Hello, 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)) which I guess could be also written (in Python 3.6 and later): y = np.transpose(A, (0, 2, 1)) @ inv(B) @ A and I obtain a Nx1x1 y matrix which I can collapse to the vector I need with np.squeeze(). However, the need for the second argument of np.transpose() seems odd to me, because all other functions handle transparently the matrix stacking. Am I missing something? Is there a more natural matrix arrangement that I could use to obtain the same results more naturally? Cheers, Daniele _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
