[Numpy-discussion] Numpy Benchmarking

[Jon Wright]

>>This strikes me as a little bit odd. Why not just provide the best-performing 
>>function to both SciPy and NumPy? Would NumPy be more difficult to install
>>if the SciPy algorithm for inv() was incorporated?
>>    
>>
Having spent a few days recently trying out various different 
eigenvector routines in Lapack I would have greatly appreciated having a 
choice of which one to use from without having to create my own 
wrappers, compiling atlas and lapack under windows (ouch). I noted that 
Numeric (24.2) seemed to be converting Float32 to double meaning my 
problem no longer fits in memory, which was the motivation for the work. 
Poking around in the svn of numpy.linalg appears to find the same lapack 
routine as Numeric (dsyevd). Perhaps I miss something in the code logic? 
The divide and conquer (*evd) uses more memory than the (*ev), as well 
as a factor of 2 for float/double, hence my problem, and the reason why 
"best performing" is a hard choice. I thought matlab has a look at the 
matrix dimensions and problem before deciding what to do (eg: the \ 
operator).

Jon