Add polynomial preprocessing bridges

src/Bridges/Bridges.jl

@@ -1,5 +1,9 @@
 module Bridges
 
+import MathOptInterface as MOI
+using PolyJuMP
+
+include("Utilities.jl")
 # Constraint bridges
 include("Constraint/Constraint.jl")
 # Objective bridges

src/Bridges/Constraint/Constraint.jl

@@ -13,5 +13,6 @@ include("zero_polynomial.jl")
 include("zero_polynomial_in_algebraic_set.jl")
 include("plus_minus.jl")
 include("to_polynomial.jl")
+include("to_reformulated_polynomial.jl")
 
 end # module

src/Bridges/Constraint/to_reformulated_polynomial.jl

@@ -0,0 +1,79 @@
+"""
+    ToReformulatedPolynomialBridge{T,S} <: Bridges.Constraint.AbstractBridge
+
+`ToReformulatedPolynomialBridge` implements a sparsity-preserving
+reformulation of `f(x) in S` into `g(x, y) in S` where `f` is a
+`MOI.ScalarNonlinearFunction`: products and powers of sums are replaced by
+auxiliary variables `y`, so that converting `g` into a polynomial does not
+expand them.
+
+Since source and target are both `MOI.ScalarNonlinearFunction`, this bridge
+doesn't have any effect when added to a `MOI.Bridges.LazyBridgeOptimizer`; use it in a
+`MOI.Bridges.Constraint.SingleBridgeOptimizer` on top of an optimizer converting
+`MOI.ScalarNonlinearFunction` into polynomials, e.g., [`PolyJuMP.QCQP.Optimizer`](@ref).
+
+## Source node
+
+`ToReformulatedPolynomialBridge` supports:
+
+  * `MOI.ScalarNonlinearFunction` in `S`
+
+## Target nodes
+
+`ToReformulatedPolynomialBridge` creates:
+
+  * `MOI.ScalarNonlinearFunction` in `S`
+  * `MOI.ScalarNonlinearFunction` in `MOI.EqualTo{T}`, defining the
+    auxiliary variables
+  * `MOI.VariableIndex` in `MOI.Interval{T}`, the auxiliary variables
+"""
+struct ToReformulatedPolynomialBridge{T,S} <:
+       MOI.Bridges.Constraint.AbstractFunctionConversionBridge{MOI.ScalarNonlinearFunction,S}
+    constraint::MOI.ConstraintIndex{MOI.ScalarNonlinearFunction,S}
+end
+
+function MOI.Bridges.Constraint.bridge_constraint(
+    ::Type{ToReformulatedPolynomialBridge{T,S}},
+    model::MOI.ModelLike,
+    func::MOI.ScalarNonlinearFunction,
+    set::S,
+) where {T,S}
+    reformulated = PolyJuMP.Bridges._to_reformulated_polynomial!(model, T, func)
+    constraint = MOI.add_constraint(model, reformulated, set)
+    return ToReformulatedPolynomialBridge{T,S}(constraint)
+end
+
+function MOI.Bridges.Objective.supports_objective_function(
+    ::Type{ToReformulatedPolynomialBridge{T}},
+    ::Type{F},
+) where {T,F<:MOI.ScalarNonlinearFunction}
+    return MOI.Utilities.is_coefficient_type(F, T)
+end
+
+
+function MOI.supports_constraint(
+    ::Type{ToReformulatedPolynomialBridge{T}},
+    ::Type{MOI.ScalarNonlinearFunction},
+    ::Type{<:MOI.AbstractScalarSet},
+) where {T}
+    return true
+end
+
+function MOI.Bridges.Constraint.concrete_bridge_type(
+    ::Type{<:ToReformulatedPolynomialBridge{T}},
+    ::Type{MOI.ScalarNonlinearFunction},
+    ::Type{S},
+) where {T,S<:MOI.AbstractScalarSet}
+    return ToReformulatedPolynomialBridge{T,S}
+end
+
+
+function MOI.get(
+    model::MOI.ModelLike,
+    attr::MOI.Bridges.ObjectiveFunctionValue,
+    ::ToReformulatedPolynomialBridge{T},
+) where {T}
+    F = FuncType{T}
+    attr_f = MOI.Bridges.ObjectiveFunctionValue{F}(attr.result_index)
+    return MOI.get(model, attr_f)
+end

src/Bridges/Objective/Objective.jl

@@ -1,10 +1,10 @@
 module Objective
 
-import MathOptInterface
-const MOI = MathOptInterface
+import MathOptInterface as MOI
 
 using PolyJuMP
 
 include("to_polynomial.jl")
+include("to_reformulated_polynomial.jl")
 
 end # module

src/Bridges/Objective/to_reformulated_polynomial.jl

@@ -0,0 +1,58 @@
+"""
+    ToReformulatedPolynomialBridge{T} <: Bridges.Objective.AbstractBridge
+
+`ToReformulatedPolynomialBridge` implements a sparsity-preserving
+reformulation of the objective `f(x)` into `g(x, y)` where `f` is a
+`MOI.ScalarNonlinearFunction`: products and powers of sums are replaced by
+auxiliary variables `y`, so that converting `g` into a polynomial does not
+expand them.
+
+Since source and target are both `MOI.ScalarNonlinearFunction`, this bridge
+doesn't have any effect when added to a `MOI.Bridges.LazyBridgeOptimizer`; use it in a
+`MOI.Bridges.Objective.SingleBridgeOptimizer` on top of an optimizer converting
+`MOI.ScalarNonlinearFunction` into polynomials, e.g., [`PolyJuMP.QCQP.Optimizer`](@ref).
+
+## Source node
+
+`ToReformulatedPolynomialBridge` supports:
+
+ * `MOI.ObjectiveFunction{MOI.ScalarNonlinearFunction}`
+
+## Target nodes
+
+`ToReformulatedPolynomialBridge` creates:
+
+ * `MOI.ObjectiveFunction{MOI.ScalarNonlinearFunction}`
+ * `MOI.ScalarNonlinearFunction` in `MOI.EqualTo{T}`, defining the auxiliary
+   variables
+ * `MOI.VariableIndex` in `MOI.Interval{T}`, the auxiliary variables
+"""
+struct ToReformulatedPolynomialBridge{T} <: MOI.Bridges.Objective.AbstractBridge end
+
+function MOI.Bridges.Objective.bridge_objective(
+    ::Type{ToReformulatedPolynomialBridge{T}},
+    model::MOI.ModelLike,
+    func::MOI.ScalarNonlinearFunction,
+) where {T}
+    reformulated = PolyJuMP.Bridges._to_reformulated_polynomial!(model, T, func)
+    F = MOI.ScalarNonlinearFunction
+    MOI.set(model, MOI.ObjectiveFunction{F}(), reformulated)
+    return ToReformulatedPolynomialBridge{T}()
+end
+
+function MOI.Bridges.Objective.supports_objective_function(
+    ::Type{ToReformulatedPolynomialBridge{T}},
+    ::Type{F},
+) where {T,F<:MOI.ScalarNonlinearFunction}
+    return MOI.Utilities.is_coefficient_type(F, T)
+end
+
+function MOI.get(
+    model::MOI.ModelLike,
+    attr::MOI.Bridges.ObjectiveFunctionValue,
+    ::ToReformulatedPolynomialBridge{T},
+) where {T}
+    F = FuncType{T}
+    attr_f = MOI.Bridges.ObjectiveFunctionValue{F}(attr.result_index)
+    return MOI.get(model, attr_f)
+end