Update

Author: odow

src/algorithms/DominguezRios.jl

@@ -207,27 +207,26 @@ function minimize_multiobjective!(algorithm::DominguezRios, model::Optimizer)
         new_f = t_max + ϵ * sum(w[i] * (scalars[i] - yI[i]) for i in 1:n)
         MOI.set(model.inner, MOI.ObjectiveFunction{typeof(new_f)}(), new_f)
         MOI.optimize!(model.inner)
-        if !_is_scalar_status_optimal(model)
+        if _is_scalar_status_optimal(model)
+            X, Y = _compute_point(model, variables, model.f)
+            obj = MOI.get(model.inner, MOI.ObjectiveValue())
+            # We need to undo the scaling of the scalar objective. There's no
+            # need to unscale `Y` because we have evaluated this explicitly from
+            # the modified `model.f`.
+            obj /= scale
+            if (obj < 1) && all(yI .< B.u)
+                push!(solutions, SolutionPoint(X, Y))
+                _update!(L, Y, yI, yN)
+            else
+                deleteat!(L[k], i)
+            end
+        else
             # In theory, this shouldn't happen, because this subproblem is meant
             # to always be feasible. However, in some of our testing, HiGHS will
             # fail and return something like OTHER_ERROR (e.g., because the
             # numerics are challenging). Rather than error completely, let's
             # just skip this box.
             deleteat!(L[k], i)
-            MOI.delete.(model.inner, constraints)
-            continue
-        end
-        X, Y = _compute_point(model, variables, model.f)
-        obj = MOI.get(model.inner, MOI.ObjectiveValue())
-        # We need to undo the scaling of the scalar objective. There's no
-        # need to unscale `Y` because we have evaluated this explicitly from
-        # the modified `model.f`.
-        obj /= scale
-        if (obj < 1) && all(yI .< B.u)
-            push!(solutions, SolutionPoint(X, Y))
-            _update!(L, Y, yI, yN)
-        else
-            deleteat!(L[k], i)
         end
         MOI.delete.(model.inner, constraints)
     end