jax.lax.linalg.svd

jax.lax.linalg.svd(x: ArrayLike, *, full_matrices: bool = 'True', compute_uv: Literal[True]) → Tuple[jax.Array, jax.Array, jax.Array]

jax.lax.linalg.svd(x: ArrayLike, *, full_matrices: bool = 'True', compute_uv: Literal[False]) → jax.Array

jax.lax.linalg.svd(x: ArrayLike, *, full_matrices: bool = 'True', compute_uv: bool = 'True') → Union[jax.Array, Tuple[jax.Array, jax.Array, jax.Array]]

Singular value decomposition.

Returns the singular values if compute_uv is False, otherwise returns a triple containing the left singular vectors, the singular values and the adjoint of the right singular vectors.

Parameters

• x (Union[Array, ndarray, bool_, number, bool, int, float, complex]) –

• full_matrices (bool) –

• compute_uv (bool) –

Return type

Union[Array, Tuple[Array, Array, Array]]