Use UpperBoundRef and LowerBoundRef; relax int vars

test/bb/lp_test.jl

@@ -51,12 +51,7 @@ function runtests(optimizer_name, optimizer; large = true)
             @test round(obj, digits = 6) == 62.714777
 
             # Probe (up is infeasible, down is feasible)
-            BB.set_bounds!(
-                mip,
-                mip.binary_variables[1:3],
-                [1.0, 1.0, 0.0],
-                [1.0, 1.0, 1.0],
-            )
+            BB.set_bounds!(mip, mip.binary_variables[1:3], [1.0, 1.0, 0.0], [1.0, 1.0, 1.0])
             status, obj = BB.solve_relaxation!(mip)
             @test status == :Optimal
             probe_up, probe_down = BB.probe(mip, mip.binary_variables[3])

test/instance/file_instance_test.jl

@@ -7,14 +7,25 @@ using MIPLearn
 using Cbc
 
 @testset "FileInstance" begin
-    @testset "Solve" begin
+    @testset "Solve (knapsack)" begin
         data = KnapsackData()
         filename = tempname()
         MIPLearn.save_data(filename, data)
         instance = FileInstance(filename, build_knapsack_model)
         solver = LearningSolver(Cbc.Optimizer)
         solve!(solver, instance)
+        h5 = Hdf5Sample("$filename.h5")
+        @test h5.get_scalar("mip_wallclock_time") > 0
+    end
 
+    @testset "Solve (vpm2)" begin
+        data = Dict("filename" => joinpath(@__DIR__, "../fixtures/danoint.mps.gz"))
+        build_model(data) = read_from_file(data["filename"])
+        filename = tempname()
+        MIPLearn.save_data(filename, data)
+        instance = FileInstance(filename, build_model)
+        solver = LearningSolver(optimizer_with_attributes(Cbc.Optimizer, "seconds" => 1.0))
+        solve!(solver, instance)
         h5 = Hdf5Sample("$filename.h5")
         @test h5.get_scalar("mip_wallclock_time") > 0
     end