# 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:** 10

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**Author:** ![stevengj](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevengj/32/71_2.png) [@stevengj](https://discourse.julialang.org/u/stevengj)\
**Post date:** [June 30, 2020, 12:37pm UTC](https://discourse.julialang.org/t/nlopt-same-complex-clalculations-in-objetive-function-and-nonlinear-constraints/41677/10 "2020-06-30T12:37:46Z")

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> [@mzaffalon](#):
>
> The risk is to sample a point that does not belong to the objective function domain, e.g. a negative number when `f(x) = sqrt(x)` .

NLopt guarantees that the function will never be evaluated outside the feasible set for box constraints, but not for arbitrary nonlinear constraints. (This is true for most nonlinear-programming algorithms that I’m aware of.) So if you have \sqrt{x}, you should use a box constraint x \ge 0 and you will be fine.

(For general constraints, this may not even be possible, e.g. you may have a pair of inequality constraints h(x) \le 0 and h(x) \ge 0, which means the feasible set is in a nonlinear manifold h(x)=0, and it is not practical to stay exactly on such a manifold in general.)

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