# Is there a Symbolics.jl interface to Ipopt (or similar)?

**URL:** <https://discourse.julialang.org/t/is-there-a-symbolics-jl-interface-to-ipopt-or-similar/100149>\
**Category:** Modelling & Simulations\
**Tags:** nlp, ipopt, modelling\
**Created:** [June 10, 2023, 8:22pm UTC](https://discourse.julialang.org/t/is-there-a-symbolics-jl-interface-to-ipopt-or-similar/100149 "2023-06-10T20:22:51Z")\
**Posts on this page:** 1\
**Showing post:** 18

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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:** [June 14, 2023, 10:49pm UTC](https://discourse.julialang.org/t/is-there-a-symbolics-jl-interface-to-ipopt-or-similar/100149/18 "2023-06-14T22:49:05Z")

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> I always struggle in JuMP to model NLPs involving arrays, summations, polynomials, custom multi-argument functions etc

Yeah this is a known problem. For now you’d need to do some ugly hack like:

```Julia
using JuMP, Ipopt
begin
    Q = -0.8:0.4:0.8
    model = Model(Ipopt.Optimizer)
    @variable(model, -2 <= p[1:5] <= 2)
    @variable(model, -1 <= w <= 3)
    @variable(model, -1 <= q <= 3)
    @objective(model, Min, w)
    total = Dict(
        _q => @NLexpression(
            model, 
            sum(_p / sqrt(2π) * exp(-(i - _q)^2 / 2) for (i, _p) in enumerate(p))
        )
        for _q in Any[Q; q; 0.5]
    )
    l1 = Dict(
        _q => @NLexpression(model, 1 - total[_q] + 0.5 * total[0.5])
        for _q in Any[Q; q]
    )
    @NLconstraint(
        model, 
        [_q in Q], 
        w * (l1[q] - l1[_q]) + (1 - w) * (total[q] - 1) <= 0
    )
    optimize!(model)
end

```

We’re working on fixing this, but it’s not ready yet: [https://github.com/jump-dev/JuMP.jl/pull/3106#issuecomment-1592091623](https://github.com/jump-dev/JuMP.jl/pull/3106#issuecomment-1592091623)

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