Support the JuMP macro operator

[amontoison]

close #195

[github-actions]

Package name	latest	stable
NLSProblems.jl
OptimizationProblems.jl

[odow]

index a3f36e4..908c38c 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -426,41 +426,46 @@ function parser_MOI(moimodel, index_map, nvar)
 end
 
 # Affine or quadratic, nothing to do
-function _nlp_model(model::Union{Nothing, MOI.Nonlinear.Model}, ::MOI.ModelLike, ::Type, ::Type)
-  return model
-end
+_nlp_model(::MOI.Nonlinear.Model, ::MOI.ModelLike, ::Type, ::Type) = false
 
 function _nlp_model(
-  dest::Union{Nothing, MOI.Nonlinear.Model},
+  dest::MOI.Nonlinear.Model,
   src::MOI.ModelLike,
   F::Type{<:Union{MOI.ScalarNonlinearFunction, MOI.VectorNonlinearFunction}},
   S::Type,
 )
+  has_nonlinear = false
   for ci in MOI.get(src, MOI.ListOfConstraintIndices{F, S}())
-    if isnothing(dest)
-      dest = MOI.Nonlinear.Model()
-    end
     MOI.Nonlinear.add_constraint(
       dest,
       MOI.get(src, MOI.ConstraintFunction(), ci),
       MOI.get(src, MOI.ConstraintSet(), ci),
     )
+    has_nonlinear = true
   end
-  return dest
+  return has_nonlinear
 end
 
-function _nlp_model(model::MOI.ModelLike)
-  nlp_model = nothing
+function _nlp_model(model::MOI.ModelLike)::Union{Nothing,MOI.Nonlinear.Model}
+  nlp_model = MOI.Nonlinear.Model()
+  has_nonlinear = false
+  for attr in MOI.get(model, MOI.ListOfModelAttributesSet())
+    if attr isa MOI.UserDefinedFunction
+      has_nonlinear = true
+      args = MOI.get(model, attr)
+      MOI.Nonlinear.register_operator(nlp_model, attr.name, attr.arity, args...)
+    end
+  end
   for (F, S) in MOI.get(model, MOI.ListOfConstraintTypesPresent())
-    nlp_model = _nlp_model(nlp_model, model, F, S)
+    has_nonlinear |= _nlp_model(nlp_model, model, F, S)
   end
   F = MOI.get(model, MOI.ObjectiveFunctionType())
   if F <: MOI.ScalarNonlinearFunction
-    if isnothing(nlp_model)
-      nlp_model = MOI.Nonlinear.Model()
-    end
-    attr = MOI.ObjectiveFunction{F}()
-    MOI.Nonlinear.set_objective(nlp_model, MOI.get(model, attr))
+    MOI.Nonlinear.set_objective(nlp_model, MOI.get(model, MOI.ObjectiveFunction{F}()))
+    has_nonlinear = true
+  end
+  if !has_nonlinear
+    return nothing
   end
   return nlp_model
 end

Results in

julia> nlp_jump = MathOptNLPModel(hs87())
MathOptNLPModel
  Problem name: Generic
   All variables: ████████████████████ 6      All constraints: ████████████████████ 4     
            free: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 free: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
           lower: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                lower: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
           upper: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                upper: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
         low/upp: ████████████████████ 6              low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
           fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                fixed: ████████████████████ 4     
          infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
            nnzh: (-23.81% sparsity)   26              linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
                                                    nonlinear: ████████████████████ 4     
                                                         nnzj: ( 37.50% sparsity)   15    

  Counters:
             obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 grad: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 cons: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
        cons_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0             cons_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 jcon: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
           jgrad: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                  jac: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0              jac_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
         jac_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                jprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0            jprod_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
       jprod_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               jtprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0           jtprod_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
      jtprod_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 hess: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                hprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
           jhess: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               jhprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0 

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 92.06%. Comparing base (c8110ed) to head (0c6f4fe).
Report is 13 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #197      +/-   ##
==========================================
+ Coverage   90.39%   92.06%   +1.66%     
==========================================
  Files           4        4              
  Lines         906      958      +52     
==========================================
+ Hits          819      882      +63     
+ Misses         87       76      -11     

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