[SciPy-user] Sundials

Nils Wagner nwagner@iam.uni-stuttgart...
Tue May 19 11:47:01 CDT 2009

On Tue, 19 May 2009 09:48:47 +0200
  Sebastian Walter <sebastian.walter@gmail.com> wrote:
> Hello Nils, hello Radu,
> This package is basically what I could use for my 
>research (optimal
> experimental design with underlying DAE dynamics).
> Scipy is really missing integrators that also provide 
> nowadays, simulation is regarded as predictive and 
> it is typical, that one wishes to do optimization with 
>ODE,DAE,PDE constraints.
> In our research, we have an objective function that 
>looks in the most
> simple case like this:
> Phi = trace(inv(dot(J.T,J)))
> where J(q) = dF/dp and F is a function of the solution 
>of a DAE at
> measurment times [t_1, t_2, ..., t_M].
> i.e. the optimization problem is
> min_q Phi(q)
> I'd like to try SUNDIALS to do some optimal experimental 
>design. But
> I'm  not sure if your package supports all we need:
> 1) Actually, we need second, third and higher order 
> preferably in a combination of adoint mode and forward 
>    Can SUNDIALS do that?
> 2) The forward mode is directional derivatives. Is it 
>possible to do
> userspecified directions? I.e. we want
> dot(dPhi/d(q_1,q_2) ,[1,2]), i.e. the derivative of Phi 
>w.r.t. two
> variables q_1 and q_2 in direction [1,2].
> 3) Doing a reverse sweep starting from the objective 
>function, we need
> to pass in adjoint directions at the meassurment times 
>[t_1, t_2, ...,
> t_M].
>    Does SUNDIALS support that? Or is it possible to 
>interrupt the
> integration at each measurement time, hand in a new 
>adjoint direction
> and then perform a warm start?
> Sebastian

Hi Sebastian,

I guess Radu is not subscribed to this list.

You probably know about

I suggest that you ask the maintaner of pysundials.



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