Add support for get MOI.NLPBlock

src/MOI_wrapper.jl

@@ -624,6 +624,10 @@ MOI.get(model::Optimizer, ::MOI.NLPBlockDualStart) = model.nlp_dual_start
 
 MOI.supports(::Optimizer, ::MOI.NLPBlock) = true
 
+# This may also be set by `optimize!` and contain the block created from
+# ScalarNonlinearFunction
+MOI.get(model::Optimizer, ::MOI.NLPBlock) = model.nlp_data
+
 function MOI.set(model::Optimizer, ::MOI.NLPBlock, nlp_data::MOI.NLPBlockData)
     model.nlp_data = nlp_data
     model.inner = nothing

test/MOI_wrapper.jl

@@ -168,6 +168,7 @@ function test_check_derivatives_for_naninf()
     # MOI.set(model, MOI.RawOptimizerAttribute("check_derivatives_for_naninf"), "no")
     MOI.optimize!(model)
     @test MOI.get(model, MOI.TerminationStatus()) == MOI.INVALID_MODEL
+    @test MOI.get(model, MOI.NLPBlock()) isa MOI.NLPBlockData
     return
 end
 
@@ -359,6 +360,18 @@ function test_scalar_nonlinear_function_is_valid()
     return
 end
 
+function test_scalar_nonlinear_function_nlp_block()
+    model = Ipopt.Optimizer()
+    x = MOI.add_variable(model)
+    f = MOI.ScalarNonlinearFunction(:^, Any[x, 4])
+    MOI.add_constraint(model, f, MOI.LessThan(1.0))
+    MOI.optimize!(model)
+    block = MOI.get(model, MOI.NLPBlock())
+    @test !block.has_objective
+    @test block.evaluator isa MOI.Nonlinear.Evaluator
+    return
+end
+
 function test_parameter()
     model = Ipopt.Optimizer()
     MOI.set(model, MOI.Silent(), true)