Here it was suggested to use mathematical transforms to impose hard constraints on search variables in the SLSQP algorithm in scipy.optimize.minimize. I assume more algorithms that allow constraints take them more as suggestions than hard facts, and it is not even just a python question at this point.
So in my case, I have a hard constraint where the individual search variables x0, x1, x2, x3 ... and their sum in particular combinations must be constrained to an interval, say 0..1. More specifically, the constraint is that the sum 1 - k*x0 +k*x1 -k*x2 -c0 +k*x3 -k*x4 -c1 ... should be in the interval 0 .. 1 for every step of the way. You can use sigmoid functions to transform the search space into an interval. But with many such functions in combinations, that becomes infeasible - right?
What is the proper way to impose hard limitations on simple summation combinations of search variables?