Hi mmutic!



 mmutic:

I need to raise a matrix to the power of -1/2, but I believe doing so using just **(-1/2) makes the computation element-wise.


A standard approach to exponentiating a matrix is to calculate its
eigendecomposition and then exponentiate the eigenvalues.
Here is a pytorch version 0.3.0 script that illustrates this:
import torch
print (torch.__version__)

m = torch.FloatTensor([[.5,.5],[.7,.9]])           # original matrix

# desired result
mres = torch.FloatTensor ([[ 2.69776664, -1.10907208], [-1.55270001,  1.81051025]])

evals, evecs = torch.eig (m, eigenvectors = True)  # get eigendecomposition
evals = evals[:, 0]                                # get real part of (real) eigenvalues

# rebuild original matrix
mchk = torch.matmul (evecs, torch.matmul (torch.diag (evals), torch.inverse (evecs)))

mchk - m                                           # check decomposition

evpow = evals**(-1/2)                              # raise eigenvalues to fractional power

# build exponentiated matrix from exponentiated eigenvalues
mpow = torch.matmul (evecs, torch.matmul (torch.diag (evpow), torch.inverse (evecs)))

mpow - mres                                        # check result

Here is the output:
>>> import torch
>>> print (torch.__version__)
0.3.0b0+591e73e
>>>
>>> m = torch.FloatTensor([[.5,.5],[.7,.9]])           # original matrix
>>>
>>> # desired result
... mres = torch.FloatTensor ([[ 2.69776664, -1.10907208], [-1.55270001,  1.81051025]])
>>>
>>> evals, evecs = torch.eig (m, eigenvectors = True)  # get eigendecomposition
>>> evals = evals[:, 0]                                # get real part of (real) eigenvalues
>>>
>>> # rebuild original matrix
... mchk = torch.matmul (evecs, torch.matmul (torch.diag (evals), torch.inverse (evecs)))
>>>
>>> mchk - m                                           # check decomposition

1.00000e-07 *
 -0.5960  0.0000
 -0.5960  1.1921
[torch.FloatTensor of size 2x2]

>>>
>>> evpow = evals**(-1/2)                              # raise eigenvalues to fractional power
>>>
>>> # build exponentiated matrix from exponentiated eigenvalues
... mpow = torch.matmul (evecs, torch.matmul (torch.diag (evpow), torch.inverse (evecs)))
>>>
>>> mpow - mres                                        # check result

1.00000e-07 *
  4.7684  7.1526
 -2.3842 -7.1526
[torch.FloatTensor of size 2x2]

You can see that this scheme recovers your Matlab result (actually
Mr. mathematics’s scipy result, because he printed it out with greater
precision).
This will work mathematically for positive semi-definite (square)
matrices (although numerically you will probably want your smallest
eigenvalue to be enough larger than zero that its computation doesn’t
yield a negative value due to numerical error).



 mathematics:

scipy will work without changing torch tensor to numpy


However, if you want to use autograd to calculate gradients (for, e.g.,
backpropagation), performing the calculation in scipy won’t work
(unless you write your own .backward() function).
Because the torch.eig() approach works entirely with pytorch tensor
functions, you get autograd / gradients “for free.”
Good luck.
K. Frank