collect_gmi_dual: profile, do not filter at the end

src/Cuts/tableau/gmi_dual.jl

@@ -32,57 +32,43 @@ function collect_gmi_dual(
 )
     reset_timer!()
 
-    # Open HDF5 file
+    @timeit "Read H5" begin
         h5_filename = replace(mps_filename, ".mps.gz" => ".h5")
         h5 = H5File(h5_filename)
-
-    # Read optimal solution
         sol_opt_dict = Dict(
             zip(
                 h5.get_array("static_var_names"),
                 convert(Array{Float64}, h5.get_array("mip_var_values")),
             ),
         )
-
-    # Read optimal value
-    obj_mip = h5.get_scalar("mip_lower_bound")
-    if obj_mip === nothing
         obj_mip = h5.get_scalar("mip_obj_value")
-    end
-    obj_lp = h5.get_scalar("lp_obj_value")
         h5.file.close()
+    end
 
     # Define relative MIP gap
     gap(v) = 100 * abs(obj_mip - v) / abs(v)
 
-    # Initialize stats
+    @timeit "Initialize" begin
         stats_obj = []
         stats_gap = []
         stats_ncuts = []
-    stats_time_convert = 0
-    stats_time_solve = 0
-    stats_time_select = 0
-    stats_time_tableau = 0
-    stats_time_gmi = 0
-    stats_time_dual = 0
-    stats_time_dual_2 = 0
         all_cuts = nothing
         all_cuts_v2 = nothing
         cuts_all = nothing
         cuts_all_v2 = nothing
         original_basis = nothing
+    end
 
-    # Read problem
+    @timeit "Read problem" begin
         model = read_from_file(mps_filename)
-
-    # Read original objective function
         or_obj_f = objective_function(model)
         revised_obj = objective_function(model)
+    end
 
     for round = 1:max_rounds
         @info "Round $(round)..."
 
-        stats_time_convert = @elapsed begin
+        @timeit "Convert to standard form" begin
             # Update objective function
             set_objective_function(model, revised_obj)
 
@@ -107,51 +93,40 @@ function collect_gmi_dual(
 
             # Assert converted solution is feasible for standard form problem
             assert_eq(data_s.constr_lhs * sol_opt_s, data_s.constr_lb)
-
         end
 
-        # Optimize standard form
+        @timeit "Optimize standard form" begin
             optimize!(model_s)
-        stats_time_solve += solve_time(model_s)
-        obj = objective_value(model_s) + data_s.obj_offset
-
             if round == 1
-            # Assert standard form problem has same value as original
+                obj = objective_value(model_s) + data_s.obj_offset
-            if obj_lp !== nothing
-                assert_eq(obj, obj_lp)
-            end
-            obj_lp = obj
                 push!(stats_obj, obj)
                 push!(stats_gap, gap(obj))
                 push!(stats_ncuts, 0)
             end
             if termination_status(model_s) != MOI.OPTIMAL
-            return
+                error("Non-optimal termination status")
+            end
         end
 
-        # Store original basis and select tableau rows
+        @timeit "Select tableau rows" begin
             basis = get_basis(model_s)
             if round == 1
                 original_basis = basis
             end
             sol_frac = get_x(model_s)
-        stats_time_select += @elapsed begin
             selected_rows =
                 select_gmi_rows(data_s, basis, sol_frac, max_rows = max_cuts_per_round)
         end
 
-        # Compute selected tableau rows
+        @timeit "Compute tableau rows" begin
-        stats_time_tableau += @elapsed begin
             tableau = compute_tableau(data_s, basis, x = sol_frac, rows = selected_rows)
 
             # Assert tableau rows have been computed correctly
             assert_eq(tableau.lhs * sol_frac, tableau.rhs)
             assert_eq(tableau.lhs * sol_opt_s, tableau.rhs)
-
         end
 
-        # Compute GMI cuts
+        @timeit "Compute GMI cuts" begin
-        stats_time_gmi += @elapsed begin
             cuts_s = compute_gmi(data_s, tableau)
 
             # Assert cuts have been generated correctly
@@ -165,7 +140,7 @@ function collect_gmi_dual(
             end
         end
 
-        # Add GMI cuts to original problem
+        @timeit "Add GMI cuts to original problem" begin
             cuts = backwards(transforms, cuts_s)
             if round == 1
                 cuts_all = cuts
@@ -186,8 +161,9 @@ function collect_gmi_dual(
                 cuts_all_v2.Bv = [cuts_all_v2.Bv; selected_rows]
             end
             constrs, gmi_exps = add_constraint_set_dual_v2(model, cuts_all)
+        end
 
-        # Optimize original form
+        @timeit "Optimize original form" begin
             set_objective_function(model, or_obj_f)
             set_optimizer(model, optimizer)
             undo_relax = relax_integrality(model)
@@ -195,9 +171,9 @@ function collect_gmi_dual(
             obj = objective_value(model)
             push!(stats_obj, obj)
             push!(stats_gap, gap(obj))
+        end
 
-        # Reoptimize with updated obj function
+        @timeit "Reoptimize with updated obj function" begin
-        stats_time_dual += @elapsed begin
             revised_obj = (
                 or_obj_f - sum(
                     shadow_price(c) * gmi_exps[iz] for (iz, c) in enumerate(constrs)
@@ -213,54 +189,14 @@ function collect_gmi_dual(
         undo_relax()
     end
 
-    # Filter out useless cuts
+    @timeit "Store cuts" begin
-    stats_time_dual_2 += @elapsed begin
-        set_objective_function(model, or_obj_f)
-        keep = []
-        obj_gmi = obj_lp
-        if (cuts_all !== nothing)
-            constrs, gmi_exps = add_constraint_set_dual_v2(model, cuts_all)
-            for (i, c) in enumerate(constrs)
-                set_name(c, @sprintf("gomory_%05d", i))
-            end
-            set_optimizer(model, optimizer)
-            undo_relax = relax_integrality(model)
-            optimize!(model)
-            obj = objective_value(model)
-            obj_gmi = obj
-            push!(stats_obj, obj)
-            push!(stats_gap, gap(obj))
-
-            # Store useful cuts; drop useless ones from the problem
-            useful = [-shadow_price(c) > 1e-3 for c in constrs]
-            drop = findall(useful .== false)
-            keep = findall(useful .== true)
-            all_cuts = ConstraintSet(;
-                lhs = cuts_all.lhs[keep, :],
-                lb = cuts_all.lb[keep],
-                ub = cuts_all.ub[keep],
-            )
-            all_cuts_v2 = ConstraintSet_v2(;
-                lhs = cuts_all_v2.lhs[keep, :],
-                lb = cuts_all_v2.lb[keep],
-                ub = cuts_all_v2.ub[keep],
-                Bss = cuts_all_v2.Bss[keep],
-                Bv = cuts_all_v2.Bv[keep],
-            )
-
-            delete.(model, constrs[drop])
-            undo_relax()
-        end
-    end
-    basis = original_basis
-
         if all_cuts !== nothing
             cut_sizezz = length(all_cuts_v2.Bv)
             var_totall =
-            length(basis.var_basic) +
+                length(original_basis.var_basic) +
-            length(basis.var_nonbasic) +
+                length(original_basis.var_nonbasic) +
-            length(basis.constr_basic) +
+                length(original_basis.constr_basic) +
-            length(basis.constr_nonbasic)
+                length(original_basis.constr_nonbasic)
             bm_size = Array{Int64,2}(undef, cut_sizezz, 4)
             basis_matrix = Array{Int64,2}(undef, cut_sizezz, var_totall)
 
@@ -273,7 +209,6 @@ function collect_gmi_dual(
                 basis_matrix[ii, :] = [vb' vn' cb' cn']
             end
 
-        # Store cuts
             @info "Storing $(length(all_cuts.ub)) GMI cuts..."
             h5 = H5File(h5_filename)
             h5.put_sparse("cuts_lhs", all_cuts.lhs)
@@ -284,22 +219,17 @@ function collect_gmi_dual(
             h5.put_array("cuts_rows", all_cuts_v2.Bv)
             h5.file.close()
         end
+    end
+
+    print_timer()
 
     return OrderedDict(
         "instance" => mps_filename,
         "max_rounds" => max_rounds,
         "rounds" => length(stats_obj) - 1,
-        "time_convert" => stats_time_convert,
-        "time_solve" => stats_time_solve,
-        "time_tableau" => stats_time_tableau,
-        "time_gmi" => stats_time_gmi,
-        "time_dual" => stats_time_dual,
-        "time_dual_2" => stats_time_dual_2,
         "obj_mip" => obj_mip,
-        "obj_lp" => obj_lp,
         "stats_obj" => stats_obj,
         "stats_gap" => stats_gap,
-        "stats_ncuts" => length(keep),
     )
 end