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!