[Numpy-discussion] Array of matrices - Inverse and dot

[Tom K.]

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.


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