# OptimizationMOI Ipopt violating inequality constraint

**URL:** <https://discourse.julialang.org/t/optimizationmoi-ipopt-violating-inequality-constraint/92608>\
**Category:** Optimization (Mathematical)\
**Tags:** optimization, ipopt\
**Created:** [January 6, 2023, 4:36pm UTC](https://discourse.julialang.org/t/optimizationmoi-ipopt-violating-inequality-constraint/92608 "2023-01-06T16:36:48Z")\
**Posts on this page:** 1\
**Showing post:** 14

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**Author:** ![ccoffrin](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ccoffrin/32/400_2.png) [@ccoffrin](https://discourse.julialang.org/u/ccoffrin)\
**Post date:** [January 7, 2023, 4:58pm UTC](https://discourse.julialang.org/t/optimizationmoi-ipopt-violating-inequality-constraint/92608/14 "2023-01-07T16:58:46Z")

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> [@ForceBru](#):
>
> My issue with JuMP is that I’d like my functions to be written in Julia, not a modelling language. Not exactly sure why that is, but I like when my functions don’t depend on anything - they’re just ordinary functions.

If this is your primary desire and you want to use a backend like Ipopt as a solver, [NonConvex.jl](https://github.com/JuliaNonconvex/Nonconvex.jl) came to my mind as the Julia framework that has similar design goals. I reviewed a bunch of them here, [Survey of Non-Linear Optimization Modeling Layers in Julia](https://discourse.julialang.org/t/survey-of-non-linear-optimization-modeling-layers-in-julia/78168)

That said, if you want maximum performance and scalability you might want to reconsider the pros and cons of using JuMP. See this discussion, [AC Optimal Power Flow in Various Nonlinear Optimization Frameworks - #35 by ccoffrin](https://discourse.julialang.org/t/ac-optimal-power-flow-in-various-nonlinear-optimization-frameworks/78486/35)

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