Enable QCPDual by default

Author: odow

src/MOI_wrapper.jl

@@ -106,7 +106,9 @@ mutable struct Optimizer <: MOI.ModelLike
 
     Note that we set the parameter `InfUnbdInfo` to `1` rather than the default
     of `0` so that we can query infeasibility certificates. Users are, however,
-    free to overide this as follows `Optimizer(InfUndbInfo=0)`.
+    free to over-ride this as follows `Optimizer(InfUndbInfo=0)`. In addition,
+    we also set `QCPDual` to `1` to enable duals in QCPs. Users can override
+    this by passing `Optimizer(QCPDual=0)`.
     """
     function Optimizer(env::Union{Nothing, Env} = nothing; kwargs...)
         model = new()
@@ -127,6 +129,9 @@ mutable struct Optimizer <: MOI.ModelLike
         if !haskey(model.params, "InfUnbdInfo")
             MOI.set(model, MOI.RawParameter("InfUnbdInfo"), 1)
         end
+        if !haskey(model.params, "QCPDual")
+            MOI.set(model, MOI.RawParameter("QCPDual"), 1)
+        end
         return model
     end
 end
@@ -1748,6 +1753,8 @@ function MOI.get(model::Optimizer, ::MOI.DualStatus)
     stat = get_status(model.inner)
     if is_mip(model.inner)
         return MOI.NO_SOLUTION
+    elseif is_qcp(model.inner) && MOI.get(model, MOI.RawParameter("QCPDual")) != 1
+        return MOI.NO_SOLUTION
     elseif stat == :optimal
         return MOI.FEASIBLE_POINT
     elseif stat == :solution_limit

test/MOI_wrapper.jl

@@ -318,3 +318,52 @@ end
     MOI.set(model, MOI.RawParameter("OutputFlag"), 0)
     @test MOI.get(model, MOI.RawParameter("OutputFlag")) == 0
 end
+
+@testset "QCPDuals without needing to pass QCPDual=1" begin
+    @testset "QCPDual default" begin
+        model = Gurobi.Optimizer(GUROBI_ENV, OutputFlag=0)
+        MOI.Utilities.loadfromstring!(model, """
+        variables: x, y, z
+        minobjective: 1.0 * x + 1.0 * y + 1.0 * z
+        c1: x + y == 2.0
+        c2: x + y + z >= 0.0
+        c3: 1.0 * x * x + -1.0 * y * y + -1.0 * z * z >= 0.0
+        c4: x >= 0.0
+        c5: y >= 0.0
+        c6: z >= 0.0
+        """)
+        MOI.optimize!(model)
+        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
+        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+        @test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
+        c1 = MOI.get(model, MOI.ConstraintIndex, "c1")
+        c2 = MOI.get(model, MOI.ConstraintIndex, "c2")
+        c3 = MOI.get(model, MOI.ConstraintIndex, "c3")
+        @test MOI.get(model, MOI.ConstraintDual(), c1) ≈ 1.0 atol=1e-6
+        @test MOI.get(model, MOI.ConstraintDual(), c2) ≈ 0.0 atol=1e-6
+        @test MOI.get(model, MOI.ConstraintDual(), c3) ≈ 0.0 atol=1e-6
+    end
+    @testset "QCPDual=0" begin
+        model = Gurobi.Optimizer(GUROBI_ENV, OutputFlag=0, QCPDual=0)
+        MOI.Utilities.loadfromstring!(model, """
+        variables: x, y, z
+        minobjective: 1.0 * x + 1.0 * y + 1.0 * z
+        c1: x + y == 2.0
+        c2: x + y + z >= 0.0
+        c3: 1.0 * x * x + -1.0 * y * y + -1.0 * z * z >= 0.0
+        c4: x >= 0.0
+        c5: y >= 0.0
+        c6: z >= 0.0
+        """)
+        MOI.optimize!(model)
+        @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
+        @test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
+        @test MOI.get(model, MOI.DualStatus()) == MOI.NO_SOLUTION
+        c1 = MOI.get(model, MOI.ConstraintIndex, "c1")
+        c2 = MOI.get(model, MOI.ConstraintIndex, "c2")
+        c3 = MOI.get(model, MOI.ConstraintIndex, "c3")
+        @test_throws Gurobi.GurobiError MOI.get(model, MOI.ConstraintDual(), c1)
+        @test_throws Gurobi.GurobiError MOI.get(model, MOI.ConstraintDual(), c2)
+        @test_throws Gurobi.GurobiError MOI.get(model, MOI.ConstraintDual(), c3)
+    end
+end