fix Interval arithmetic can't be applied to quadratic constraints to insert big M value #62

Author: hdavid16

examples/ex1.jl

@@ -19,8 +19,8 @@ print(m)
 # ┌ Warning: disj_y[2] : x in [5.0, 9.0] uses the `MOI.Interval` set. Each instance of the interval set has been split into two constraints, one for each bound.
 # Feasibility
 # Subject to
-#  XOR(disj_y) : y[1] + y[2] == 1.0         <- XOR constraint
-#  y[1] ∨ y[2] : y[1] + y[2] >= 1.0         <- reformulated logical proposition (name is the proposition)
+#  XOR : y[1] + y[2] == 1.0         <- XOR constraint
+#  prop : y[1] + y[2] >= 1.0         <- reformulated logical proposition (name is the proposition)
 #  disj_y[1,lb] : -x + 5 y[1] <= 5.0       <- left-side of constraint in 1st disjunct (name is assigned to disj_y[1][lb])
 #  disj_y[1,ub] : x + 7 y[1] <= 10.0       <- right-side of constraint in 1st disjunct (name is assigned to disj_y[1][ub])
 #  disj_y[2,lb] : -x + 10 y[2] <= 5.0      <- left-side of constraint in 2nd disjunct (name is assigned to disj_y[2][lb])

examples/ex2.jl

@@ -20,7 +20,7 @@ print(m)
 
 # Feasibility
 # Subject to
-#  XOR(disj_y) : y[1] + y[2] == 1.0         <- XOR constraint
+#  XOR : y[1] + y[2] == 1.0         <- XOR constraint
 #  con1[1,lb] : -x[1] + 5 y[1] <= 5.0      <- left-side of con1[1]
 #  con1[1,ub] : x[1] + 7 y[1] <= 10.0      <- right-side of con1[1]
 #  con1[2,lb] : -x[2] + 5 y[1] <= 5.0      <- left-side of con1[2]

src/bigm.jl

@@ -41,11 +41,11 @@ big_m_reformulation!(constr::AbstractArray{<:ConstraintRef}, bin_var, M, i, j, k
     big_m_reformulation(constr[k], bin_var, M, i, j, k)
 
 """
-    infer_bigm(constr)
+    calculate_tight_M(constr::ConstraintRef{<:AbstractModel, MOI.ConstraintIndex{MOI.ScalarAffineFunction{T},V}}) where {T,V}
 
-Apply interval arithmetic on a constraint to infer the tightest Big-M value from the bounds on the constraint.
+Apply interval arithmetic on a linear constraint to infer the tightest Big-M value from the bounds on the constraint.
 """
-function infer_bigm(constr::ConstraintRef)
+function calculate_tight_M(constr::ConstraintRef{<:AbstractModel, MOI.ConstraintIndex{MOI.ScalarAffineFunction{T},V}}) where {T,V}
     constr_obj = constraint_object(constr)
     constr_terms = constr_obj.func.terms
     constr_set = constr_obj.set
@@ -78,4 +78,4 @@ function infer_bigm(constr::ConstraintRef)
     isinf(M) && error("M parameter for $constr cannot be infered due to lack of variable bounds.")
     return M
 end
-infer_bigm(constr::NonlinearConstraintRef) = error("$constr is a nonlinear constraint and a tight Big-M parameter cannot be inferred via interval arithmetic.")
+calculate_tight_M(constr::ConstraintRef) = error("$constr is a nonlinear or quadratic constraint and a tight Big-M parameter cannot be inferred via interval arithmetic.")

src/utils.jl

@@ -15,7 +15,7 @@ get_indices(arr) = Iterators.product(axes(arr)...)
 
 Get M or ϵ parameter for reformulation.
 """
-get_reform_param(param::Missing, args...; constr) = infer_bigm(constr) #if param is missing, infer bigM (ϵ does not pass a kwarg)
+get_reform_param(param::Missing, args...; constr) = calculate_tight_M(constr) #if param is missing, infer bigM (ϵ does not pass a kwarg)
 get_reform_param(param::Number, args...; kwargs...) = param #if param is a number return it
 get_reform_param(param::Union{Vector,Tuple}, idx::Int, args...; kwargs...) = #index param by next Integer arg (idx)
     get_reform_param(param[idx], args...; kwargs...)