torch.linalg.svdvals

torch.linalg.svdvals(A, *, driver=None, 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:

• driver (str, optional) – name of the cuSOLVER method to be used. This keyword argument only works on CUDA inputs. Available options are: None, gesvd, gesvdj, and gesvda. Check torch.linalg.svd() for details. Default: None.

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

Returns:

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

Examples:

>>> A = torch.randn(5, 3)
>>> S = torch.linalg.svdvals(A)
>>> S
tensor([2.5139, 2.1087, 1.1066])

>>> torch.dist(S, torch.linalg.svd(A, full_matrices=False).S)
tensor(2.4576e-07)