> > On Thu, Oct 26, 2017 at 12:11 PM, Daniele Nicolodi <dani...@grinta.net> > > wrote: > > > >> is there a better way to write the dot product between a stack of > >> matrices? In my case I need to compute > >> > >> y = A.T @ inv(B) @ A > >> > >> with A a 3x1 matrix and B a 3x3 matrix, N times, with N in the few > >> hundred thousands range. I thus "vectorize" the thing using stack of > >> matrices, so that A is a Nx3x1 matrix and B is Nx3x3 and I can write: > >> > >> y = np.matmul(np.transpose(A, (0, 2, 1)), np.matmul(inv(B), A)) > >>
If you only ever multiply your matrix inverse by a single vector then you may also wish to consider np.linalg.solve(B,A) which usually has a better prefactor (although for 3x3 it's pretty marginal, your hardware may vary). Peter _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
