Function svdvals

numpy/linalg/_linalg.py:1860–1906

Returns the singular values of a matrix (or a stack of matrices) ``x``. When x is a stack of matrices, the function will compute the singular values for each matrix in the stack. This function is Array API compatible. Calling ``np.svdvals(x)`` to get singular values is the sam

(x, /)

@array_function_dispatch(_svdvals_dispatcher)
def svdvals(x, /):
    """
    Returns the singular values of a matrix (or a stack of matrices) ``x``.
    When x is a stack of matrices, the function will compute the singular
    values for each matrix in the stack.

    This function is Array API compatible.

    Calling ``np.svdvals(x)`` to get singular values is the same as
    ``np.svd(x, compute_uv=False, hermitian=False)``.

    Parameters
    ----------
    x : (..., M, N) array_like
        Input array having shape (..., M, N) and whose last two
        dimensions form matrices on which to perform singular value
        decomposition. Should have a floating-point data type.

    Returns
    -------
    out : ndarray
        An array with shape (..., K) that contains the vector(s)
        of singular values of length K, where K = min(M, N).

    See Also
    --------
    scipy.linalg.svdvals : Compute singular values of a matrix.

    Examples
    --------

    >>> np.linalg.svdvals([[1, 2, 3, 4, 5],
    ...                    [1, 4, 9, 16, 25],
    ...                    [1, 8, 27, 64, 125]])
    array([146.68862757,   5.57510612,   0.60393245])

    Determine the rank of a matrix using singular values:

    >>> s = np.linalg.svdvals([[1, 2, 3],
    ...                        [2, 4, 6],
    ...                        [-1, 1, -1]]); s
    array([8.38434191e+00, 1.64402274e+00, 2.31534378e-16])
    >>> np.count_nonzero(s > 1e-10)  # Matrix of rank 2
    2

    """
    return svd(x, compute_uv=False, hermitian=False)


def _cond_dispatcher(x, p=None):