[Ipopt] optimization with experimental data

[Stefan Vigerske]

Hi,

you can store your parameters as data in your derivation of the TNLP class.
Then you can pass them in either in the constructor or just before
calling OptimizeTNLP.

Stefan

pierre-lin.pommier at fr.michelin.com wrote:
> Hello,
> 
> I'm looking for to solve an optimization problem. I have some experimental 
> data and I want to minimize the difference (L2-norm) between the model and 
> the measures.
> 
> I have some difficulties to link Ipopt solver : how can I take into 
> account these experimental data in functions "eval_f", "eval_grad_f", 
> "eval_g", "eval_jac_g" and "eval_h" ?
> 
> You gave an example to minimize a function without experimental data.
> 
> For instance, if my function is
> 
> y(p1, p2, x) = p1 * exp(p2 * x)
> 
> where p1 and p2 are my 2 parameters, x is my variable (the temperature for 
> instance). So at each componant of x, called x_i, I must have a new point 
> y(p1, p2, x_i)
> 
> the jacobian of my function is
> 
> exp(p2 * x)
> p2 * p1 * exp(p2 * x)
> 
> for each x.
> 
> How can I program functions "eval_f", "eval_grad_f", "eval_g", 
> "eval_jac_g" and "eval_h" ?
> 
> Thank you very much.
> 
> Sincerly,
> 
> Pierre-Lin Pommier
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-- 
Stefan Vigerske
Humboldt University Berlin, Numerical Mathematics
http://www.math.hu-berlin.de/~stefan