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Hierarchical.jl
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# Copyright 2019, Oscar Dowson and contributors
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v.2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at http://mozilla.org/MPL/2.0/.
module TestHierarchical
using Test
import HiGHS
import MultiObjectiveAlgorithms as MOA
import MultiObjectiveAlgorithms: MOI
function run_tests()
for name in names(@__MODULE__; all = true)
if startswith("$name", "test_")
@testset "$name" begin
getfield(@__MODULE__, name)()
end
end
end
return
end
function test_sorted_priorities()
@test MOA._sorted_priorities([0, 0, 0]) == [[1, 2, 3]]
@test MOA._sorted_priorities([1, 0, 0]) == [[1], [2, 3]]
@test MOA._sorted_priorities([0, 1, 0]) == [[2], [1, 3]]
@test MOA._sorted_priorities([0, 0, 1]) == [[3], [1, 2]]
@test MOA._sorted_priorities([0, 1, 1]) == [[2, 3], [1]]
@test MOA._sorted_priorities([0, 2, 1]) == [[2], [3], [1]]
return
end
function test_knapsack()
P = Float64[1 0 0 0; 0 1 1 0; 0 0 1 1; 0 1 0 0]
model = MOA.Optimizer(HiGHS.Optimizer)
MOI.set(model, MOA.Algorithm(), MOA.Hierarchical())
@test MOI.supports(model, MOA.ObjectivePriority(1))
MOI.set.(model, MOA.ObjectivePriority.(1:4), [2, 1, 1, 0])
@test MOI.supports(model, MOA.ObjectiveWeight(1))
MOI.set.(model, MOA.ObjectiveWeight.(1:4), [1, 0.5, 0.5, 1])
@test MOI.supports(model, MOA.ObjectiveRelativeTolerance(1))
MOI.set(model, MOA.ObjectiveRelativeTolerance(1), 0.1)
MOI.set(model, MOI.Silent(), true)
x = MOI.add_variables(model, 4)
MOI.add_constraint.(model, x, MOI.GreaterThan(0.0))
MOI.add_constraint.(model, x, MOI.LessThan(1.0))
MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
f = MOI.Utilities.operate(vcat, Float64, P * x...)
f.constants[4] = 1_000.0
MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
MOI.add_constraint(model, sum(1.0 * x[i] for i in 1:4), MOI.LessThan(2.0))
MOI.optimize!(model)
@test MOI.get(model, MOI.ResultCount()) == 1
x_sol = MOI.get(model, MOI.VariablePrimal(), x)
@test ≈(x_sol, [0.9, 0, 0.9, 0.2]; atol = 1e-3)
y_sol = MOI.get(model, MOI.ObjectiveValue())
@test ≈(y_sol, P * x_sol .+ [0.0, 0.0, 0.0, 1_000.0]; atol = 1e-4)
return
end
function test_knapsack_min()
P = Float64[1 0 0 0; 0 1 1 0; 0 0 1 1; 0 1 0 0]
model = MOA.Optimizer(HiGHS.Optimizer)
MOI.set(model, MOA.Algorithm(), MOA.Hierarchical())
MOI.set.(model, MOA.ObjectivePriority.(1:4), [2, 1, 1, 0])
MOI.set.(model, MOA.ObjectiveWeight.(1:4), [1, 0.5, 0.5, 1])
MOI.set(model, MOA.ObjectiveRelativeTolerance(1), 0.1)
MOI.set(model, MOI.Silent(), true)
x = MOI.add_variables(model, 4)
MOI.add_constraint.(model, x, MOI.GreaterThan(0.0))
MOI.add_constraint.(model, x, MOI.LessThan(1.0))
MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
f = MOI.Utilities.operate(vcat, Float64, -P * x...)
MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
MOI.add_constraint(model, sum(1.0 * x[i] for i in 1:4), MOI.LessThan(2.0))
MOI.optimize!(model)
x_sol = MOI.get(model, MOI.VariablePrimal(), x)
@test ≈(x_sol, [0.9, 0, 0.9, 0.2]; atol = 1e-3)
return
end
function test_infeasible()
model = MOA.Optimizer(HiGHS.Optimizer)
MOI.set(model, MOA.Algorithm(), MOA.Hierarchical())
MOI.set(model, MOI.Silent(), true)
x = MOI.add_variables(model, 2)
MOI.add_constraint.(model, x, MOI.GreaterThan(0.0))
MOI.add_constraint(model, 1.0 * x[1] + 1.0 * x[2], MOI.LessThan(-1.0))
f = MOI.Utilities.operate(vcat, Float64, 1.0 .* x...)
MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == MOI.INFEASIBLE
@test MOI.get(model, MOI.PrimalStatus()) == MOI.NO_SOLUTION
@test MOI.get(model, MOI.DualStatus()) == MOI.NO_SOLUTION
return
end
function test_unbounded()
model = MOA.Optimizer(HiGHS.Optimizer)
MOI.set(model, MOA.Algorithm(), MOA.Hierarchical())
MOI.set(model, MOI.Silent(), true)
x = MOI.add_variables(model, 2)
MOI.add_constraint.(model, x, MOI.GreaterThan(0.0))
f = MOI.Utilities.operate(vcat, Float64, 1.0 .* x...)
MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == MOI.DUAL_INFEASIBLE
@test MOI.get(model, MOI.PrimalStatus()) == MOI.NO_SOLUTION
@test MOI.get(model, MOI.DualStatus()) == MOI.NO_SOLUTION
return
end
function test_vector_of_variables_objective()
model = MOI.instantiate(; with_bridge_type = Float64) do
return MOA.Optimizer(HiGHS.Optimizer)
end
MOI.set(model, MOA.Algorithm(), MOA.Hierarchical())
MOI.set(model, MOI.Silent(), true)
x = MOI.add_variables(model, 2)
MOI.add_constraint.(model, x, MOI.ZeroOne())
f = MOI.VectorOfVariables(x)
MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
MOI.add_constraint(model, sum(1.0 * xi for xi in x), MOI.GreaterThan(1.0))
MOI.optimize!(model)
MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
return
end
end
TestHierarchical.run_tests()