"Dr. Dmitry Gokhman" firstname.lastname@example.org writes:
I have a rank three n-dim tensor A. For two of the axes I want to perform v^t A v (a quadratic form with scalar output, where v is a vector). The final output should be a vector. I also need to compute the derivative of this with respect to v. This involves symmetrizing and matrix-vector multiplication (2 sym(A)v using two axes of A only, which gives a vector) with the final result being a matrix.
Whenever dealing with somewhat more complex operations of this time, I think it's best to go back to the basic NumPy functionality rather then figuring out if there happens to be a function that magically does it. In this case, assuming that the first axis of A is the one that is not summed over:
sum( sum(A*v[NewAxis, NewAxis, :], -1) * v[NewAxis, :], -1)
The idea is to align v with one of the dimensions of A, then multiply elementwise and sum over the common axis. Note that the first (inner) sum leaves a rank-2 array, so for the second multiplication v gets extended to rank-2 only.
PS One more dumb question: I just installed the ScientificPython-2.4.1 rpm on my reincarnated Mandrake linux machine running python2.2. Do I need to do something to configure it? My scripts aren't finding things (e.g. indexing.py).
If you took the binary RPM from my site, they might not work correctly with Mandrake, as they were made for RedHat. The source RPM should work with all RPM-based Linux distributions. There is nothing that needs configuring with an RPM.
Also note that Scientific is a package, so the correct way to import the indexing module is