scipy.linalg.diagsvd¶

scipy.linalg.diagsvd(s, M, N)[source]¶

Construct the sigma matrix in SVD from singular values and size M, N.

Parameters

s(M,) or (N,) array_like: Singular values
Mint: Size of the matrix whose singular values are s.
Nint: Size of the matrix whose singular values are s.

Returns

S(M, N) ndarray: The S-matrix in the singular value decomposition

See also

svd: Singular value decomposition of a matrix
svdvals: Compute singular values of a matrix.

Examples

>>> from scipy.linalg import diagsvd
>>> vals = np.array([1, 2, 3])  # The array representing the computed svd
>>> diagsvd(vals, 3, 4)
array([[1, 0, 0, 0],
       [0, 2, 0, 0],
       [0, 0, 3, 0]])
>>> diagsvd(vals, 4, 3)
array([[1, 0, 0],
       [0, 2, 0],
       [0, 0, 3],
       [0, 0, 0]])

scipy.linalg.svdvals scipy.linalg.orth