`MethodError: objects of type Nothing are not callable` when optimizing basic function with `OptimizationMOI` and Ipopt

Code

Optimizing a simple function without specifying autodiff.

import Pkg
Pkg.activate(temp=true)
Pkg.add([
 Pkg.PackageSpec(name="Optimization", version="3.10.0"),
 Pkg.PackageSpec(name="OptimizationMOI", version="0.1.5"),
 Pkg.PackageSpec(name="Ipopt", version="1.1.0")
], io=devnull)

import Random
import Optimization, Ipopt
using OptimizationMOI

objective_basic(params::AbstractVector, data::AbstractVector) =
    sum(@. (params[1] * data + params[2])^2)

data = randn(Random.MersenneTwister(2), 1000);

prob = Optimization.OptimizationProblem(objective_basic, [0.2, 0.1], data)
Optimization.solve(prob, Ipopt.Optimizer())

Error message

julia> Optimization.solve(prob, Ipopt.Optimizer())
This is Ipopt version 3.14.4, running with linear solver MUMPS 5.4.1.

Number of nonzeros in equality constraint Jacobian...:        0
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:        3

ERROR: MethodError: objects of type Nothing are not callable
Stacktrace:
  [1] eval_objective_gradient(moiproblem::OptimizationMOI.MOIOptimizationProblem{Float64, OptimizationFunction{true, SciMLBase.NoAD, typeof(objective_basic), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, Vector{Float64}, Matrix{Float64}, Matrix{Float64}, Matrix{Float64}}, G::Vector{Float64}, x::Vector{Float64})
    @ OptimizationMOI ~/.julia/packages/OptimizationMOI/ZoxrV/src/OptimizationMOI.jl:87
  [2] eval_objective_gradient(model::Ipopt.Optimizer, grad::Vector{Float64}, x::Vector{Float64})
    @ Ipopt ~/.julia/packages/Ipopt/rQctM/src/MOI_wrapper.jl:525
  [3] (::Ipopt.var"#eval_grad_f_cb#2"{Ipopt.Optimizer})(x::Vector{Float64}, grad_f::Vector{Float64})
    @ Ipopt ~/.julia/packages/Ipopt/rQctM/src/MOI_wrapper.jl:598
  [4] _Eval_Grad_F_CB(n::Int32, x_ptr::Ptr{Float64}, #unused#::Int32, grad_f::Ptr{Float64}, user_data::Ptr{Nothing})
    @ Ipopt ~/.julia/packages/Ipopt/rQctM/src/C_wrapper.jl:54
  [5] IpoptSolve(prob::Ipopt.IpoptProblem)
    @ Ipopt ~/.julia/packages/Ipopt/rQctM/src/C_wrapper.jl:442
  [6] optimize!(model::Ipopt.Optimizer)
    @ Ipopt ~/.julia/packages/Ipopt/rQctM/src/MOI_wrapper.jl:727
  [7] __solve(prob::OptimizationProblem{true, OptimizationFunction{true, SciMLBase.NoAD, typeof(objective_basic), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, Vector{Float64}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, opt::Ipopt.Optimizer; maxiters::Nothing, maxtime::Nothing, abstol::Nothing, reltol::Nothing, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ OptimizationMOI ~/.julia/packages/OptimizationMOI/ZoxrV/src/OptimizationMOI.jl:329
  [8] __solve
    @ ~/.julia/packages/OptimizationMOI/ZoxrV/src/OptimizationMOI.jl:276 [inlined]
  [9] #solve#540
    @ ~/.julia/packages/SciMLBase/QqtZA/src/solve.jl:84 [inlined]
 [10] solve(::OptimizationProblem{true, OptimizationFunction{true, SciMLBase.NoAD, typeof(objective_basic), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, Vector{Float64}, Vector{Float64}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}}, ::Ipopt.Optimizer)
    @ SciMLBase ~/.julia/packages/SciMLBase/QqtZA/src/solve.jl:78
 [11] top-level scope
    @ REPL[22]:1

Optimizing with Optimization.AutoForwardDiff() works fine:

julia> prob2 = Optimization.OptimizationProblem(Optimization.OptimizationFunction(objective_basic, Optimization.AutoForwardDiff()), [0.2, 0.1], data)
OptimizationProblem. In-place: true
u0: 2-element Vector{Float64}:
 0.2
 0.1

julia> Optimization.solve(prob2, Ipopt.Optimizer())
This is Ipopt version 3.14.4, running with linear solver MUMPS 5.4.1.
...
EXIT: Optimal Solution Found.
u: 2-element Vector{Float64}:
 -5.551115123125783e-17
 -1.3877787807814457e-17

julia> 

Versions

• Julia 1.9.0-beta2
• Optimization v3.10.0
• OptimizationMOI v0.1.5
• Ipopt v1.1.0

Start with the reproducer in the issue, then inspect OptimizationMOI.jl at the eval_objective_gradient and __solve entry points named in the stack trace, along with Ipopt's MOI wrapper. Confirm the failure without autodiff and add a regression test showing that the basic objective solves successfully; the existing AutoForwardDiff case provides a comparison.