Add support for VectorNonlinearFunction objectives (#68)

Project.toml

@@ -11,7 +11,7 @@ MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 Combinatorics = "1"
 HiGHS = "1"
 Ipopt = "1"
-MathOptInterface = "1.15"
+MathOptInterface = "1.19"
 julia = "1.6"
 
 [extras]

src/MultiObjectiveAlgorithms.jl

@@ -88,6 +88,13 @@ function _scalarise(f::MOI.VectorQuadraticFunction, w::Vector{Float64})
     return MOI.ScalarQuadraticFunction(quad_terms, affine_terms, constant)
 end
 
+function _scalarise(f::MOI.VectorNonlinearFunction, w::Vector{Float64})
+    scalars = map(zip(w, f.rows)) do (wi, fi)
+        return MOI.ScalarNonlinearFunction(:*, Any[wi, fi])
+    end
+    return MOI.ScalarNonlinearFunction(:+, scalars)
+end
+
 abstract type AbstractAlgorithm end
 
 MOI.Utilities.map_indices(::Function, x::AbstractAlgorithm) = x
@@ -493,6 +500,9 @@ function MOI.get(model::Optimizer, attr::MOI.ListOfModelAttributesSet)
 end
 
 function MOI.delete(model::Optimizer, x::MOI.VariableIndex)
+    if model.f isa MOI.VectorNonlinearFunction
+        throw(MOI.DeleteNotAllowed(x))
+    end
     MOI.delete(model.inner, x)
     if model.f !== nothing
         model.f = MOI.Utilities.remove_variable(model.f, x)
@@ -582,7 +592,7 @@ function _compute_point(
     X = Dict{MOI.VariableIndex,Float64}(
         x => MOI.get(model.inner, MOI.VariablePrimal(), x) for x in variables
     )
-    Y = MOI.Utilities.eval_variables(x -> X[x], f)
+    Y = MOI.Utilities.eval_variables(Base.Fix1(getindex, X), model, f)
     return X, Y
 end
 

src/algorithms/EpsilonConstraint.jl

@@ -106,7 +106,7 @@ function optimize_multiobjective!(
     end
     ci = MOI.add_constraint(model, f1, SetType(bound))
     status = MOI.OPTIMAL
-    while true
+    for _ in 1:n_points
         if _time_limit_exceeded(model, start_time)
             status = MOI.TIME_LIMIT
             break

test/algorithms/EpsilonConstraint.jl

@@ -362,6 +362,38 @@ function test_poor_numerics()
     return
 end
 
+function test_vectornonlinearfunction()
+    μ = [0.006898463772627643, -0.02972609131603086]
+    Q = [0.030446 0.00393731; 0.00393731 0.00713285]
+    N = 2
+    model = MOA.Optimizer(Ipopt.Optimizer)
+    MOI.set(model, MOA.Algorithm(), MOA.EpsilonConstraint())
+    MOI.set(model, MOA.SolutionLimit(), 10)
+    MOI.set(model, MOI.Silent(), true)
+    w = MOI.add_variables(model, N)
+    MOI.add_constraint.(model, w, MOI.GreaterThan(0.0))
+    MOI.add_constraint.(model, w, MOI.LessThan(1.0))
+    MOI.add_constraint(model, sum(1.0 * w[i] for i in 1:N), MOI.EqualTo(1.0))
+    f = MOI.VectorNonlinearFunction([
+        μ' * w,
+        MOI.ScalarNonlinearFunction(
+            :/,
+            Any[μ'*w, MOI.ScalarNonlinearFunction(:sqrt, Any[w'*Q*w])],
+        ),
+    ])
+    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
+    MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
+    MOI.optimize!(model)
+    @test MOI.get(model, MOI.ResultCount()) >= 1
+    for i in 1:MOI.get(model, MOI.ResultCount())
+        w_sol = MOI.get(model, MOI.VariablePrimal(i), w)
+        y = MOI.get(model, MOI.ObjectiveValue(i))
+        @test y ≈ [μ' * w_sol, (μ' * w_sol) / sqrt(w_sol' * Q * w_sol)]
+    end
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
+    return
+end
+
 function test_time_limit()
     p1 = [77, 94, 71, 63, 96, 82, 85, 75, 72, 91, 99, 63, 84, 87, 79, 94, 90]
     p2 = [65, 90, 90, 77, 95, 84, 70, 94, 66, 92, 74, 97, 60, 60, 65, 97, 93]