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.
I'm not exactly sure what you mean by a rank three n-dim tensor (in Numeric the rank is the number of dimensions in the array). But, I think you can accomplish what you desire in two different ways: 1) If A has N dimensions and you want to perform the reduction over axis I'll label a1 and a2 (that is a1 is the axis for the v^t*A sum while a2 is the axis for the A*v sum). Then I think this should work (if a2 > a1). ex1 = [Numeric.NewAxis]*(N-1) ex1[a1] = slice(None) ex2 = [Numeric.NewAxis]*N ex2[a2] = slice(None) Nar = Numeric.add.reduce result = Nar(v[ex1]*Nar(A*v[ex2],axis=a2),axis=a1) # I think you need a recent version of Numeric for the axis keyword # to be defined here. Otherwise, just pass a2 and a1 as arguments # without the keyword. 2) Using dot (requires transposing the matrix) as dot only operates over certain dimensions --- I would not try this. -Travis Oliphant