# Ipopt solves large-scale nonlinear problems

**URL:** https://discourse.julialang.org/t/ipopt-solves-large-scale-nonlinear-problems/98831
**Category:** Optimization (Mathematical)
**Tags:** nlp
**Created:** [May 14, 2023, 11:17am UTC](https://discourse.julialang.org/t/ipopt-solves-large-scale-nonlinear-problems/98831 "2023-05-14T11:17:28Z")
**Posts on this page:** 1
**Showing post:** 3

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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: [May 14, 2023, 2:15pm UTC](https://discourse.julialang.org/t/ipopt-solves-large-scale-nonlinear-problems/98831/3 "2023-05-14T14:15:38Z")

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I think you will find this post interesting,

> [@AC Optimal Power Flow in Various Nonlinear Optimization Frameworks](https://discourse.julialang.org/t/ac-optimal-power-flow-in-various-nonlinear-optimization-frameworks/78486/2):
>
> You forgot to add constraints slight_smile I will look into the Zygote issue. Nonconvex uses l-BFGS Ipopt by default which explains the Hessian.

And this talk,

[![](https://global.discourse-cdn.com/julialang/original/3X/d/5/d5a2c72743cbbed33e3cf52df9e543495bab8d0c.jpeg "Benchmarking Nonlinear Optimization with AC Optimal Power Flow | Carleton Coffrin | JuliaCon 2022") ](https://www.youtube.com/watch?v=tvBNQcuU-hY)

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