[SciPy-User] Sparse jacobian in scipy ODE solver

Per Nielsen evilper@gmail....
Sat Jan 21 07:14:49 CST 2012

Hi all

I am working with large linear system 1st order ODE systems of the form

dydt = M * y

where M is very sparse complex valued matrix. I currently use the ode class
from scipy.integrator like:

from scipy.integrate import ode
r = ode(f)
r.set_integrator('zvode', method='adams')

and calculate f = dydt as M.dot(y). I would like to make use of the
jacobian of dydt, which for my linear problem is simply M, to speed up my
simulations. However, when I supply a sparse matrix as the jacobian I get
the following error (see attached script):

rv_cb_arr is NULL
Call-back cb_jac_in_zvode__user__routines failed.
TypeError                                 Traceback (most recent call last)
/Users/per/Dropbox/DTU/python/tryouts/ode_jac_test.py in <module>()
     62     while r.successful() and r.t < t1:
---> 63         r.integrate(r.t + dt)
     64         ts.append(r.t)
     65         ys.append(r.y)

in integrate(self, t, step, relax)
    324         self.y,self.t = mth(self.f,self.jac or (lambda :None),
    325                             self.y,self.t,t,
--> 326                             self.f_params,self.jac_params)
    327         return self.y

in run(self, *args)
    685     def run(self,*args):
--> 686         y1,t,istate =
    687         if istate < 0:
    688             warnings.warn('zvode: ' +

TypeError: a float is required

It seems that the jacobian can not be sparse. But is this a fundamental
problem or could one somehow work around it and still use ode from scipy?

Any help or comments would be appreciated.


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