# Unable to use lagrange to solve 2 varable functions in modeling toolkit

**URL:** <https://discourse.julialang.org/t/unable-to-use-lagrange-to-solve-2-varable-functions-in-modeling-toolkit/72043>\
**Category:** Optimization (Mathematical)\
**Created:** [November 25, 2021, 5:47am UTC](https://discourse.julialang.org/t/unable-to-use-lagrange-to-solve-2-varable-functions-in-modeling-toolkit/72043 "2021-11-25T05:47:13Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [November 25, 2021, 5:26pm UTC](https://discourse.julialang.org/t/unable-to-use-lagrange-to-solve-2-varable-functions-in-modeling-toolkit/72043/6 "2021-11-25T17:26:08Z")

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I’ll reiterate my suggestion to use JuMP instead:

> [@Advice on using ModelingToolkit to solve Lagrange Problems](https://discourse.julialang.org/t/advice-on-using-modelingtoolkit-to-solve-lagrange-problems/71473/25):
>
> julia\> using JuMP, Ipopt julia\> function solve() model = Model(Ipopt.Optimizer) @variable(model, x[1:2] \>= 0.001, start = 1) @NLobjective(model, Max, 16 \* log(x[1]) + 9 \* log(x[2])) @NLconstraint(model, x[1]^2 / 100 + x[2]^2 / 36 == 100) optimize!(model) @assert termination\_status(model) == LOCALLY\_SOLVED return value.(x) end solve (generic function with 1 method) julia\> solve() This is Ipopt version 3.13.4, ru…

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