Hello everyone, I am working on the optimization of some model parameters in my simulation, which simulates the impact of communication on the attitude towards a specific topic. I want to optimize 17 parameters in a non-linear function to get a minimal error-value. Therefor I implemented two different Optimization-Algorithms in my Simulation. First I tried the Nelder-Mead Algorithm. But in this case I have the problem, that first my error-value increases, then decreases again and starts to stagnate on a non-satisfying value. Is this even possible for the Nelder-Mead method, that the error increases however I want to minimize it? Then I also tried a different Optimization-Algorithm, the Levenberg-Marquardt-Algorithm. Here the problem is that the changes in the parameters are too small, so that the optimization already stops after one iteration. Do you maybe have an idea about approaching this problem or do you know a different Optimization-Algorithm that could suit my problem?
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