Jean-Baptiste Rudant wrote:

I would like to operate in an easy and efficient way (without python loop) with arrays of matrices.

Suppose a and b are some arrays of N1*N2 matrices of size 3*3, I would like to calculate inv_a and dot_ab, which would be arrays of N1*N2 (3*3)-matrices, such as :

inv_a[i, j] = np.linalg.inv(a[i, j]) dot_ab[i, j] = np.dot(a[i, j], b[i, j])

(where a and b could be : N1 = 5 N2 = 6 a = np.random((N1, N2, 3, 3) b = np.random((N1, N2, 3, 3) ).

Here's a one-liner: numpy.array(map(numpy.dot, a, b)) that works for matrix multiply if a, b are (n, 3, 3). Could do the same for linalg.inv. This comes up a lot in OFDM MIMO systems so I wrote C++ code for complex matrix multiply (arbitrary size), 2x2 complex inverse and 2x2 complex matrix singular values, and then swigged it. I know a colleague at work has extended this work to arbitrary size inverse. I wish I could share the code but it is something developed for my company which has fairly strict policies about posting these things (not worth the red-tape)... I vote "+1" for such features in Numpy. I haven't looked too much "under the hood" of numpy so I am not sure how you would do it or how hard it would be. Regards, Tom K. -- View this message in context: http://www.nabble.com/Array-of-matrices---Inverse-and-dot-tp21666949p2167062... Sent from the Numpy-discussion mailing list archive at Nabble.com.