[PYTHON MATRIX-SIG] linear algebra

[Ralph Paul]

On Mon, 19 Aug 1996 09:38:35 -0400  owner-matrix-sig@python.org wrote:
> Should these two transpositions be there?  This is obviously related to
> the different index order between NumPy and FORTRAN.  Still, I've always
> personally considered it a superficial difference that is best ignored.
> 
> If I remember by Linear Algebra correctly
> transpose(inverse(transpose(m))) == inverse(m).
> 
> However, I very rarely use any linear algebra functions in my code so
> I'm not the right person to be making that decision.  Opinions?

Hi,

I would like to comment on that. 
It probably does not make a big difference if stick with limited 
matrix- vector operations like : b=A*x <=> b=x'*A' but if go one level
lower I think it gets quite annoying to have to switch your indexing around.

Just to throw in a suggestion let me tell what the Ada95 standard does to 
work around this problem. In Ada95 the compiler has to have  a 
" pragma Convention (Fortran, Fortran_Matrix);" which changes the way 
an array is actually stored in memory.
For the programmer there is no change at all, the compiler takes care of all
necessary changes to make the array Fortran as well as Ada compatible.

Would this be possible in python without a serious perfomance overhead ?


Ralph Paul

	paul@aem.umn.edu
or	ralph@ifr.luftahrt.uni-stuttgart.de




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