scipy.linalg.svdvals

scipy.linalg.svdvals(a, overwrite_a=0)

Compute singular values of a matrix.

Parameters:

a : array, shape (M, N)

Matrix to decompose

overwrite_a : boolean

Whether data in a is overwritten (may improve performance)

Returns:

s: array, shape (K,) :

The singular values, sorted so that s[i] >= s[i+1]. K = min(M, N)

Raises LinAlgError if SVD computation does not converge :

See also

svd

return the full singular value decomposition of a matrix

diagsvd

return the Sigma matrix, given the vector s