Hi, I’m trying to implement a model with constraints based on an NCP-function (continuous & differentiable) and put it into an NLP solver such as IPOPT:
@constraint(m, f(x,y) <= 0)
where
f(x,y) := xy if x+y >= 0
-0.5(x^2 + y^2 ) if x+y < 0
Can I get some hints on how to implement such constraints on JuMP if it’s possible?
You could write a normal Julia function for f, and then call JuMP.register and @NLconstraint, see Nonlinear Modeling — JuMP -- Julia for Mathematical Programming 0.13 documentation
@constraint(model, ifelse(x + y >= 0, x*y, -0.5*(x^2 + y^2)) <= 0) is another way. If you can avoid user-defined functions, it’s better to do so because registering multivariate user-defined functions disables hessian computations.
This works great. Thank you very much!