Fix distance_to_set for sets without a definition in MOI

src/Constraint.jl

@@ -40,12 +40,35 @@ end
 
 AbstractTrees.children(c::Constraint) = (c.child,)
 
-# A fallback. Define a new method if `MOI.Utilities.distance_to_set`
-# is not defined.
-function is_feasible(x, set, tol)
+# A default fallback which means that we are unsure.
+is_feasible(x, set, tol) = missing
+
+function is_feasible(
+    x::Vector,
+    set::Union{
+        MOI.Nonnegatives,
+        MOI.Nonpositives,
+        MOI.Zeros,
+        MOI.SecondOrderCone,
+        MOI.RotatedSecondOrderCone,
+        MOI.ExponentialCone,
+        MOI.DualExponentialCone,
+        MOI.PowerCone,
+        MOI.DualPowerCone,
+        MOI.GeometricMeanCone,
+        MOI.NormCone,
+        MOI.NormInfinityCone,
+        MOI.NormOneCone,
+    },
+    tol,
+)
     return MOI.Utilities.distance_to_set(x, set) <= tol
 end
 
+function is_feasible(x::AbstractMatrix, set::MOI.AbstractVectorSet, tol)
+    return is_feasible(vec(x), set, tol)
+end
+
 function is_feasible(x::Number, set::MOI.AbstractVectorSet, tol)
     return is_feasible([x], set, tol)
 end
@@ -88,13 +111,26 @@ end
 
 function _add_constraint!(context::Context, c::Constraint)
     if vexity(c.child) == ConstVexity()
-        # This `evaluate` call is safe, since even if it refers to a `fix!`'d variable,
-        # it happens when we are formulating the problem (not at expression-time), so there
-        # is not time for the variable to be re-`fix!`'d to a different value (or `free!`'d)
-        if !is_feasible(evaluate(c.child), c.set, CONSTANT_CONSTRAINT_TOL[])
+        # This `evaluate` call is safe, since even if it refers to a `fix!`'d
+        # variable, it happens when we are formulating the problem (not at
+        # expression-time), so there is no time for the variable to be
+        # re-`fix!`'d to a different value (or `free!`'d)
+        feas = is_feasible(evaluate(c.child), c.set, CONSTANT_CONSTRAINT_TOL[])
+        # There are three possible values of feas: true, false, and missing.
+        if feas === true
+            # Do nothing. The constraint is satisfied. Do not add it to the
+            # solver.
+            return
+        elseif feas === false
+            # We have proven the constraint is not satisfied. Set a flag and
+            # bail. We don't need to add it to the solver.
             context.detected_infeasible_during_formulation = true
+            return
+        else
+            # The feasibility check was unsure, likely because a method was
+            # missing. Pass the constraint to the solver.
+            @assert ismissing(feas)
         end
-        return
     end
     f = conic_form!(context, c.child)
     context.constr_to_moi_inds[c] = MOI_add_constraint(context.model, f, c.set)

src/to_MOI.jl

@@ -30,6 +30,11 @@ function MOI_add_constraint(model, f, set)
     return MOI.add_constraint(model, f, set)
 end
 
+function MOI_add_constraint(model, f::SPARSE_VECTOR{T}, set) where {T}
+    g = MOI.VectorAffineFunction{T}(MOI.VectorAffineTerm{T}[], f)
+    return MOI.add_constraint(model, g, set)
+end
+
 function MOI_add_constraint(model, f::SparseTape, set)
     return MOI_add_constraint(model, to_vaf(f), set)
 end

test/test_constraints.jl

@@ -397,6 +397,49 @@ function test_RelativeEntropyEpiConeSquare()
     return
 end
 
+function test_is_feasible()
+    @test Convex.is_feasible([1.0, 0.0], MOI.Nonnegatives(2), 0.0)
+    @test !Convex.is_feasible([-1.0, 0.0], MOI.Nonnegatives(2), 0.0)
+    @test Convex.is_feasible([-1.0, 0.0], MOI.Nonpositives(2), 0.0)
+    @test !Convex.is_feasible([1.0, 0.0], MOI.Nonpositives(2), 0.0)
+    @test Convex.is_feasible([1e-5, 0.0], MOI.Zeros(2), 1e-5)
+    @test !Convex.is_feasible([1e-5, 0.0], MOI.Zeros(2), 0.0)
+    @test Convex.is_feasible([5.0, 3.0, 4.0], MOI.SecondOrderCone(3), 0.0)
+    set = MOI.PositiveSemidefiniteConeSquare(2)
+    @test Convex.is_feasible([1.0 0.0; 0.0 1.0], set, 0.0)
+    @test !Convex.is_feasible([-1.0 0.0; 0.0 1.0], set, 0.0)
+    @test !Convex.is_feasible([1.0 1e-6; 0.0 1.0], set, 0.0)
+    set = MOI.NormSpectralCone(2, 2)
+    @test Convex.is_feasible([1.0, 1.0, 0.0, 0.0, 1.0], set, 0.0) === missing
+    return
+end
+
+function test_distance_to_set_matrix()
+    x = Variable(2, 2)
+    y = Variable()
+    fix!(x, [1 0; 0 1])
+    # Constraint has a fixed `Matrix` value.
+    model = minimize(y, [sum(x; dims = 1) <= 1, y >= 1])
+    solve!(model, SCS.Optimizer; silent = true)
+    @test ≈(model.optval, 1.0; atol = 1e-3)
+    return
+end
+
+function test_distance_to_set_undefined()
+    t = Variable()
+    fix!(t, 2)
+    x = Variable(2, 2)
+    fix!(x, [1 0; 0 1])
+    y = Variable()
+    # This constraint is fixed, and `MOI.distance_to_set` is not defined for it,
+    # but it should still work without erroring.
+    c = Convex.Constraint(vcat(t, vec(x)), MOI.NormSpectralCone(2, 2))
+    model = minimize(y, [c, y >= 1])
+    solve!(model, SCS.Optimizer; silent = true)
+    @test ≈(model.optval, 1.0; atol = 1e-4)
+    return
+end
+
 end  # TestConstraints
 
 TestConstraints.runtests()