torch.linalg.svdvals

torch.linalg.svdvals(A, *, out=None) → Tensor

Computes the singular values of a matrix.

Supports input of float, double, cfloat and cdouble dtypes. Also supports batches of matrices, and if A is a batch of matrices then the output has the same batch dimensions.

The singular values are returned in descending order.

Note

This function is equivalent to NumPy’s linalg.svd(A, compute_uv=False).

Note

When inputs are on a CUDA device, this function synchronizes that device with the CPU.

See also

torch.linalg.svd() computes the full singular value decomposition.

Parameters

A (Tensor) – tensor of shape (*, m, n) where * is zero or more batch dimensions.

Keyword Arguments

out (Tensor, optional) – output tensor. Ignored if None. Default: None.

Returns

A real-valued tensor, even when A is complex.

Examples:

>>> import torch
>>> a = torch.randn(5, 3)
>>> a
tensor([[-1.3490, -0.1723,  0.7730],
        [-1.6118, -0.3385, -0.6490],
        [ 0.0908,  2.0704,  0.5647],
        [-0.6451,  0.1911,  0.7353],
        [ 0.5247,  0.5160,  0.5110]])
>>> s = torch.linalg.svdvals(a)
>>> s
tensor([2.5139, 2.1087, 1.1066])