[SciPy-User] Speeding up odeint for big problems
Mon Sep 27 01:02:37 CDT 2010
I'm using odeint to solve a system of PDEs using the method of lines. This
works well for problems in one dimension where there are about ~10^2 coupled
ODEs. In 2D, I have ~10^4, and speed is a major issue. Does anyone have
any general tips on ways to optimally use odeint to solve large problems
using method of lines? Some useful info/questions about my problem:
--The Jacobian is dense, even if I use finite differencing for the spatial
--Using something like PyDSTool might be difficult to implement because I
need many of SciPy's handy functions like FFTs, etc.
--I'm worried I might be wasting time by passing a large object as an
argument to the RHS function. This is out of convenience because it
contains useful information about the problem. Does odeint do type
conversions on all attributes/methods of the input argument, or only on what
is needed? Would paring down the input save time?
Any input is greatly appreciated. I'd really like to avoid having to code
this up in C.
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