[SciPy-user] using (c)vode [was: odeint rtol and atol default values)]

[Doug Latornell]

I believe you're correct re: Octave and lsode.

I tracked through documentation to the sources pages for vode, and the other
algorithms, read them and decided that vode was well suited to my problem.
Can't recall the exact issues that convinced me now, though I think its
ability to adapt to stiff problems was one point.

Accuracy was equivalent to Octave/lsode, but I don't have a closed form case
to compare to.  I'm modelling production process data with *lots* of sources
of deviation.  Both integrators give me acceptable predictions (for my
purposes).

Doug


On 6/27/06, Steve Schmerler <elcorto at gmx.net> wrote:
>
> Doug Latornell wrote:
> > Hi Steve;
> >
> > I've been happily using the ode class with the vode integrator for a few
> > months now.  I rewrote a model from Octave into Python/NumPy/SciPy.
> > Agreement between the Python/ode/vode code and the Octave one was good.
> > The model is substantially faster in SciPy than it was in Octave, but
> > there are a lot of factors that changed (processor, OS, etc.).
>
> Did you have a special reason for chosing vode/ode over lsoda/odeint
> (speed, accuracy, ...)?
>
> If I'm right, Octave uses lsode
> (
> http://www.gnu.org/software/octave/doc/interpreter/Ordinary-Differential-Equations.html#Ordinary-Differential-Equations
> )
>
> cheers,
> steve
>
> --
> Random number generation is the art of producing pure gibberish as
> quickly as possible.
>
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