# Ipopt does NOT memorize a best solution found so far

**URL:** <https://discourse.julialang.org/t/ipopt-does-not-memorize-a-best-solution-found-so-far/127244>\
**Category:** Optimization (Mathematical)\
**Tags:** ipopt\
**Created:** [March 21, 2025, 9:57pm UTC](https://discourse.julialang.org/t/ipopt-does-not-memorize-a-best-solution-found-so-far/127244 "2025-03-21T21:57:20Z")\
**Posts on this page:** 1\
**Showing post:** 5

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**Author:** ![WalterMadelim](https://avatars.discourse-cdn.com/v4/letter/w/3e96dc/32.png) [@WalterMadelim](https://discourse.julialang.org/u/WalterMadelim)\
**Post date:** [April 22, 2025, 8:29am UTC](https://discourse.julialang.org/t/ipopt-does-not-memorize-a-best-solution-found-so-far/127244/5 "2025-04-22T08:29:16Z")

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Thanks for your information.  
I actually did use second-order method, I thought. If the objective function is the primal function, then I take its gradient, and then used the Jacobian of that gradient.

> [@apo383](#):
>
> unlikely to scale well to higher dimensions

This is probably true. But the reason might be that I was not using Sparse techniques.  
Do you have any idea about this?  
I guess I need to invoke some autodiff packages, which works well in conjunction with Sparse techniques. Yet I haven’t figure this out. 🙂  
Here is a related link, I’m now reading [Advanced tutorial · DifferentiationInterface.jl](https://juliadiff.org/DifferentiationInterface.jl/DifferentiationInterface/stable/tutorials/advanced/#Sparsity).

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