# Ipopt's "local" optimum can be very substandard

**URL:** <https://discourse.julialang.org/t/ipopts-local-optimum-can-be-very-substandard/127217>\
**Category:** Optimization (Mathematical)\
**Tags:** question, jump, ipopt\
**Created:** [March 21, 2025, 11:05am UTC](https://discourse.julialang.org/t/ipopts-local-optimum-can-be-very-substandard/127217 "2025-03-21T11:05:28Z")\
**Posts on this page:** 1\
**Showing post:** 5

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**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [March 21, 2025, 11:14pm UTC](https://discourse.julialang.org/t/ipopts-local-optimum-can-be-very-substandard/127217/5 "2025-03-21T23:14:15Z")

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I have no other comments. This is expected behavior of Ipopt. If you violate the assumption that the problem is convex, there is no guarantee what stationary it will find.

> Do I have to explicitly pass gradient and Hessian info to Ipopt? or something else?

Nope. JuMP uses automatic differentiation to compute the various gradients and Hessian oracles.

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