation does not converge.

a : (M, N) array_like

Matrix to decompose.

overwrite_a : bool, optional

Whether to overwrite a; may improve performance. Default is False.

check_finite : bool, optional

Whether to check that the input matrix contains 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.

Returns:

s : (min(M, N),) ndarray

The singular values, sorted in decreasing order.

Raises:

LinAlgError

If SVD computation does not converge.

See also

svd: Compute the full singular value decomposition of a matrix.
diagsvd: Construct the Sigma matrix, given the vector s.

Notes

svdvals(a) only differs from svd(a, compute_uv=False) by its handling of the edge case of empty a, where it returns an empty sequence:

>>> a = np.empty((0, 2))
>>> from scipy.linalg import svdvals
>>> svdvals(a)
array([], dtype=float64)