s/ParetoSolution/SolutionPoint

src/MOO.jl

@@ -9,12 +9,12 @@ import MathOptInterface
 
 const MOI = MathOptInterface
 
-struct ParetoSolution
+struct SolutionPoint
     x::Dict{MOI.VariableIndex,Float64}
     y::Vector{Float64}
 end
 
-function Base.isapprox(a::ParetoSolution, b::ParetoSolution; kwargs...)
+function Base.isapprox(a::SolutionPoint, b::SolutionPoint; kwargs...)
     return isapprox(a.y, b.y; kwargs...)
 end
 
@@ -46,7 +46,7 @@ mutable struct Optimizer <: MOI.AbstractOptimizer
     inner::MOI.AbstractOptimizer
     algorithm::Union{Nothing,AbstractAlgorithm}
     f::Union{Nothing,MOI.AbstractVectorFunction}
-    solutions::Union{Nothing,Vector{ParetoSolution}}
+    solutions::Union{Nothing,Vector{SolutionPoint}}
     termination_status::MOI.TerminationStatusCode
 
     function Optimizer(optimizer_factory)

src/algorithms/EpsilonConstraint.jl

@@ -57,7 +57,7 @@ function optimize_multiobjective!(
     # Compute the epsilon that we will be incrementing by each iteration
     n_points = MOI.get(algorithm, SolutionLimit())
     ε = abs(right - left) / (n_points - 1)
-    solutions = ParetoSolution[]
+    solutions = SolutionPoint[]
     f1, f2 = MOI.Utilities.eachscalar(model.f)
     MOI.set(model.inner, MOI.ObjectiveFunction{typeof(f2)}(), f2)
     # Add epsilon constraint
@@ -81,7 +81,7 @@ function optimize_multiobjective!(
         )
         Y = MOI.Utilities.eval_variables(x -> X[x], model.f)
         if isempty(solutions) || !(Y ≈ solutions[end].y)
-            push!(solutions, ParetoSolution(X, Y))
+            push!(solutions, SolutionPoint(X, Y))
         end
     end
     MOI.delete(model, ci)

src/algorithms/Hierarchical.jl

@@ -131,5 +131,5 @@ function optimize_multiobjective!(algorithm::Hierarchical, model::Optimizer)
     for c in constraints
         MOI.delete(model, c)
     end
-    return MOI.OPTIMAL, [ParetoSolution(X, Y)]
+    return MOI.OPTIMAL, [SolutionPoint(X, Y)]
 end

src/algorithms/Lexicographic.jl

@@ -65,5 +65,5 @@ function optimize_multiobjective!(algorithm::Lexicographic, model::Optimizer)
     for c in constraints
         MOI.delete(model, c)
     end
-    return MOI.OPTIMAL, [ParetoSolution(X, Y)]
+    return MOI.OPTIMAL, [SolutionPoint(X, Y)]
 end

src/algorithms/NISE.jl

@@ -47,7 +47,7 @@ function _solve_weighted_sum(model::Optimizer, ::NISE, weights::Vector{Float64})
         x in MOI.get(model.inner, MOI.ListOfVariableIndices())
     )
     Y = MOI.Utilities.eval_variables(x -> X[x], model.f)
-    return status, ParetoSolution(X, Y)
+    return status, SolutionPoint(X, Y)
 end
 
 function optimize_multiobjective!(algorithm::NISE, model::Optimizer)
@@ -58,7 +58,7 @@ function optimize_multiobjective!(algorithm::NISE, model::Optimizer)
         status, solution = _solve_weighted_sum(model, algorithm, [1.0])
         return status, [solution]
     end
-    solutions = Dict{Float64,ParetoSolution}()
+    solutions = Dict{Float64,SolutionPoint}()
     for w in (0.0, 1.0)
         status, solution = _solve_weighted_sum(model, algorithm, w)
         if status != MOI.OPTIMAL