# NLopt - Same complex clalculations in objetive function and nonlinear constraints

**URL:** <https://discourse.julialang.org/t/nlopt-same-complex-clalculations-in-objetive-function-and-nonlinear-constraints/41677>\
**Category:** Performance\
**Created:** [June 18, 2020, 4:15pm UTC](https://discourse.julialang.org/t/nlopt-same-complex-clalculations-in-objetive-function-and-nonlinear-constraints/41677 "2020-06-18T16:15:26Z")\
**Posts on this page:** 1\
**Showing post:** 17

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**Author:** ![pierre-haessig](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pierre-haessig/32/217129_2.png) [@pierre-haessig](https://discourse.julialang.org/u/pierre-haessig)\
**Post date:** [July 19, 2021, 1:20pm UTC](https://discourse.julialang.org/t/nlopt-same-complex-clalculations-in-objetive-function-and-nonlinear-constraints/41677/17 "2021-07-19T13:20:40Z")

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Thanks for the feedback! I had seen your [post announcing Nonconvex](https://discourse.julialang.org/t/ann-nonconvex-jl-a-toolbox-for-ad-based-constrained-non-convex-optimization/59362) (and the interesting subsequent discussion), but I’ve not taken the time to dig into using it. About the same days, somebody else pointed me to [Julia Smooth Optimizers](https://juliasmoothoptimizers.github.io/): too many things to read!

I had not thought about moving the objective as a constraint. This indeed removes the need for caching since NLopt supports vector constraints. However, I suspect this requires further investigation to see how each algorithm reacts. Indeed, this is a standard trick in Linear or Conic Programming (e.g. to transform a QP into SOCP if I remember well), but I’m not sure that all NLP solvers would react well to this apparent absence of objective.

About Nonconvex, I was about to say that in my usecase I need ForwardDiff(\*), but I see [deep in the doc](https://mohamed82008.github.io/Nonconvex.jl/stable/gradients/#Using-other-automatic-differentiation-backends) that using ForwardDiff instead of Zygote is possible. Perhaps you can adapt your Getting started (in item _“The use of Zygote.jl for automatic differentiation (AD) of the objective and constraint functions. Specifying analytic gradients is also possible…”_) to reflect this. This also reminds me that I have to investigate ChainRules as well…

(\*) or more truely: I’m pretty happy by ForwardDiff, very unsatisfied by ReverseDiff and I’ve yet to investigate more AD packages like Zygote. I’ve less than ten inputs and outputs.

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