Hi,

I have a model from which I estimate some parameters, and the parameters are “data” to my second model. I want to be able to optimize the parameters of the first model in a way that the second model fits well. Since `optimize`

just takes functions (cost functions) I cannot write both optimizations right after another in one function. Is there any specific package in Julia for this kind of optimization?

```
function DEmodel()
....
end
function DEsolve()
...
end
function second_model()
... [it is just a curve fit, not a DE model]
end
function residuals_to_firstModel()
DEmodel() - data
end
r = optimize(residuals_to_firstModel, initial, LevenbergMarquardt())
parameters = r.minimizer
function residuals_to_secondModel()
second_model() - parameters
end
p = optimize(residuals_to_secondModel, initial, LevenbergMarquardt())
```

I don’t know how to make a loop so that the second model’s goodness of fit, feedbacks to the first model to adjust the parameters.

I appreciate any ideas!