GPMixin: trivial constraints created when keep_soft_constraints=False
If a path goal with targets has a timestep for which both the target minimum and target maximum are equal to
np.Nan, when soft constraints are transformed into hard constraints the following type of constraint is created:
-\infty \leq f(x) \leq \infty.
These turns out to be problematic when using CPLEX. Indeed, CasADi passes the constraint
f(x) \leq \infty to CPLEX which leads to optimization issues when the barrier method is used.