Improve code coverage

src/algorithms/TambyVanderpooten.jl

@@ -158,7 +158,8 @@ function minimize_multiobjective!(
             end
         end
         optimize_inner!(model)
-        if !_is_scalar_status_optimal(model)
+        status = MOI.get(model.inner, MOI.TerminationStatus())
+        if !_is_scalar_status_optimal(status)
             MOI.delete.(model, ε_constraints)
             return status, nothing
         end
@@ -171,7 +172,8 @@ function minimize_multiobjective!(
             MOI.EqualTo(y_k),
         )
         optimize_inner!(model)
-        if !_is_scalar_status_optimal(model)
+        status = MOI.get(model.inner, MOI.TerminationStatus())
+        if !_is_scalar_status_optimal(status)
             MOI.delete.(model, ε_constraints)
             MOI.delete(model, y_k_constraint)
             return status, nothing

test/algorithms/Lexicographic.jl

@@ -49,6 +49,33 @@ function test_knapsack()
     return
 end
 
+function test_caching_optimizer_knapsack()
+    model = MOI.instantiate(
+        () -> MOA.Optimizer(HiGHS.Optimizer);
+        with_cache_type = Float64,
+    )
+    P = Float64[1 0 0 0; 0 1 0 0; 0 0 0 1; 0 0 1 0]
+    MOI.set(model, MOA.Algorithm(), MOA.Lexicographic())
+    @test MOI.supports(model, MOA.LexicographicAllPermutations())
+    @test MOI.supports(model, MOA.ObjectiveRelativeTolerance(1))
+    MOI.set(model, MOA.LexicographicAllPermutations(), false)
+    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, 1, 0, 0.1]; atol = 1e-3)
+    return
+end
+
 function test_knapsack_default()
     P = Float64[1 0 0 0; 0 1 0 0; 0 0 0 1]
     model = MOA.Optimizer(HiGHS.Optimizer)

test/algorithms/TambyVanderpooten.jl

@@ -11,6 +11,7 @@ import HiGHS
 import MultiObjectiveAlgorithms as MOA
 import MultiObjectiveAlgorithms: MOI
 
+include(joinpath(dirname(@__DIR__), "mock_optimizer.jl"))
 include(joinpath(dirname(@__DIR__), "problems.jl"))
 include(joinpath(dirname(@__DIR__), "vOptLib.jl"))
 
@@ -146,6 +147,35 @@ 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 0:5
+        model = MOA.Optimizer(mock_optimizer(fail_after))
+        MOI.set(model, MOA.Algorithm(), MOA.TambyVanderpooten())
+        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
+        @test MOI.get(model, MOI.ResultCount()) == 0
+    end
+    return
+end
+
 end  # module TestTambyVanderpooten
 
 TestTambyVanderpooten.run_tests()