jax.numpy.linalg.svdvals

jax.numpy.linalg.svdvals(x, /)

Compute the singular values of a matrix.

JAX implementation of numpy.linalg.svdvals().

Parameters:

x (jax.typing.ArrayLike) – array of shape (..., M, N) for which singular values will be computed.

Returns:

array of singular values of shape (..., K) with K = min(M, N).

Return type:

Array

See also

jax.numpy.linalg.svd(): compute singular values and singular vectors

Example

>>> x = jnp.array([[1, 2, 3],
...                [4, 5, 6]])
>>> jnp.linalg.svdvals(x)
Array([9.508031 , 0.7728694], dtype=float32)