Hi I am wondering if anyone has figured out a way to solve the generalized eigenvalue problem as presented in this paper: https://arxiv.org/abs/1511.04707. The original work was done in theano using theano.tensor.slinalg.Eigvalsh . but in pytorch there is not an easy way of solving this generalized eigenvalue problem with a similar function. I’m wondering if anyone has any tips on how to either recast the problem or if there is another way of solving this.
Thanks!
Isn’t torch.symeig very similar?
It doesn’t have backward just yet, but people are working on it.
Best regards
Thomas
torch.symeig solves a regular symmetric eigenvalue problem but not the generalized. In my case I have essentially Ax = lambda*Bx where A and B are symmetric PD matrices. I guess if B is non-singular I could solve this by computing inv(B)*A and solving that regular eigenvalue problem but there are no longer guarantees on the symmetry of that matrix and the inverse is generally unstable.
There is a better, albeit computationally expensive reduction using the cholesky decomposition (but use upper=False) of B:
http://www.alglib.net/eigen/symmetric/generalizedsymmevd.php
In the meantime, the symeig backward was merged in master.
Best regards
Thomas
Cool I’ll check that out- thanks.