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:
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:
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!