Merge pull request #150 from LukasBarner/max_min_quad

flips signs of quadslack for maximization, fixes 142

src/dual_equality_constraints.jl

@@ -44,6 +44,7 @@ function add_dual_equality_constraints(
         scalar_affine_terms,
         primal_dual_map.primal_var_dual_quad_slack,
         primal_objective,
+        sense_change,
     )
 
     # terms from mixing variables and parameters
@@ -240,26 +241,27 @@ function add_scalar_affine_terms_from_quad_obj(
     },
     primal_var_dual_quad_slack::Dict{MOI.VariableIndex,MOI.VariableIndex},
     primal_objective::PrimalObjective{T},
+    sense_change::T,
 ) where {T}
     for term in primal_objective.obj.quadratic_terms
         if term.variable_1 == term.variable_2
             dual_vi = primal_var_dual_quad_slack[term.variable_1]
             push_to_scalar_affine_terms!(
                 scalar_affine_terms[term.variable_1],
-                -MOI.coefficient(term),
+                -sense_change * MOI.coefficient(term),
                 dual_vi,
             )
         else
             dual_vi_1 = primal_var_dual_quad_slack[term.variable_1]
             push_to_scalar_affine_terms!(
                 scalar_affine_terms[term.variable_2],
-                -MOI.coefficient(term),
+                -sense_change * MOI.coefficient(term),
                 dual_vi_1,
             )
             dual_vi_2 = primal_var_dual_quad_slack[term.variable_2]
             push_to_scalar_affine_terms!(
                 scalar_affine_terms[term.variable_1],
-                -MOI.coefficient(term),
+                -sense_change * MOI.coefficient(term),
                 dual_vi_2,
             )
         end

test/Tests/test_max_min_dual_equal_feasibility_quadratic.jl

@@ -0,0 +1,101 @@
+function get_DualMinModel_no_bounds()
+    MinModel = Model()
+    @variable(MinModel, Q₁)
+    @variable(MinModel, Q₂)
+
+    @objective(MinModel, Min, (Q₁ + Q₂)^2)
+    @constraint(MinModel, C₁, Q₁ + 1 >= 0)
+    @constraint(MinModel, C₂, Q₂ + 1 >= 0)
+
+    DualMinModel = dualize(MinModel; dual_names = DualNames("dual", ""))
+    return DualMinModel
+end
+
+function get_DualMaxModel_no_bounds()
+    MaxModel = Model()
+    @variable(MaxModel, Q₁)
+    @variable(MaxModel, Q₂)
+    @objective(MaxModel, Max, -(Q₁ + Q₂)^2)
+    @constraint(MaxModel, C₁, Q₁ + 1 >= 0)
+    @constraint(MaxModel, C₂, Q₂ + 1 >= 0)
+
+    DualMaxModel = dualize(MaxModel; dual_names = DualNames("dual", ""))
+    return DualMaxModel
+end
+
+function get_DualMinModel_with_bounds()
+    MinModel = Model()
+    @variable(MinModel, Q₁ >= 0)
+    @variable(MinModel, Q₂ >= 0)
+
+    @objective(MinModel, Min, (Q₁ + Q₂)^2)
+    @constraint(MinModel, C₁, Q₁ + 1 >= 0)
+    @constraint(MinModel, C₂, Q₂ + 1 >= 0)
+
+    DualMinModel = dualize(MinModel; dual_names = DualNames("dual", ""))
+    return DualMinModel
+end
+
+function get_DualMaxModel_with_bounds()
+    MaxModel = Model()
+
+    @variable(MaxModel, Q₁ >= 0)
+    @variable(MaxModel, Q₂ >= 0)
+    @objective(MaxModel, Max, -(Q₁ + Q₂)^2)
+    @constraint(MaxModel, C₁, Q₁ + 1 >= 0)
+    @constraint(MaxModel, C₂, Q₂ + 1 >= 0)
+
+    DualMaxModel = dualize(MaxModel; dual_names = DualNames("dual", ""))
+    return DualMaxModel
+end
+
+function test_equivalence_max_min(DualMinModel, DualMaxModel)
+    for (F, S) in list_of_constraint_types(DualMinModel)
+        DualMinModel_eq_con_funs = [
+            MOI.get(backend(DualMinModel), MOI.ConstraintFunction(), ctr_idx) for
+            ctr_idx in JuMP.index.(all_constraints(DualMinModel, F, S))
+        ]
+        DualMaxModel_eq_con_funs = [
+            MOI.get(backend(DualMaxModel), MOI.ConstraintFunction(), ctr_idx) for
+            ctr_idx in JuMP.index.(all_constraints(DualMaxModel, F, S))
+        ]
+        @test length(DualMinModel_eq_con_funs) ==
+              length(DualMaxModel_eq_con_funs)
+        for i in eachindex(DualMinModel_eq_con_funs)
+            if typeof(DualMinModel_eq_con_funs[i]) != MOI.VariableIndex
+                @test MOI.coefficient.(DualMinModel_eq_con_funs[i].terms) ==
+                      MOI.coefficient.(DualMaxModel_eq_con_funs[i].terms)
+                @test MOI.constant.(DualMinModel_eq_con_funs[i]) ==
+                      MOI.constant.(DualMaxModel_eq_con_funs[i])
+            end
+        end
+        DualMinModel_eq_con_sets = [
+            MOI.get(backend(DualMinModel), MOI.ConstraintSet(), ctr_idx) for
+            ctr_idx in JuMP.index.(all_constraints(DualMinModel, F, S))
+        ]
+        DualMaxModel_eq_con_sets = [
+            MOI.get(backend(DualMaxModel), MOI.ConstraintSet(), ctr_idx) for
+            ctr_idx in JuMP.index.(all_constraints(DualMaxModel, F, S))
+        ]
+        @test length(DualMinModel_eq_con_sets) ==
+              length(DualMaxModel_eq_con_sets)
+        for i in eachindex(DualMinModel_eq_con_sets)
+            @test MOI.constant.(DualMinModel_eq_con_sets[i]) ==
+                  MOI.constant.(DualMaxModel_eq_con_sets[i])
+        end
+    end
+end
+
+@testset "max min dual equal feasibility quadratic" begin
+    @testset "max min dual equal feasibility quadratic no variable bounds" begin
+        DualMinModel = get_DualMinModel_no_bounds()
+        DualMaxModel = get_DualMaxModel_no_bounds()
+        test_equivalence_max_min(DualMinModel, DualMaxModel)
+    end
+
+    @testset "max min dual equal feasibility quadratic with variable bounds" begin
+        DualMinModel = get_DualMinModel_with_bounds()
+        DualMaxModel = get_DualMaxModel_with_bounds()
+        test_equivalence_max_min(DualMinModel, DualMaxModel)
+    end
+end

test/runtests.jl

@@ -106,3 +106,6 @@ include("Solvers/scs_test.jl")
 include("Tests/test_JuMP_dualize.jl")
 include("Tests/test_MOI_wrapper.jl")
 include("Tests/test_modify.jl")
+
+# Test that dual feasibility of quadratic min and max is equivalent (see issue #142)
+include("Tests/test_max_min_dual_equal_feasibility_quadratic.jl")