svdvals¶

paddle.linalg. svdvals ( x: Tensor, name: str | None = None ) → Tensor [source]

Computes the singular values of one matrix or a batch of matrices.

Let \(X\) be the input matrix or a batch of input matrices, the output singular values \(S\) are the diagonal elements of the matrix produced by singular value decomposition:

\[X = U * diag(S) * V^{H}\]

Parameters

• x (Tensor) – The input tensor. Its shape should be […, M, N], where … is zero or more batch dimensions. The data type of x should be float32 or float64.

• name (str|None, optional) – Name for the operation. For more information, please refer to Name. Default: None.

Returns

Singular values of x. The shape is […, K], where K = min(M, N).

Return type

Tensor

Examples

>>> import paddle

>>> x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]])
>>> s = paddle.linalg.svdvals(x)
>>> print(s)
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[8.14753819, 0.78589684])