Hi Karl,

Karl Rupp <r...@iue.tuwien.ac.at> writes:
> I consider Vector * Vector -> Matrix to be surprising or at least 
> somewhat non-intuitive. Following your operations, the most reasonable 
> definition would be
>
>  >    Matrix * Matrix -> Matrix (matrix product)
>  >    Vector * Vector -> Vector (element-wise product)
>  >    Scalar * Scalar -> Scalar (scalar product)
>
> so that operator* maps elements from the same 'space' back into the same 
> space again. I don't see any reason why Vector*Vector->Matrix should be 
> 'more suited' than Vector*Vector->Scalar, hence better to use inner() 
> and outer() in such case. If I'm not mistaken, dot() is just a forward 
> to inner() in NumPy, so this would be consistent. On the other hand, I 
> don't see any problem with leaving vector*vector undefined...

I quite like your dimensionality-preserving reasoning, so I updated it
according to your scheme above.

Also, thanks for the prod about spaces vs tabs -- I thought I'd spotted
that before, but it seemed I'd not configured my editor correctly..

I also have a couple of questions about operations on heterogeneous
matrix types. Principally, should I build infrastructure anticipating
(for instance) the product of a column-major and a row-major matrix? Or
a sparse matrix and a dense matrix? Or should users just convert their
matrices to homogeneous types? Also: do you intend to support operations
on sparse matrices in the scheduler? So much to do, so few hours in a
day...


T


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