[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
>sensitivities:
> nowadays, simulation is regarded as predictive and
>therefore
> 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
>derivatives,
> preferably in a combination of adoint mode and forward
>mode.
> 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
http://pysundials.sourceforge.net/
http://sourceforge.net/projects/pysundials
I suggest that you ask the maintaner of pysundials.
Cheers,
Nils
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