I have a proposal about M * A, where M is a Sage matrix and A a NumPy array. The current behaviour appears to be the Kronecker product; I'm guessing that this is just be a side-effect of Python applying element-wise __mul__ (if it is intentional and relied upon, this proposal got harder).
I don't know if you do code in custom behaviour with non-Sage objects, but anyway: The suggestion is to have M (the Sage matrix) act as a linear transformation on "stacked vectors", where the first axis of the array A is used for right-mul and the last axis of A for left-mul. Example: sage: M=random_matrix(RDF, 3, 10) sage: A = numpy.ones((10, 23, 34), dtype=numpy.double) sage: type(M*A) <type 'numpy.ndarray'> sage: (M*A).shape (3, 23, 34) sage: (A*random_matrix(RDF, 34, 5)).shape (10, 23, 5) This maintains (S.transpose() * M.T).T = M*S, and is the matrix product for 2-dimensional NumPy arrays. A doesn't represent anything in linear algebra, it just causes (for convenience) repeated application of the linear transformation to the rows/columns of A. I'd love to promise to implement it but I have no idea how hard it is to pull off, so I'll promise two days of work if this is accepted and somebody provides me with an attack plan. Dag Sverre -- To post to this group, send an email to sage-devel@googlegroups.com To unsubscribe from this group, send an email to sage-devel+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URL: http://www.sagemath.org
