Improve test coverage (#444)

test/MOI_wrapper.jl

@@ -78,6 +78,7 @@ end
 
 function test_ConstraintDualStart()
     model = Ipopt.Optimizer()
+    MOI.set(model, MOI.Silent(), true)
     x = MOI.add_variables(model, 2)
     l = MOI.add_constraint(model, x[1], MOI.GreaterThan(1.0))
     u = MOI.add_constraint(model, x[1], MOI.LessThan(1.0))
@@ -748,6 +749,39 @@ function test_RawOptimizerAttribute()
     return
 end
 
+function test_empty_nlp_evaluator()
+    model = Ipopt.Optimizer()
+    block = MOI.get(model, MOI.NLPBlock())
+    evaluator = block.evaluator
+    @test MOI.features_available(evaluator) == [:Grad, :Jac, :Hess]
+    MOI.initialize(evaluator, [:Grad, :Jac, :Hess])
+    x = Float64[]
+    @test MOI.eval_constraint(evaluator, Float64[], x) === nothing
+    @test MOI.jacobian_structure(evaluator) == Tuple{Int,Int}[]
+    @test MOI.hessian_lagrangian_structure(evaluator) == Tuple{Int,Int}[]
+    @test MOI.eval_constraint_jacobian(evaluator, Float64[], x) === nothing
+    H, mu = Float64[], Float64[]
+    @test MOI.eval_hessian_lagrangian(evaluator, H, x, 1.0, mu) === nothing
+    return
+end
+
+function test_NLPBlockDualStart()
+    model = Ipopt.Optimizer()
+    MOI.set(model, MOI.Silent(), true)
+    x = MOI.add_variables(model, 4)
+    MOI.set.(model, MOI.VariablePrimalStart(), x, 1.0)
+    block = MOI.NLPBlockData(
+        MOI.NLPBoundsPair.([25.0, 40.0], [Inf, 40.0]),
+        MOI.Test.HS071(false),
+        true,
+    )
+    MOI.set(model, MOI.NLPBlock(), block)
+    MOI.set(model, MOI.NLPBlockDualStart(), [1.0, -1.0])
+    MOI.optimize!(model)
+    @test isapprox(MOI.get(model, MOI.NLPBlockDual()), [0.0, 0.0]; atol = 1e-6)
+    return
+end
+
 function test_function_type_to_func()
     model = Ipopt.Optimizer()
     x = MOI.add_variable(model)