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