more tests

Author: joaquimg

src/feasibility.jl

@@ -815,7 +815,7 @@ function _analyze_complementarity!(model, data)
         obj = JuMP.constraint_object(con)
         func = obj.func
         set = obj.set
-        func_val = JuMP.value(x -> data.primal_point[x], func) - _set_value(set)
+        func_val = JuMP.value.(x -> data.primal_point[x], func) - _set_value(set)
         comp_val = MOI.Utilities.set_dot(func_val, data.dual_point[con], set)
         if abs(comp_val) > data.atol
             push!(data.complementarity, ComplemetarityViolation(con, comp_val))
@@ -824,9 +824,14 @@ function _analyze_complementarity!(model, data)
     return
 end
 
-function _set_value(set::MOI.AbstractScalarSet)
-    return 0.0
-end
+# not needed because it would have stoped in dualization before
+# function _set_value(set::MOI.AbstractScalarSet)
+#     return 0.0
+# end
+# function _set_value(set::MOI.Interval)
+#     error("Interval sets are not supported.")
+#     return (set.lower, set.upper)
+# end
 
 function _set_value(set::MOI.AbstractVectorSet)
     return zeros(MOI.dimension(set))
@@ -844,11 +849,6 @@ function _set_value(set::MOI.EqualTo)
     return set.value
 end
 
-function _set_value(set::MOI.Interval)
-    error("Interval sets are not supported.")
-    return (set.lower, set.upper)
-end
-
 function _analyze_objectives!(
     model::JuMP.GenericModel{T},
     dual_model,
@@ -1140,7 +1140,7 @@ function _dualize2(
     kwargs...,
 )
     mode = JuMP.mode(model)
-    if mode != JuMP.AUTOMATIC
+    if mode == JuMP.MANUAL
         error("Dualization does not support solvers in $(mode) mode")
     end
     dual_model = JuMP.Model()

test/feasibility.jl

@@ -37,6 +37,7 @@ function test_no_solution()
     @constraint(model, c, x^4 >= 0) # this will make the model non-linear
     @test_throws ErrorException ModelAnalyzer.Feasibility.dual_feasibility_report(
         model,
+        point = Dict(), # to skip dual solutions error
     )
 end
 
@@ -176,6 +177,64 @@ function test_analyse_simple()
     return
 end
 
+function test_analyse_simple_direct()
+    model = direct_model(HiGHS.Optimizer())
+    set_silent(model)
+    @variable(model, x)
+    @constraint(model, c, x >= 0)
+    @objective(model, Min, 2 * x)
+
+    optimize!(model)
+
+    data = ModelAnalyzer.analyze(ModelAnalyzer.Feasibility.Analyzer(), model)
+
+    list = ModelAnalyzer.list_of_issue_types(data)
+
+    @test length(list) == 0
+
+    return
+end
+
+function test_with_interval()
+    model = Model(HiGHS.Optimizer)
+    set_silent(model)
+    @variable(model, x >= 1)
+    @constraint(model, c, 2 * x in MOI.Interval(0.0, 3.0))
+    @objective(model, Min, x)
+    optimize!(model)
+    @test !ModelAnalyzer.Feasibility._can_dualize(model)
+    # TODO this should eb a waning at least
+    data = ModelAnalyzer.analyze(ModelAnalyzer.Feasibility.Analyzer(), model)
+    return
+end
+
+function test_analyse_many_constraint_types()
+    model = Model(HiGHS.Optimizer)
+    set_silent(model)
+    @variable(model, x >= 1)
+    @variable(model, y <= 0)
+    @variable(model, z == 0)
+    @variable(model, w)
+    @variable(model, 0 <= v <= 1)
+    @constraint(model, c1, x >= 0)
+    @constraint(model, c2, x <= 10)
+    @constraint(model, c3, x == 5)
+    @constraint(model, c4, y >= -1)
+    @constraint(model, c5, w == 3)
+    @constraint(model, c6, [2v] in Nonpositives()) # this should be redundant as z is fixed to 0
+    @objective(model, Min, x)
+
+    optimize!(model)
+
+    data = ModelAnalyzer.analyze(ModelAnalyzer.Feasibility.Analyzer(), model)
+
+    list = ModelAnalyzer.list_of_issue_types(data)
+
+    @test length(list) == 0
+
+    return
+end
+
 function test_analyse_mip()
     model = Model(HiGHS.Optimizer)
     set_silent(model)