`numpy.linalg:svdvals`

source: build-install/usr/lib/python3.14/site-packages/numpy/linalg/_linalg.py :1860

Signature

def svdvals ( x , / )

Summary

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.

Extended Summary

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).

Examples

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

✓

Determine the rank of a matrix using singular values:

s = np.linalg.svdvals([[1, 2, 3],
                       [2, 4, 6],
                       [-1, 1, -1]]); s

✓

np.count_nonzero(s > 1e-10)  # Matrix of rank 2

`numpy.linalg:svdvals`

Signature

Summary

Extended Summary

Parameters

Returns

Examples

See also

Aliases

Referenced by

This package