# numpy.linalg.svdvals#

linalg.svdvals(x, /)[source]#

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:
outndarray

An array with shape (…, K) that contains the vector(s) of singular values of length K, where K = min(M, N).

`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
```