Analyzing various values for c with respect to the density, showed that c density = 1000 induce the aforementioned desired behavior. In figure the loss function values, using the NLP loss function, for a hypothetical and unsuitable c density = 10 and a suitable c density = 1000 are shown (solid and dashed orange lines). The gray line shows the corresponding original loss function values (see equation ( )); Note, that those values can exceed 1, although this cannot be seen in the figure. Higher c density values result in a narrower area where z * < 1, and a steeper slope results in the transition areas. When c is too large, high loss function values can overlap with the range of acceptable errors (blue area), prohibiting the optimization algorithm from terminating. However, when c is too low, unwanted optimization results are assigned a low loss function value, erroneously causing the optimization to finish. For the optimization performed herein, the density sensitivity hyperparameter is set to c density = 1000. For that value, a well-balanced configuration between the aforementioned aspects is achieved. The same scheme and rules need to be applied when determining c energy . In figure