I am fitting experimental data with well-knowed mathematical model, minimizing the root mean square error (my objective function) with fmincon command. I noticed that Octave does not converge at the optimal solution at the first shot (I know the expected values of the model parameters thanks to an optimization in Excel and in Matlab).
I have overcome the problem running a large number of simulations (e.g. 100+) with random initial conditions but included in the boundary range and extraprolating the solution with the best goodness (lowest fval). So, I obtain the expected results.
However, it is not clear to me why the fmincon command in Octave does not converge at the optimum values with only one attempt, while the same script runned in Matlab does.
According to this, I report the result of the 100 simulations for the two cases and as you can easily see in the following graph, the fval value for the code run in Matlab always remains constant (each simulation converges) while Octave presents many simulations with results far from the optimal point.
Similar problem was also reported here, but there was no solution
- OS: Windows 10 Pro
- Octave version: Version 6.1.0
- Installation method: e.g. Downloaded and installed “octave-6.1.0-w64-installer.exe” from https://www.octave.org/download.html