# 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:** 11

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**Author:** ![Tamas\_Papp](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/tamas_papp/32/25949_2.png) [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp)\
**Post date:** [June 30, 2020, 1:53pm UTC](https://discourse.julialang.org/t/nlopt-same-complex-clalculations-in-objetive-function-and-nonlinear-constraints/41677/11 "2020-06-30T13:53:13Z")

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> [@stevengj](#):
>
> This is true for most nonlinear-programming algorithms that I’m aware of.

I think you are right, but I am wondering if some simple heuristic could remedy this. Eg if the feasible set D is convex, x \in D, and y \notin D, one could find a z = \alpha x + (1 - \alpha) y \in D using bisection. (This is just a random idea, maybe it has been tried and discarded).

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