Add support for interrupting during the solve loops

Author: odow

src/MultiObjectiveAlgorithms.jl

@@ -644,7 +644,24 @@ function optimize_multiobjective!(
     return minimize_multiobjective!(algorithm, model)
 end
 
+function _check_interrupt()
+    try
+        reenable_sigint(() -> nothing)
+    catch ex
+        if ex isa InterruptException
+            return true
+        end
+        rethrow(ex)
+    end
+    return false
+end
+
 function MOI.optimize!(model::Optimizer)
+    disable_sigint(() -> _optimize!(model))
+    return
+end
+
+function _optimize!(model::Optimizer)
     start_time = time()
     empty!(model.solutions)
     model.termination_status = MOI.OPTIMIZE_NOT_CALLED

src/algorithms/Chalmet.jl

@@ -57,7 +57,7 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     optimize_inner!(model)
     status = MOI.get(model.inner, MOI.TerminationStatus())
     if !_is_scalar_status_optimal(status)
-        return status, nothing
+        return status, solutions
     end
     _, y1[2] = _compute_point(model, variables, f2)
     MOI.set(model.inner, MOI.ObjectiveFunction{typeof(f1)}(), f1)
@@ -69,7 +69,7 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     optimize_inner!(model)
     status = MOI.get(model.inner, MOI.TerminationStatus())
     if !_is_scalar_status_optimal(status)
-        return status, nothing
+        return status, solutions
     end
     x1, y1[1] = _compute_point(model, variables, f1)
     MOI.delete(model.inner, y1_constraint)
@@ -78,7 +78,7 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     optimize_inner!(model)
     status = MOI.get(model.inner, MOI.TerminationStatus())
     if !_is_scalar_status_optimal(status)
-        return status, nothing
+        return status, solutions
     end
     _, y2[1] = _compute_point(model, variables, f1)
     if y2[1] ≈ solutions[1].y[1]
@@ -93,7 +93,7 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     optimize_inner!(model)
     status = MOI.get(model.inner, MOI.TerminationStatus())
     if !_is_scalar_status_optimal(status)
-        return status, nothing
+        return status, solutions
     end
     x2, y2[2] = _compute_point(model, variables, f2)
     MOI.delete(model.inner, y2_constraint)
@@ -103,6 +103,8 @@ function minimize_multiobjective!(algorithm::Chalmet, model::Optimizer)
     while !isempty(Q)
         if _time_limit_exceeded(model, start_time)
             return MOI.TIME_LIMIT, solutions
+        elseif _check_interrupt()
+            return MOI.INTERRUPTED, solutions
         end
         r, s = pop!(Q)
         yr, ys = solutions[r].y, solutions[s].y

src/algorithms/Dichotomy.jl

@@ -79,6 +79,8 @@ function optimize_multiobjective!(algorithm::Dichotomy, model::Optimizer)
     if MOI.output_dimension(model.f) == 1
         if _time_limit_exceeded(model, start_time)
             return MOI.TIME_LIMIT, nothing
+        elseif _check_interrupt()
+            return MOI.INTERRUPTED, nothing
         end
         status, solution = _solve_weighted_sum(model, algorithm, [1.0])
         return status, [solution]
@@ -87,6 +89,8 @@ function optimize_multiobjective!(algorithm::Dichotomy, model::Optimizer)
     for (i, w) in (1 => 1.0, 2 => 0.0)
         if _time_limit_exceeded(model, start_time)
             return MOI.TIME_LIMIT, nothing
+        elseif _check_interrupt()
+            return MOI.INTERRUPTED, nothing
         end
         status, solution = _solve_weighted_sum(model, algorithm, [w, 1.0 - w])
         if !_is_scalar_status_optimal(status)
@@ -106,14 +110,16 @@ function optimize_multiobjective!(algorithm::Dichotomy, model::Optimizer)
         if _time_limit_exceeded(model, start_time)
             status = MOI.TIME_LIMIT
             break
+        elseif _check_interrupt()
+            status = MOI.INTERRUPTED
+            break
         end
         (a, b) = popfirst!(queue)
         y_d = solutions[a].y .- solutions[b].y
         w = y_d[2] / (y_d[2] - y_d[1])
         status, solution = _solve_weighted_sum(model, algorithm, [w, 1.0 - w])
         if !_is_scalar_status_optimal(status)
-            # Exit the solve with some error.
-            return status, nothing
+            break # Exit the solve with some error.
         elseif solution ≈ solutions[a] || solution ≈ solutions[b]
             # We have found an existing solution. We're free to prune (a, b)
             # from the search space.

src/algorithms/DominguezRios.jl

@@ -186,6 +186,9 @@ function minimize_multiobjective!(algorithm::DominguezRios, model::Optimizer)
         if _time_limit_exceeded(model, start_time)
             status = MOI.TIME_LIMIT
             break
+        elseif _check_interrupt()
+            status = MOI.INTERRUPTED
+            break
         end
         i, k = _select_next_box(L, k)
         B = L[k][i]

src/algorithms/EpsilonConstraint.jl

@@ -114,6 +114,9 @@ function minimize_multiobjective!(
         if _time_limit_exceeded(model, start_time)
             status = MOI.TIME_LIMIT
             break
+        elseif _check_interrupt()
+            status = MOI.INTERRUPTED
+            break
         end
         MOI.set(model, MOI.ConstraintSet(), ci, MOI.LessThan{Float64}(bound))
         optimize_inner!(model)

src/algorithms/KirlikSayin.jl

@@ -118,7 +118,11 @@ function minimize_multiobjective!(algorithm::KirlikSayin, model::Optimizer)
     status = MOI.OPTIMAL
     while !isempty(L)
         if _time_limit_exceeded(model, start_time)
-            return MOI.TIME_LIMIT, solutions
+            status = MOI.TIME_LIMIT
+            break
+        elseif _check_interrupt()
+            status = MOI.INTERRUPTED
+            break
         end
         max_volume_index = argmax([_volume(Rᵢ, _project(yI, k)) for Rᵢ in L])
         uᵢ = L[max_volume_index].u

src/algorithms/Lexicographic.jl

@@ -127,6 +127,9 @@ function _solve_in_sequence(
         if _time_limit_exceeded(model, start_time)
             status = MOI.TIME_LIMIT
             break
+        elseif _check_interrupt()
+            status = MOI.INTERRUPTED
+            break
         end
         f = scalars[i]
         MOI.set(model.inner, MOI.ObjectiveFunction{typeof(f)}(), f)

src/algorithms/RandomWeighting.jl

@@ -62,6 +62,8 @@ function optimize_multiobjective!(algorithm::RandomWeighting, model::Optimizer)
         while length(solutions) < MOI.get(algorithm, SolutionLimit())
             if _time_limit_exceeded(model, start_time)
                 return MOI.TIME_LIMIT, filter_nondominated(sense, solutions)
+            elseif _check_interrupt()
+                return MOI.INTERRUPTED, filter_nondominated(sense, solutions)
             end
             weights = rand(P)
             f = _scalarise(model.f, weights)

src/algorithms/TambyVanderpooten.jl

@@ -126,6 +126,9 @@ function minimize_multiobjective!(
         if _time_limit_exceeded(model, start_time)
             status = MOI.TIME_LIMIT
             break
+        elseif _check_interrupt()
+            status = MOI.INTERRUPTED
+            break
         end
         k, u = _select_search_zone(U_N, yI, yN)
         MOI.set(