guarantees on their behaviour when the inputs have non-finite values
like "inf" or "NaN". As such, neither does PyTorch. The operations may
return a tensor with non-finite values, or raise an exception, or even
segfault.
Consider using "torch.isfinite()" before calling these functions to
detect this situation.
Extremal values in linalg
Functions within "torch.linalg" have more Extremal Values than other
PyTorch functions.
Solvers and Inverses assume that the input matrix "A" is invertible.
If it is close to being non-invertible (for example, if it has a very
small singular value), then these algorithms may silently return
incorrect results. These matrices are said to be ill-conditioned. If
provided with ill-conditioned inputs, the result of these functions
they may vary when using the same inputs on different devices or when
using different backends via the keyword "driver".
Spectral operations like "svd", "eig", and "eigh" may also return