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docs/src/tutorials/constraints.md

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@@ -43,6 +43,7 @@ Let's check that the constraints are satisfied and the objective is lower than a
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res = zeros(2)
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cons(res, sol.u, _p)
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prob.f(sol.u, _p)
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nothing # hide
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```
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We can also use the Ipopt library with the OptimizationMOI package.
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cons(res, sol.u, _p)
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println(res)
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prob.f(sol.u, _p)
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nothing # hide
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```
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We can also use ModelingToolkit as our AD backend and generate symbolic derivatives and expression graph for the objective and constraints.
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Let's modify the bounds to use the function as an equality constraint. The constraint now becomes -
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```
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```math
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\begin{align}
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x_1^2 + x_2^2 = 1.0 \\

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