Add tests for solve failures in Chalmet

src/algorithms/Chalmet.jl

@@ -67,6 +67,10 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
         MOI.LessThan(y1[2]),
     )
     MOI.optimize!(model.inner)
+    status = MOI.get(model.inner, MOI.TerminationStatus())
+    if !_is_scalar_status_optimal(status)
+        return status, nothing
+    end
     x1, y1[1] = _compute_point(model, variables, f1)
     MOI.delete(model.inner, y1_constraint)
     push!(solutions, SolutionPoint(x1, y1))
@@ -87,6 +91,10 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
         MOI.LessThan(y2[1]),
     )
     MOI.optimize!(model.inner)
+    status = MOI.get(model.inner, MOI.TerminationStatus())
+    if !_is_scalar_status_optimal(status)
+        return status, nothing
+    end
     x2, y2[2] = _compute_point(model, variables, f2)
     MOI.delete(model.inner, y2_constraint)
     push!(solutions, SolutionPoint(x2, y2))

test/algorithms/Chalmet.jl

@@ -11,6 +11,8 @@ import HiGHS
 import MultiObjectiveAlgorithms as MOA
 import MultiObjectiveAlgorithms: MOI
 
+include(joinpath(dirname(@__DIR__), "mock_optimizer.jl"))
+
 function run_tests()
     for name in names(@__MODULE__; all = true)
         if startswith("$name", "test_")
@@ -253,6 +255,34 @@ function test_single_point()
     return
 end
 
+function test_solve_failures()
+    m, n = 2, 10
+    p1 = [5.0 1 10 8 3 5 3 3 7 2; 10 6 1 6 8 3 2 10 6 1]
+    p2 = [4.0 6 4 3 1 6 8 2 9 7; 8 8 8 2 4 8 8 1 10 1]
+    w = [5.0 9 3 5 10 5 7 10 7 8; 4 8 8 6 10 8 10 7 5 1]
+    b = [34.0, 33.0]
+    for fail_after in 0:3
+        model = MOA.Optimizer(mock_optimizer(fail_after))
+        MOI.set(model, MOA.Algorithm(), MOA.Chalmet())
+        x_ = MOI.add_variables(model, m * n)
+        x = reshape(x_, m, n)
+        MOI.add_constraint.(model, x, MOI.Interval(0.0, 1.0))
+        f = MOI.Utilities.operate(vcat, Float64, sum(p1 .* x), sum(p2 .* x))
+        MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
+        MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
+        for i in 1:m
+            f_i = sum(w[i, j] * x[i, j] for j in 1:n)
+            MOI.add_constraint(model, f_i, MOI.LessThan(b[i]))
+        end
+        for j in 1:n
+            MOI.add_constraint(model, sum(1.0 .* x[:, j]), MOI.EqualTo(1.0))
+        end
+        MOI.optimize!(model)
+        @test MOI.get(model, MOI.TerminationStatus()) == MOI.NUMERICAL_ERROR
+    end
+    return
+end
+
 end  # module TestChalmet
 
 TestChalmet.run_tests()

test/algorithms/Dichotomy.jl

@@ -12,6 +12,8 @@ import Ipopt
 import MultiObjectiveAlgorithms as MOA
 import MultiObjectiveAlgorithms: MOI
 
+include(joinpath(dirname(@__DIR__), "mock_optimizer.jl"))
+
 function run_tests()
     for name in names(@__MODULE__; all = true)
         if startswith("$name", "test_")
@@ -407,6 +409,34 @@ function test_vector_of_variables_objective()
     return
 end
 
+function test_solve_failures()
+    m, n = 2, 10
+    p1 = [5.0 1 10 8 3 5 3 3 7 2; 10 6 1 6 8 3 2 10 6 1]
+    p2 = [4.0 6 4 3 1 6 8 2 9 7; 8 8 8 2 4 8 8 1 10 1]
+    w = [5.0 9 3 5 10 5 7 10 7 8; 4 8 8 6 10 8 10 7 5 1]
+    b = [34.0, 33.0]
+    for fail_after in 0:3
+        model = MOA.Optimizer(mock_optimizer(fail_after))
+        MOI.set(model, MOA.Algorithm(), MOA.Dichotomy())
+        x_ = MOI.add_variables(model, m * n)
+        x = reshape(x_, m, n)
+        MOI.add_constraint.(model, x, MOI.Interval(0.0, 1.0))
+        f = MOI.Utilities.operate(vcat, Float64, sum(p1 .* x), sum(p2 .* x))
+        MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
+        MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
+        for i in 1:m
+            f_i = sum(w[i, j] * x[i, j] for j in 1:n)
+            MOI.add_constraint(model, f_i, MOI.LessThan(b[i]))
+        end
+        for j in 1:n
+            MOI.add_constraint(model, sum(1.0 .* x[:, j]), MOI.EqualTo(1.0))
+        end
+        MOI.optimize!(model)
+        @test MOI.get(model, MOI.TerminationStatus()) == MOI.NUMERICAL_ERROR
+    end
+    return
+end
+
 end  # module TestDichotomy
 
 TestDichotomy.run_tests()

test/algorithms/EpsilonConstraint.jl

@@ -12,6 +12,7 @@ import Ipopt
 import MultiObjectiveAlgorithms as MOA
 import MultiObjectiveAlgorithms: MOI
 
+include(joinpath(dirname(@__DIR__), "mock_optimizer.jl"))
 include(joinpath(dirname(@__DIR__), "vOptLib.jl"))
 
 function run_tests()
@@ -502,6 +503,34 @@ function test_too_many_objectives()
     return
 end
 
+function test_solve_failures()
+    m, n = 2, 10
+    p1 = [5.0 1 10 8 3 5 3 3 7 2; 10 6 1 6 8 3 2 10 6 1]
+    p2 = [4.0 6 4 3 1 6 8 2 9 7; 8 8 8 2 4 8 8 1 10 1]
+    w = [5.0 9 3 5 10 5 7 10 7 8; 4 8 8 6 10 8 10 7 5 1]
+    b = [34.0, 33.0]
+    for fail_after in 0:3
+        model = MOA.Optimizer(mock_optimizer(fail_after))
+        MOI.set(model, MOA.Algorithm(), MOA.EpsilonConstraint())
+        x_ = MOI.add_variables(model, m * n)
+        x = reshape(x_, m, n)
+        MOI.add_constraint.(model, x, MOI.Interval(0.0, 1.0))
+        f = MOI.Utilities.operate(vcat, Float64, sum(p1 .* x), sum(p2 .* x))
+        MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
+        MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
+        for i in 1:m
+            f_i = sum(w[i, j] * x[i, j] for j in 1:n)
+            MOI.add_constraint(model, f_i, MOI.LessThan(b[i]))
+        end
+        for j in 1:n
+            MOI.add_constraint(model, sum(1.0 .* x[:, j]), MOI.EqualTo(1.0))
+        end
+        MOI.optimize!(model)
+        @test MOI.get(model, MOI.TerminationStatus()) == MOI.NUMERICAL_ERROR
+    end
+    return
+end
+
 end  # module TestEpsilonConstraint
 
 TestEpsilonConstraint.run_tests()

test/mock_optimizer.jl

@@ -0,0 +1,32 @@
+#  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/.
+
+function _solve_mock(mock)
+    highs = HiGHS.Optimizer()
+    MOI.set(highs, MOI.Silent(), true)
+    index_map = MOI.copy_to(highs, mock)
+    MOI.optimize!(highs)
+    x = [index_map[xi] for xi in MOI.get(mock, MOI.ListOfVariableIndices())]
+    MOI.Utilities.mock_optimize!(
+        mock,
+        MOI.get(highs, MOI.TerminationStatus()),
+        MOI.get(highs, MOI.VariablePrimal(), x),
+    )
+    obj = MOI.get(highs, MOI.ObjectiveValue())
+    MOI.set(mock, MOI.ObjectiveValue(), obj)
+    return
+end
+
+function mock_optimizer(fail_after::Int)
+    return () -> begin
+        model = MOI.Utilities.MockOptimizer(MOI.Utilities.Model{Float64}())
+        MOI.Utilities.set_mock_optimize!(
+            model,
+            ntuple(i -> _solve_mock, fail_after)...,
+            mock -> MOI.Utilities.mock_optimize!(mock, MOI.NUMERICAL_ERROR),
+        )
+        return model
+    end
+end