[SciPy-dev] genetic algorithm, number theory, filterdesign,zerofinding
Chuck.Harris at sdl.usu.edu
Thu Apr 11 12:16:38 CDT 2002
I've taken a look at the 1-D solvers in CVS. A few comments
1. fixed_point computes incorrect roots
2. they make assumptions on the arguments that are not enforced
3. none take into account the varying granularity of floating point
4. newton should probably check for blowup, as this is not uncommon
5. all the python routines suffer from large overheads. We should go for C.
That said, the routines a pretty quick. I think a good starting point would be to take the simplest and put them in C. I expect this would at least halve the execution time for easy to compute functions.
For newton, I don't think the option of using computed derivatives is worth including. There is a slightly higher order of convergence (2 vs 1.4), but this is likely to be swamped in function evaluation time, especially if the function is python and the routine is C.
> -----Original Message-----
> From: Travis Oliphant [mailto:oliphant.travis at ieee.org]
> Sent: Wednesday, April 10, 2002 5:51 PM
> To: scipy-dev at scipy.org
> Subject: RE: [SciPy-dev] genetic algorithm, number theory,
> On Wed, 2002-04-10 at 13:06, Chuck Harris wrote:
> > Hi,
> > I've decided to start with the zero finders, as fsolve is
> in general a poor choice in one dimension. This brings up the
> problem of validating function arguments and I am searching
> for guidelines.
> There are other zero-finders in the CVS version of SciPy, besides
> We could include a check for 1-D functions if your assessment
> of fsolve
> is substantiated.
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