Add tests

Author: blegat

src/RelativeEntropy/bridges/sage.jl

@@ -14,14 +14,10 @@ function MOI.Bridges.Constraint.bridge_constraint(
 ) where {T,F,G}
     m = size(set.α, 1)
     ν = Matrix{MOI.VariableIndex}(undef, m, m)
-    age_constraints =
-        Vector{MOI.ConstraintIndex{MOI.VectorOfVariables,SignomialAGECone}}(
-            undef,
-            m,
-        )
+    A = SignomialAGECone
+    c = Vector{MOI.ConstraintIndex{MOI.VectorOfVariables,A}}(undef, m)
     for k in 1:m
-        ν[k, :], age_constraints[k] =
-            MOI.add_constrained_variables(model, SignomialAGECone(set.α, k))
+        ν[k, :], c[k] = MOI.add_constrained_variables(model, A(set.α, k))
     end
     scalars = MOI.Utilities.eachscalar(func)
     n = size(set.α, 2)
@@ -36,7 +32,7 @@ function MOI.Bridges.Constraint.bridge_constraint(
             MOI.EqualTo(zero(T)),
         )
     end
-    return SAGEBridge{T,F,G}(ν, age_constraints, equality_constraints)
+    return SAGEBridge{T,F,G}(ν, c, equality_constraints)
 end
 
 function MOI.supports_constraint(

src/RelativeEntropy/bridges/signomial.jl

@@ -1,5 +1,18 @@
+"""
+    SignomialBridge{T,S,P,F} <: MOI.Bridges.Constraint.AbstractBridge
+
+We use the Signomial Representative `SR` equation of [MCW21].
+
+[MCW20] Riley Murray, Venkat Chandrasekaran, Adam Wierman
+"Newton Polytopes and Relative Entropy Optimization"
+https://arxiv.org/abs/1810.01614
+[MCW21] Murray, Riley, Venkat Chandrasekaran, and Adam Wierman.
+"Signomial and polynomial optimization via relative entropy and partial dualization."
+Mathematical Programming Computation 13 (2021): 257-295.
+https://arxiv.org/pdf/1907.00814.pdf
+"""
 struct SignomialBridge{T,S,P,F} <: MOI.Bridges.Constraint.AbstractBridge
-    constraints::Vector{MOI.ConstraintIndex{F,S}}
+    constraints::MOI.ConstraintIndex{F,S}
 end
 
 _signomial(set::PolynomialSAGECone) = SignomialSAGECone(set.α)
@@ -13,17 +26,18 @@ function MOI.Bridges.Constraint.bridge_constraint(
     func::F,
     set,
 ) where {T,S,P,F}
-    g = MOI.scalarize(func)
+    g = MOI.Utilities.scalarize(func)
     for i in eachindex(g)
-        if isodd(sum(set.α[i, :]))
+        if any(isodd, set.α[i, :])
             vi = MOI.add_variable(model)
             # vi ≤ -|g[i]|
-            MOI.add_constraint(model, one(T) * vi - g[i], MOI.LessThan(zero(T)))
-            MOI.add_constraint(model, one(T) * vi + g[i], MOI.LessThan(zero(T)))
+            MOI.Utilities.normalize_and_add_constraint(model, one(T) * vi - g[i], MOI.LessThan(zero(T)))
+            MOI.Utilities.normalize_and_add_constraint(model, one(T) * vi + g[i], MOI.LessThan(zero(T)))
             g[i] = vi
         end
     end
-    constraint = MOI.add_constraint(model, g, _signomial(set))
+    @show g
+    constraint = MOI.add_constraint(model, MOI.Utilities.vectorize(g), _signomial(set))
     return SignomialBridge{T,S,P,F}(constraint)
 end
 

test/relative_entropy.jl

@@ -11,31 +11,58 @@ import ECOS
 using JuMP
 using PolyJuMP
 
-function _test_motzkin(x, y, T, solver, set)
+function _test_motzkin(x, y, T, solver, set, feasible, square, neg)
     model = Model(solver)
+    a = square ? x^2 : x
+    b = square ? y^2 : y
     PolyJuMP.setpolymodule!(model, PolyJuMP.RelativeEntropy)
-    motzkin = x^4 * y^2 + x^2 * y^4 + one(T) - 3x^2 * y^2
+    if neg
+        motzkin = -a^2 * b - a * b^2 + one(T) - 3a * b
+    else
+        motzkin = a^2 * b + a * b^2 + one(T) - 3a * b
+    end
+    @show motzkin
     @constraint(model, motzkin in set)
     optimize!(model)
-    @test termination_status(model) == MOI.OPTIMAL
-    @test primal_status(model) == MOI.FEASIBLE_POINT
+    inner = model.moi_backend.optimizer.model
+    println(inner)
+    vis = MOI.get(inner, MOI.ListOfVariableIndices())
+    @show MOI.get(inner, MOI.VariablePrimal(), vis)
+    if feasible
+        @test termination_status(model) == MOI.OPTIMAL
+        @test primal_status(model) == MOI.FEASIBLE_POINT
+    else
+        @test termination_status(model) == MOI.INFEASIBLE
+    end
 end
 
 function test_motzkin(x, y, T, solver)
-    _test_motzkin(
-        x,
-        y,
-        T,
-        solver,
-        PolyJuMP.RelativeEntropy.SignomialAGESet(x^2 * y^2),
-    )
-    return _test_motzkin(
-        x,
-        y,
-        T,
-        solver,
-        PolyJuMP.RelativeEntropy.SignomialSAGESet(),
-    )
+    set = PolyJuMP.RelativeEntropy.SignomialAGESet(x^2 * y^2)
+    _test_motzkin(x, y, T, solver, set, true, true, false)
+    set = PolyJuMP.RelativeEntropy.SignomialAGESet(x * y)
+    _test_motzkin(x, y, T, solver, set, true, false, false)
+    set = PolyJuMP.RelativeEntropy.SignomialAGESet(x^4 * y^2)
+    _test_motzkin(x, y, T, solver, set, false, true, false)
+    set = PolyJuMP.RelativeEntropy.SignomialAGESet(x^2 * y)
+    _test_motzkin(x, y, T, solver, set, false, false, false)
+    set = PolyJuMP.RelativeEntropy.SignomialSAGESet()
+    _test_motzkin(x, y, T, solver, set, true, true, false)
+    _test_motzkin(x, y, T, solver, set, true, false, false)
+    _test_motzkin(x, y, T, solver, set, false, true, true)
+    _test_motzkin(x, y, T, solver, set, false, false, true)
+    set = PolyJuMP.RelativeEntropy.PolynomialAGESet(x^2 * y^2)
+    _test_motzkin(x, y, T, solver, set, true, true, false)
+    set = PolyJuMP.RelativeEntropy.PolynomialAGESet(x * y)
+    _test_motzkin(x, y, T, solver, set, false, false, false)
+    set = PolyJuMP.RelativeEntropy.PolynomialAGESet(x^4 * y^2)
+    _test_motzkin(x, y, T, solver, set, false, true, false)
+    set = PolyJuMP.RelativeEntropy.PolynomialAGESet(x^2 * y)
+    _test_motzkin(x, y, T, solver, set, false, false, false)
+    set = PolyJuMP.RelativeEntropy.PolynomialSAGESet()
+    _test_motzkin(x, y, T, solver, set, true, true, false)
+    set = PolyJuMP.RelativeEntropy.PolynomialSAGESet()
+    _test_motzkin(x, y, T, solver, set, false, false, false)
+    return
 end
 
 import ECOS