Specifications

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The graph confirms that the vast majority of the improvement is obtained in a couple of iterations and the
final iteration contributes only by a minor, almost insignificant adjustment. Such iterations with insignificant
improvements occur due to the convergence criterion, i.e., the criterion that stops the optimization process.
The optimizer does not detect mathemtically that the objective function has an optimum value; it merely
detects that the changes of the found solution are small from one iteration to the next. Therefore, the
optimization process will always end with one (or more) steps with insignificant changes.
The optimal solution in the Model View looks like this:
Just above the Metab variable in the tree you can find the two independent variables, SaddleHeight and
SaddlePos, and they can be graphed the same way revealing that their convergence is less monotone over
the iterations. This is also quite usual for optimization processes.
An interesting way to investigate the convergence is to plot it in the variable/objective space rather than
over the iterations. This is what we need the window with the parameter study surface for. At the top of this
window you will find panels listing series and data to be plotted. Please right-click in the series window and