minimization fmincon with ode

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AM
AM on 8 Jan 2019
Commented: Torsten on 8 Jan 2019
Hello, I am using fmincon to fit my model to data.
I have a set of differential equations that give me the model results:
dy/dt=f(x,y,t)
where y are the points I want to compare to experimental points, x are my decision variables and t is time.
My objective function objfun is the sum of the squares of the residuals (y-data)^2
I profiled my code and I saw that it spends the most time solving the ode. I wanted to know if there is a way, numerically, to use my set of ODE dy/dt to determine the gradient dobjfun/dx so I can give it to fmincon beforehand instead of it using finite differences to determine it.
Thank you,

Accepted Answer

Torsten
Torsten on 8 Jan 2019
Edited: Torsten on 8 Jan 2019
To get dobjectfun/dx numerically, you had to solve even more ODEs:
Section: Use a Gradient Evaluation Function.
I wouldn't advice you to do so.
Best wishes
Torsten.
  2 Comments
Torsten
Torsten on 8 Jan 2019
You can use derivative-free optimization methods, e.g. fminsearch.
For methods that need the Jacobian, nothing can be done.

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