Add support for vector-valued objective functions (#497)

Author: odow

Project.toml

@@ -9,7 +9,7 @@ Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 
 [compat]
-MathOptInterface = "1.7"
+MathOptInterface = "1.12"
 julia = "1.6"
 
 [extras]

src/MOI_wrapper/MOI_multi_objective.jl

@@ -126,3 +126,37 @@ function MOI.get(model::Gurobi.Optimizer, attr::MultiObjectiveValue)
     _check_ret(model, ret)
     return val[]
 end
+
+function MOI.set(
+    model::Optimizer,
+    ::MOI.ObjectiveFunction{F},
+    f::F,
+) where {F<:MOI.VectorAffineFunction{Float64}}
+    for (i, fi) in enumerate(MOI.Utilities.eachscalar(f))
+        MOI.set(model, MultiObjectiveFunction(i), fi)
+    end
+    model.objective_type = _VECTOR_AFFINE
+    return
+end
+
+function MOI.get(
+    model::Optimizer,
+    ::MOI.ObjectiveFunction{MOI.VectorAffineFunction{Float64}},
+)
+    env = GRBgetenv(model)
+    F = MOI.ScalarAffineFunction{Float64}
+    f = F[]
+    for i in 1:MOI.get(model, NumberOfObjectives())
+        ret = GRBsetintparam(env, "ObjNumber", i - 1)
+        _check_ret(env, ret)
+        push!(f, _get_affine_objective(model; is_multiobjective = true))
+    end
+    return MOI.Utilities.operate(vcat, Float64, f...)
+end
+
+function MOI.supports(
+    model::Optimizer,
+    ::MOI.ObjectiveFunction{MOI.VectorAffineFunction{Float64}},
+)
+    return true
+end

src/MOI_wrapper/MOI_wrapper.jl

@@ -18,7 +18,14 @@ const CleverDicts = MOI.Utilities.CleverDicts
     _INTERVAL,
     _EQUAL_TO
 )
-@enum(_ObjectiveType, _SINGLE_VARIABLE, _SCALAR_AFFINE, _SCALAR_QUADRATIC)
+@enum(
+    _ObjectiveType,
+    _SINGLE_VARIABLE,
+    _SCALAR_AFFINE,
+    _SCALAR_QUADRATIC,
+    _VECTOR_AFFINE,
+)
+
 @enum(
     _CallbackState,
     _CB_NONE,
@@ -1150,19 +1157,11 @@ function MOI.set(
     return
 end
 
-function MOI.get(
-    model::Optimizer,
-    ::MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}},
-)
-    if model.objective_type == _SCALAR_QUADRATIC
-        error(
-            "Unable to get objective function. Currently: " *
-            "$(model.objective_type).",
-        )
-    end
+function _get_affine_objective(model::Optimizer; is_multiobjective::Bool)
     _update_if_necessary(model)
     dest = zeros(length(model.variable_info))
-    ret = GRBgetdblattrarray(model, "Obj", 0, length(dest), dest)
+    name = is_multiobjective ? "ObjN" : "Obj"
+    ret = GRBgetdblattrarray(model, name, 0, length(dest), dest)
     _check_ret(model, ret)
     terms = MOI.ScalarAffineTerm{Float64}[]
     for (index, info) in model.variable_info
@@ -1171,11 +1170,25 @@ function MOI.get(
         push!(terms, MOI.ScalarAffineTerm(coefficient, index))
     end
     constant = Ref{Cdouble}()
-    ret = GRBgetdblattr(model, "ObjCon", constant)
+    name = is_multiobjective ? "ObjNCon" : "ObjCon"
+    ret = GRBgetdblattr(model, name, constant)
     _check_ret(model, ret)
     return MOI.ScalarAffineFunction(terms, constant[])
 end
 
+function MOI.get(
+    model::Optimizer,
+    ::MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}},
+)
+    if model.objective_type == _SCALAR_QUADRATIC
+        error(
+            "Unable to get objective function. Currently: " *
+            "$(model.objective_type).",
+        )
+    end
+    return _get_affine_objective(model; is_multiobjective = false)
+end
+
 function MOI.set(
     model::Optimizer,
     ::MOI.ObjectiveFunction{F},
@@ -3091,6 +3104,10 @@ function MOI.get(model::Optimizer, attr::MOI.ObjectiveValue)
             attr.result_index - 1,
         )
     end
+    N = MOI.get(model, NumberOfObjectives())
+    if N > 1
+        return [MOI.get(model, MultiObjectiveValue(i)) for i in 1:N]
+    end
     valueP = Ref{Cdouble}()
     key = attr.result_index == 1 ? "ObjVal" : "PoolObjVal"
     ret = GRBgetdblattr(model, key, valueP)
@@ -3409,9 +3426,11 @@ function MOI.get(model::Optimizer, ::MOI.ObjectiveFunctionType)
         return MOI.VariableIndex
     elseif model.objective_type == _SCALAR_AFFINE
         return MOI.ScalarAffineFunction{Float64}
-    else
-        @assert model.objective_type == _SCALAR_QUADRATIC
+    elseif model.objective_type == _SCALAR_QUADRATIC
         return MOI.ScalarQuadraticFunction{Float64}
+    else
+        @assert model.objective_type == _VECTOR_AFFINE
+        return MOI.VectorAffineFunction{Float64}
     end
 end
 

test/MOI/MOI_multiobjective.jl

@@ -55,7 +55,7 @@ c4: y >= 0.25
     @test MOI.get(model, Gurobi.MultiObjectivePriority(2)) == 0
 
     MOI.optimize!(model)
-    @test MOI.get(model, MOI.ObjectiveValue()) ≈ 1.5
+    @test MOI.get(model, MOI.ObjectiveValue()) ≈ [1.5, 2.0]
     @test MOI.get(model, MOI.VariablePrimal(), x) ≈ 0.5
     @test MOI.get(model, MOI.VariablePrimal(), y) ≈ 0.5
 
@@ -101,6 +101,37 @@ c4: y >= 0.25
     @test MOI.get(model, Gurobi.MultiObjectiveValue(2)) ≈ BFS[3].f2
 end
 
+function test_example_biobjective_knapsack()
+    p1 = [77.0, 94, 71, 63, 96, 82, 85, 75, 72, 91, 99, 63, 84, 87, 79, 94, 90]
+    p2 = [65.0, 90, 90, 77, 95, 84, 70, 94, 66, 92, 74, 97, 60, 60, 65, 97, 93]
+    w = [80.0, 87, 68, 72, 66, 77, 99, 85, 70, 93, 98, 72, 100, 89, 67, 86, 91]
+    model = Gurobi.Optimizer()
+    x = MOI.add_variables(model, length(w))
+    MOI.add_constraint.(model, x, MOI.ZeroOne())
+    MOI.add_constraint(model, w' * x, MOI.LessThan(900.0))
+    obj_f = MOI.Utilities.operate(vcat, Float64, p1' * x, p2' * x)
+    MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
+    MOI.set(model, MOI.ObjectiveFunction{typeof(obj_f)}(), obj_f)
+    MOI.optimize!(model)
+    results = Dict(
+        [955.0, 906.0] => [2, 3, 5, 6, 9, 10, 11, 14, 15, 16, 17],
+        [948.0, 939.0] => [1, 2, 3, 5, 6, 8, 10, 11, 15, 16, 17],
+        [934.0, 971.0] => [2, 3, 5, 6, 8, 10, 11, 12, 15, 16, 17],
+        [918.0, 983.0] => [2, 3, 4, 5, 6, 8, 10, 11, 12, 16, 17],
+    )
+    found_non_dominated_point = false
+    for i in 1:MOI.get(model, MOI.ResultCount())
+        X = findall(elt -> elt > 0.9, MOI.get.(model, MOI.VariablePrimal(i), x))
+        Y = MOI.get(model, MOI.ObjectiveValue(i))
+        if haskey(results, Y)
+            @test results[Y] == X
+            found_non_dominated_point = true
+        end
+    end
+    @test found_non_dominated_point
+    return
+end
+
 end
 
 TestMultiobjective.runtests()