[Numpy-discussion] untenable matrix behavior in SVN

Charles R Harris charlesr.harris@gmail....
Wed Apr 30 17:06:51 CDT 2008

On Tue, Apr 29, 2008 at 1:22 PM, Timothy Hochberg <tim.hochberg@ieee.org>

> Let me throw out a couple of more thoughts:
> First, there seems to be disagreement about what a row_vector and
> column_vector are (and even if they are sensible concepts, but let's leave
> that aside for moment). One school of thought is that they are
> one-dimensional objects that have some orientation (hence row/column). They
> correspond, more or less, to covariant and contravariant tensors, although I
> can never recall which is which.  The second view, which I suspect is
> influenced by MatLab and its ilk, is  that they are 2-dimensional 1xN and
> Nx1 arrays. It's my view that the pseudo tensor approach is more powerful,
> but it does require some metainformation be added to the array. This
> metadata can either take the form of making the different objects different
> classes, which leads to the matrix/row/column formulation, or adding some
> sort of tag to the array object (proposal #5, which so far lacks any
> detail).
> Second, most of the stuff that we have been discussing so far is primarily
> about notational convenience. However, there is matrix related stuff that is
> at best poorly supported now, namely operations on stacks of arrays (or
> vectors). As a concrete example, I at times need to work with stacks of
> small matrices. If I do the operations one by one, the overhead is
> prohibitive, however, most of that overhead can be avoided. For example, I
> rewrote some of the linalg routines to work on stacks of matrices and
> inverse is seven times faster for a 100x10x10 array (a stack of 100 10x10
> matrices) when operating on a stack than when operating on the matrices one
> at a time. This is a result of sharing the setup overhead, the C routines
> that called are the same in either case.

Some operations on stacks of small matrices are easy to get, for instance,
+,-,*,/, and matrix multiply. The last is the interesting one. If A and B
are stacks of matrices with the same number of dimensions with the matrices
stored in the last two indices, then

sum(A[...,:,:,newaxis]*B[...,newaxis,:,:], axis=-2)

is the matrix-wise multiplication of the two stacks. If B is replaced by a
stack of 1D vectors, x, it is even simpler:

sum(A[...,:,:]*x[...,newaxis,:], axis=-1)

This doesn't go through BLAS, but for large stacks of small matrices it
might be even faster than BLAS because BLAS is kinda slow for small

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