Update DominguezRios to delete boxes that are already searched.

src/algorithms/DominguezRios.jl

@@ -148,6 +148,10 @@ function _update!(
     return
 end
 
+_isapprox(::Nothing, ::_DominguezRiosBox) = false
+
+_isapprox(A::_DominguezRiosBox, B::_DominguezRiosBox) = A.l ≈ B.l && A.u ≈ B.u
+
 function minimize_multiobjective!(algorithm::DominguezRios, model::Optimizer)
     @assert MOI.get(model.inner, MOI.ObjectiveSense()) == MOI.MIN_SENSE
     n = MOI.output_dimension(model.f)
@@ -190,13 +194,22 @@ function minimize_multiobjective!(algorithm::DominguezRios, model::Optimizer)
     solutions = SolutionPoint[]
     k = 0
     status = MOI.OPTIMAL
+    B_prev = Vector{Union{Nothing,_DominguezRiosBox}}(nothing, n)
     while any(!isempty(l) for l in L)
         if (ret = _check_premature_termination(model)) !== nothing
             status = ret
             break
         end
         i, k = _select_next_box(L, k)
         B = L[k][i]
+        # We check for the repeated search of similar boxes in the same
+        # optimization direction. If the same box was searched before, we delete
+        # it from the list of boxes.
+        if _isapprox(B_prev[k], B)
+            deleteat!(L[k], i)
+            continue
+        end
+        B_prev[k] = B
         # We're going to scale `w` here by `scale` instead of the usual
         # `1 / max(...)`. It will show up in a few places bbelow.
         w = scale ./ max.(1, B.u - yI)

test/algorithms/DominguezRios.jl

@@ -152,6 +152,25 @@ function test_vector_of_variables_objective()
     return
 end
 
+function test_lp()
+    model = MOI.instantiate(; with_bridge_type = Float64) do
+        return MOA.Optimizer(HiGHS.Optimizer)
+    end
+    MOI.set(model, MOA.Algorithm(), MOA.DominguezRios())
+    MOI.set(model, MOI.Silent(), true)
+    x = MOI.add_variables(model, 2)
+    MOI.add_constraint(model, x[1], MOI.GreaterThan(0.0))
+    MOI.add_constraint(model, x[2], MOI.Interval(0.0, 3.0))
+    MOI.add_constraint(model, 3.0 * x[1] - 1.0 * x[2], MOI.LessThan(6.0))
+    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
+    f = MOI.Utilities.vectorize([3.0 1.0; -1.0 -2.0] * x)
+    MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
+    MOI.optimize!(model)
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
+    @test MOI.get(model, MOI.ResultCount()) > 1
+    return
+end
+
 end  # module TestDominguezRios
 
 TestDominguezRios.run_tests()