Debugging in Ipopt without JuMP

[jd-foster]

Welcome. You may be interested in this past post:

Survey of Non-Linear Optimization Modeling Layers in Julia

I am doing a survey of the NLP modeling layers in Julia to see which might be applicable to the kinds of problems that I regularly solve. I have the following basic requirements of the modeling layer, Support for non-convex functions for the objective Support for a system non-convex equality and inequality constraint functions (the equality constraints usually cannot be expressed explicitly as a manifold) Support for polynomial and transcendental functions (e.g. x^2*y^3,sin(x)) Some kind of a…

I’m moving your post to the more relevant Optimization category where you might get more expert attention.

@Itzahack23

Actually, when I use JuMP + Ipopt, the problem is not solved correctly, and I found that the number of nonzero elements in Lagrangian Hessian is much larger than the number theoretically derived.

If you can share a reproducible example, this would be of interest to JuMP developers to potentially identify an issue.