Hello everyone,

I’m trying to model the constraint defined as the following piecewise function:

in a model:

```
def _function(x):
condlist= [(x>=x1) & (x<x2),
(x>=x2) & (x<x3),
(x>=x3) & (x<=x4)]
funclist= [lambda x: k1 * (x-x1) - high,
lambda x: CHF_base,
lambda x: k3 * (x-x3) + base]
return np.piecewise(x, condlist, funclist)
```

The deterministic function outside the model seems to give expected results. But the implementation in the model (* deflevel*) in NLP form (using the sigmoid function) does not give the expected results (the variable ‘

**leve**l’ should evolve according to ‘

**net_l**’). The other hypothesis is that specific options are required for the solver.

Model link: https://github.com/Lere16/Sample.git

Any help you can give me would be a great help.

Thank you so much.