# Debugging in Ipopt without JuMP

**URL:** <https://discourse.julialang.org/t/debugging-in-ipopt-without-jump/83384>\
**Category:** Optimization (Mathematical)\
**Tags:** ipopt\
**Created:** [June 27, 2022, 7:16am UTC](https://discourse.julialang.org/t/debugging-in-ipopt-without-jump/83384 "2022-06-27T07:16:12Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![jd-foster](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jd-foster/32/35824_2.png) [@jd-foster](https://discourse.julialang.org/u/jd-foster)\
**Post date:** [June 27, 2022, 10:37am UTC](https://discourse.julialang.org/t/debugging-in-ipopt-without-jump/83384/2 "2022-06-27T10:37:58Z")

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Welcome. You may be interested in this past post:

> [@Survey of Non-Linear Optimization Modeling Layers in Julia](https://discourse.julialang.org/t/survey-of-non-linear-optimization-modeling-layers-in-julia/78168):
>
> 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.

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