[SciPy-user] using (c)vode [was: odeint rtol and atol default values)]
doug-scipy at sadahome.ca
Thu Jun 29 17:30:51 CDT 2006
It was the abstract of this paper
http://www.llnl.gov/CASC/nsde/pubs/207532.pdf that convinced me to give the
vode algorithm a try.
On 6/27/06, Doug Latornell <doug-scipy at sadahome.ca> wrote:
> 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).
> 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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