scipy.linalg.

diagsvd#

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

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

The documentation is written assuming array arguments are of specified “core” shapes. However, array argument(s) of this function may have additional “batch” dimensions prepended to the core shape. In this case, the array is treated as a batch of lower-dimensional slices; see Batched Linear Operations for details.

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

>>> import numpy as np
>>> 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]])