# BoundaError with NLopt in JuMP with a user-defined function and its gradient

**URL:** <https://discourse.julialang.org/t/boundaerror-with-nlopt-in-jump-with-a-user-defined-function-and-its-gradient/99598>\
**Category:** Optimization (Mathematical)\
**Tags:** jump, nonlinear, nlopt\
**Created:** [May 30, 2023, 11:39am UTC](https://discourse.julialang.org/t/boundaerror-with-nlopt-in-jump-with-a-user-defined-function-and-its-gradient/99598 "2023-05-30T11:39:27Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![naouess](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/naouess/32/35619_2.png) [@naouess](https://discourse.julialang.org/u/naouess)\
**Post date:** [May 30, 2023, 11:39am UTC](https://discourse.julialang.org/t/boundaerror-with-nlopt-in-jump-with-a-user-defined-function-and-its-gradient/99598/1 "2023-05-30T11:39:27Z")

</div>

Hello,

I am solving an NLP in JuMP with several nonlinear user-defined functions as constraints. I provide the functions and their gradients in the following way:

```julia
for j in 1:24
	register(m, Symbol("myfunc_$j"), length(x), create_cons(j, inputs)[1], create_cons(j, inputs)[2])
	add_nonlinear_constraint(m, :($(Symbol("myfunc_$j"))($(x...)) >= $(p)))
end

```

The function ` create_cons(j, inputs)` returns two values: `f` and ` ∇f` in that order, which are the function and its gradient, depending on the iterator `j` and the dictionary `inputs` holding some input parameters.

I can solve the problem with no errors when using Ipopt. However, when I try to solve the problem with NLopt using the algorithm `:LD_SLSQP`, I get the following error:

```julia
ERROR: BoundsError
Stacktrace:
  [1] _copyto_impl!(dest::Vector{Float64}, doffs::Int64, src::Vector{Float64}, soffs::Int64, n::Int64)
    @ Base ./array.jl:329
  [2] copyto!
    @ ./array.jl:322 [inlined]
  [3] copyto!
    @ ./array.jl:346 [inlined]
  [4] _reverse_mode(d::MathOptInterface.Nonlinear.ReverseAD.NLPEvaluator, x::Vector{Float64})
    @ MathOptInterface.Nonlinear.ReverseAD ~/.julia/packages/MathOptInterface/goW8i/src/Nonlinear/ReverseAD/reverse_mode.jl:57
  [5] eval_constraint_jacobian(d::MathOptInterface.Nonlinear.ReverseAD.NLPEvaluator, J::Vector{Float64}, x::Vector{Float64})
    @ MathOptInterface.Nonlinear.ReverseAD ~/.julia/packages/MathOptInterface/goW8i/src/Nonlinear/ReverseAD/mathoptinterface_api.jl:211
  [6] eval_constraint_jacobian(evaluator::MathOptInterface.Nonlinear.Evaluator{MathOptInterface.Nonlinear.ReverseAD.NLPEvaluator}, J::Vector{Float64}, x::Vector{Float64})
    @ MathOptInterface.Nonlinear ~/.julia/packages/MathOptInterface/goW8i/src/Nonlinear/evaluator.jl:143
  [7] (::NLopt.var"#g_eq#22"{NLopt.Optimizer, Vector{Float64}, Vector{Float64}, Vector{Tuple{Int64, Int64}}, Int64, Vector{Int64}, Vector{Int64}})(result::Vector{Float64}, x::Vector{Float64}, jac::Matrix{Float64})
    @ NLopt ~/.julia/packages/NLopt/OIUOZ/src/MOI_wrapper.jl:906
  [8] optimize!(model::NLopt.Optimizer)
    @ NLopt ~/.julia/packages/NLopt/OIUOZ/src/MOI_wrapper.jl:939
  [9] optimize!
    @ ~/.julia/packages/MathOptInterface/goW8i/src/Bridges/bridge_optimizer.jl:376 [inlined]
 [10] optimize!
    @ ~/.julia/packages/MathOptInterface/goW8i/src/MathOptInterface.jl:85 [inlined]
 [11] optimize!(m::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{NLopt.Optimizer}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.Model{Float64}}})
    @ MathOptInterface.Utilities ~/.julia/packages/MathOptInterface/goW8i/src/Utilities/cachingoptimizer.jl:316
 [12] optimize!(model::Model; ignore_optimize_hook::Bool, _differentiation_backend::MathOptInterface.Nonlinear.SparseReverseMode, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ JuMP ~/.julia/packages/JuMP/ptoff/src/optimizer_interface.jl:440
 [13] optimize!
    @ ~/.julia/packages/JuMP/ptoff/src/optimizer_interface.jl:410 [inlined]
 [14] top-level scope
    @ ./timing.jl:262 [inlined]
 [15] top-level scope
    @ ./Code/src/Main.jl:0

```

I don’t understand why reverseAD is called, although I provide the gradients.  
For info, I do provide initial values to the decision variablesm which represent a feasible point to the problem.

Any ideas why this occurs? I appreciate your help!

---

<div class="post-metadata">

**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:** [May 30, 2023, 7:28pm UTC](https://discourse.julialang.org/t/boundaerror-with-nlopt-in-jump-with-a-user-defined-function-and-its-gradient/99598/2 "2023-05-30T19:28:19Z")

</div>

Can you provide a reproducible example? Hard to tell whats going on here just from that snippet.