This is related to a question I posted earlier.
Suppose I have array A with dimensions n x m x l and array x with
dimensions m x l. Interpret this as an array of l nxm matrices and
and array of l m dimensional vectors. I wish to compute the matrix-
vector product A[:,:,k] x[:,k] for each k = 0,... l -1. I discovered
that I could accomplish this with the command
np.diagonal(np.tensordot(A, k, axes=(1,0)), axis1= 1, axis2 = 2)
The tensordot command gives me
A_{ijk}x_{jl} = C_{ikl}
And the diagonal command grabs the entries in array C where k=l.
Is this the "optimal" way to make this calculation in numpy? It
certainly makes for nice, clean code, but is it the fastest I can get?
-gideon
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