On 5/22/07, Martin Albrecht <[EMAIL PROTECTED]> wrote:
>
> > There might be something simpler though.
>
> How about if we overload Python's built-in map function such that it also
> works for matrices and falls back to the built-in one for the rest? Or if
> that is an issue because it might be confusing for Python users, we could
> call it smap/sage_map?
>
> So for example:
> sage: A = random_matrix(GF(127),2,2)
> sage: B = smap(lambda x: x + 1, A)
>
> Thoughts?
Something like this is a really good idea. I don't think it's the
best solution for conjugate transpose (see below). Here
is a slight alternative to your proposal. What do people think?
sage: A = random_matrix(GF(127),2,2)
sage: B = A.map( lambda x : x+1 )
sage: B = A.apply( lambda x : x+1 )
Of course the input to map or apply is any callable function.
-----
In this particular case -- e.g., conjugate transpose, the only reasonable
answer is that this has to be implemented as a compiled sagex function
for that type of matrix. I suspect Josh Kantor will just post a patch,
since he maintains that code. I.e., there should be a method
def conjugate_transpose(self):
do some sagex stuff
Any iteration, etc., type thing will be way too slow.
--
William Stein
Associate Professor of Mathematics
University of Washington
http://www.williamstein.org
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