Thu Jun 29 10:37:20 CDT 2006
approximation to the jacobian matrix and not too wide. There are very
good (fast and accurate) integrators in vode that even don't need
jacobian, but it again depends on the propertis of the ODE system.
My advice would be to try different integrators on a relatively small
problem (say few hundreds of equations) and tune the integrator parameters
for the best performance (it is usually a good idea to keep accuracy high
as for large problems smaller accuracy introduces quickly numerical errors
and the integration becomes very slow or does not converge at all).
And after that, run the integrator on a large problem.
On Thu, 7 Mar 2002, Travis Oliphant wrote:
> > > Hi,
> > > I am a new user of SciPy. It looks like a very convenient method to access
> > > powerful numerical codes written in other languages from Python.
> > >
> > > At this point, I have an application in which I need to solve many (hundreds
> > > or thousands) of coupled ode's. Is the 'odeint' functionality in SciPy up to
> > > this task? I believe the code uses lsod(e|a|ar|es), which should, in theory,
> > > be adequate. Are there any 'issues' associated with the wrapping of these
> > > routines in Python? Anyone have experience using odeint for solving large
> > > numbers of ode's?
> > >
> I've not tried it for such large scale problems (I've only gone to 4 or 5
> coupled ode's).
> As you say, the underlying code should handle it.
> The only question is how long will it take. Using Numeric arrays in your
> function to compute the derivative is definitely recommended, to save
> Why don't you give it a try?
> -Travis Oliphant
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> SciPy-user at scipy.net
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