torch.logdet¶

torch.logdet(input) → Tensor¶

Calculates log determinant of a square matrix or batches of square matrices.

Note

Result is -inf if input has zero log determinant, and is nan if input has negative determinant.

Note

Backward through logdet() internally uses SVD results when input is not invertible. In this case, double backward through logdet() will be unstable in when input doesn’t have distinct singular values. See svd() for details.

Parameters

input (Tensor) – the input tensor of size (*, n, n) where * is zero or more batch dimensions.

Example:

>>> A = torch.randn(3, 3)
>>> torch.det(A)
tensor(0.2611)
>>> torch.logdet(A)
tensor(-1.3430)
>>> A
tensor([[[ 0.9254, -0.6213],
         [-0.5787,  1.6843]],

        [[ 0.3242, -0.9665],
         [ 0.4539, -0.0887]],

        [[ 1.1336, -0.4025],
         [-0.7089,  0.9032]]])
>>> A.det()
tensor([1.1990, 0.4099, 0.7386])
>>> A.det().log()
tensor([ 0.1815, -0.8917, -0.3031])