DualGMI: Skip empty H5 files

Project.toml

@@ -1,7 +1,7 @@
 name = "MIPLearn"
 uuid = "2b1277c3-b477-4c49-a15e-7ba350325c68"
 authors = ["Alinson S Xavier <git@axavier.org>"]
-version = "0.4.0"
+version = "0.4.2"
 
 [deps]
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
@@ -41,3 +41,5 @@ Requires = "1"
 Statistics = "1"
 TimerOutputs = "0.5"
 julia = "1"
+PrecompileTools = "1"
+SCIP = "0.12"

deps/build.jl

@@ -5,7 +5,7 @@ function install_miplearn()
     Conda.update()
     pip = joinpath(dirname(pyimport("sys").executable), "pip")
     isfile(pip) || error("$pip: invalid path")
-    run(`$pip install miplearn==0.4.0`)
+    run(`$pip install miplearn==0.4.4`)
 end
 
 install_miplearn()

src/BB/log.jl

@@ -6,7 +6,7 @@ using Printf
 
 function print_progress_header()
     @printf(
-        "%8s %9s %9s %13s %13s %9s %6s %13s %6s %-24s %9s %9s %6s %6s",
+        "%8s %9s %9s %13s %13s %9s %9s %13s %9s %-24s %9s %9s %6s %6s",
         "time",
         "processed",
         "pending",
@@ -46,7 +46,7 @@ function print_progress(
             branch_ub = @sprintf("%9.2f", last(node.branch_ub))
         end
         @printf(
-            "%8.2f %9d %9d %13.6e %13.6e %9.2e %6d %13.6e %6s %-24s %9s %9s %6d %6d",
+            "%8.2f %9d %9d %13.6e %13.6e %9.2e %9d %13.6e %9s %-24s %9s %9s %6d %6d",
             time_elapsed,
             pool.processed,
             length(pool.processing) + length(pool.pending),

src/BB/lp.jl

@@ -134,7 +134,11 @@ function _get_int_variables(
                 var_ub = constr.upper
                 MOI.delete(optimizer, _upper_bound_index(var))
             end
-            MOI.add_constraint(optimizer, var, MOI.Interval(var_lb, var_ub))
+            MOI.add_constraint(
+                optimizer,
+                MOI.VariableIndex(var.index),
+                MOI.Interval(var_lb, var_ub),
+            )
         end
         push!(vars, var)
         push!(lb, var_lb)

src/Cuts/tableau/gmi_dual.jl

@@ -8,6 +8,8 @@ using HiGHS
 using Random
 using DataStructures
 
+import ..H5FieldsExtractor
+
 global ExpertDualGmiComponent = PyNULL()
 global KnnDualGmiComponent = PyNULL()
 
@@ -24,8 +26,10 @@ function collect_gmi_dual(
     optimizer,
     max_rounds = 10,
     max_cuts_per_round = 500,
+    time_limit = 3_600,
 )
     reset_timer!()
+    initial_time = time()
 
     @timeit "Read H5" begin
         h5_filename = replace(mps_filename, ".mps.gz" => ".h5")
@@ -205,6 +209,12 @@ function collect_gmi_dual(
                 sum(sp[i] * gmi_exps[i] for (i, c) in enumerate(constrs) if useful[i]),
             )
         end
+
+        elapsed_time = time() - initial_time
+        if elapsed_time > time_limit
+            @info "Time limit exceeded. Stopping."
+            break
+        end
     end
 
     @timeit "Store cuts in H5 file" begin
@@ -253,138 +263,6 @@ function collect_gmi_dual(
     )
 end
 
-function ExpertDualGmiComponent_before_mip(test_h5, model, _)
-    # Read cuts and optimal solution
-    h5 = H5File(test_h5, "r")
-    sol_opt_dict = Dict(
-        zip(
-            h5.get_array("static_var_names"),
-            convert(Array{Float64}, h5.get_array("mip_var_values")),
-        ),
-    )
-    cut_basis_vars = h5.get_array("cuts_basis_vars")
-    cut_basis_sizes = h5.get_array("cuts_basis_sizes")
-    cut_rows = h5.get_array("cuts_rows")
-    obj_mip = h5.get_scalar("mip_lower_bound")
-    if obj_mip === nothing
-        obj_mip = h5.get_scalar("mip_obj_value")
-    end
-    h5.close()
-
-    # Initialize stats
-    stats_time_convert = 0
-    stats_time_tableau = 0
-    stats_time_gmi = 0
-    all_cuts = nothing
-
-    stats_time_convert = @elapsed begin
-        # Extract problem data
-        data = ProblemData(model)
-
-        # Construct optimal solution vector (with correct variable sequence)
-        sol_opt = [sol_opt_dict[n] for n in data.var_names]
-
-        # Assert optimal solution is feasible for the original problem
-        assert_leq(data.constr_lb, data.constr_lhs * sol_opt)
-        assert_leq(data.constr_lhs * sol_opt, data.constr_ub)
-
-        # Convert to standard form
-        data_s, transforms = convert_to_standard_form(data)
-        model_s = to_model(data_s)
-        set_optimizer(model_s, HiGHS.Optimizer)
-        relax_integrality(model_s)
-
-        # Convert optimal solution to standard form
-        sol_opt_s = forward(transforms, sol_opt)
-
-        # Assert converted solution is feasible for standard form problem
-        assert_eq(data_s.constr_lhs * sol_opt_s, data_s.constr_lb)
-
-    end
-
-    current_basis = nothing
-    for (r, row) in enumerate(cut_rows)
-        stats_time_tableau += @elapsed begin
-            if r == 1 || cut_basis_vars[r, :] != cut_basis_vars[r-1, :]
-                vbb, vnn, cbb, cnn = cut_basis_sizes[r, :]
-                current_basis = Basis(;
-                    var_basic = cut_basis_vars[r, 1:vbb],
-                    var_nonbasic = cut_basis_vars[r, vbb+1:vbb+vnn],
-                    constr_basic = cut_basis_vars[r, vbb+vnn+1:vbb+vnn+cbb],
-                    constr_nonbasic = cut_basis_vars[r, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
-                )
-            end
-            tableau = compute_tableau(data_s, current_basis, rows = [row])
-            assert_eq(tableau.lhs * sol_opt_s, tableau.rhs)
-        end
-        stats_time_gmi += @elapsed begin
-            cuts_s = compute_gmi(data_s, tableau)
-            assert_does_not_cut_off(cuts_s, sol_opt_s)
-        end
-        cuts = backwards(transforms, cuts_s)
-        assert_does_not_cut_off(cuts, sol_opt)
-
-        if all_cuts === nothing
-            all_cuts = cuts
-        else
-            all_cuts.lhs = [all_cuts.lhs; cuts.lhs]
-            all_cuts.lb = [all_cuts.lb; cuts.lb]
-            all_cuts.ub = [all_cuts.ub; cuts.ub]
-        end
-    end
-
-    # Strategy 1: Add all cuts during the first call
-    function cut_callback_1(cb_data)
-        if all_cuts !== nothing
-            constrs = build_constraints(model, all_cuts)
-            @info "Enforcing $(length(constrs)) cuts..."
-            for c in constrs
-                MOI.submit(model, MOI.UserCut(cb_data), c)
-            end
-            all_cuts = nothing
-        end
-    end
-
-    # Strategy 2: Add violated cuts repeatedly until unable to separate
-    callback_disabled = false
-    function cut_callback_2(cb_data)
-        if callback_disabled
-            return
-        end
-        x = all_variables(model)
-        x_val = callback_value.(cb_data, x)
-        lhs_val = all_cuts.lhs * x_val
-        is_violated = lhs_val .> all_cuts.ub
-        selected_idx = findall(is_violated .== true)
-        selected_cuts = ConstraintSet(
-            lhs=all_cuts.lhs[selected_idx, :],
-            ub=all_cuts.ub[selected_idx],
-            lb=all_cuts.lb[selected_idx],
-        )
-        constrs = build_constraints(model, selected_cuts)
-        if length(constrs) > 0
-            @info "Enforcing $(length(constrs)) cuts..."
-            for c in constrs
-                MOI.submit(model, MOI.UserCut(cb_data), c)
-            end
-        else
-            @info "No violated cuts found. Disabling callback."
-            callback_disabled = true
-        end
-    end
-
-    # Set up cut callback
-    set_attribute(model, MOI.UserCutCallback(), cut_callback_1)
-    # set_attribute(model, MOI.UserCutCallback(), cut_callback_2)
-
-    stats = Dict()
-    stats["ExpertDualGmi: cuts"] = length(all_cuts.lb)
-    stats["ExpertDualGmi: time convert"] = stats_time_convert
-    stats["ExpertDualGmi: time tableau"] = stats_time_tableau
-    stats["ExpertDualGmi: time gmi"] = stats_time_gmi
-    return stats
-end
-
 function add_constraint_set_dual_v2(model::JuMP.Model, cs::ConstraintSet)
     vars = all_variables(model)
     nrows, ncols = size(cs.lhs)
@@ -441,6 +319,58 @@ function _dualgmi_features(h5_filename, extractor)
     end
 end
 
+function _dualgmi_compress_h5(h5_filename)
+    vars_to_basis_offset = Dict()
+    basis_vars = []
+    basis_sizes = []
+    cut_basis::Array{Int} = []
+    cut_row::Array{Int} = []
+
+    h5 = H5File(h5_filename, "r")
+    orig_cut_basis_vars = h5.get_array("cuts_basis_vars")
+    orig_cut_basis_sizes = h5.get_array("cuts_basis_sizes")
+    orig_cut_rows = h5.get_array("cuts_rows")
+    h5.close()
+    if orig_cut_basis_vars === nothing
+        @warn "orig_cut_basis_vars is null; skipping file"
+        return
+    end
+    ncuts, _ = size(orig_cut_basis_vars)
+    if ncuts == 0
+        return
+    end
+
+    for i in 1:ncuts
+        vars = orig_cut_basis_vars[i, :]
+        sizes = orig_cut_basis_sizes[i, :]
+        row = orig_cut_rows[i]
+        if vars ∉ keys(vars_to_basis_offset)
+            offset = size(basis_vars)[1] + 1
+            vars_to_basis_offset[vars] = offset
+            push!(basis_vars, vars)
+            push!(basis_sizes, sizes)
+        end
+        offset = vars_to_basis_offset[vars]
+        push!(cut_basis, offset)
+        push!(cut_row, row)
+    end
+
+    basis_vars = hcat(basis_vars...)'
+    basis_sizes = hcat(basis_sizes...)'
+    _, n_vars = size(basis_vars)
+    if n_vars == 0
+        @warn "n_vars is zero; skipping file"
+        return
+    end
+
+    h5 = H5File(h5_filename, "r+")
+    h5.put_array("gmi_basis_vars", basis_vars)
+    h5.put_array("gmi_basis_sizes", basis_sizes)
+    h5.put_array("gmi_cut_basis", cut_basis)
+    h5.put_array("gmi_cut_row", cut_row)
+    h5.file.close()
+end
+
 function _dualgmi_generate(train_h5, model)
     @timeit "Read problem data" begin
         data = ProblemData(model)
@@ -448,54 +378,71 @@ function _dualgmi_generate(train_h5, model)
     @timeit "Convert to standard form" begin
         data_s, transforms = convert_to_standard_form(data)
     end
-
     @timeit "Collect cuts from H5 files" begin
-        vars_to_unique_basis_offset = Dict()
+        basis_vars_to_basis_offset = Dict()
-        unique_basis_vars = nothing
+        combined_basis_sizes = nothing
-        unique_basis_sizes = nothing
+        combined_basis_sizes_list = Any[]
-        unique_basis_rows = nothing
+        combined_basis_vars = nothing
-    
+        combined_basis_vars_list = Any[]
+        combined_cut_rows = Any[]
         for h5_filename in train_h5
-            h5 = H5File(h5_filename, "r")
+            @timeit "get_array (new)" begin
-            cut_basis_vars = h5.get_array("cuts_basis_vars")
+                h5 = H5File(h5_filename, "r")
-            cut_basis_sizes = h5.get_array("cuts_basis_sizes")
+                gmi_basis_vars = h5.get_array("gmi_basis_vars")
-            cut_rows = h5.get_array("cuts_rows")
+                if gmi_basis_vars === nothing
-            ncuts, nvars = size(cut_basis_vars)
+                    @warn "$(h5_filename) does not contain gmi_basis_vars; skipping"
-            if unique_basis_vars === nothing
+                    continue
-                unique_basis_vars = Matrix{Int}(undef, 0, nvars)
-                unique_basis_sizes = Matrix{Int}(undef, 0, 4)
-                unique_basis_rows = Dict{Int,Set{Int}}()                
-            end
-            for i in 1:ncuts
-                vars = cut_basis_vars[i, :]
-                sizes = cut_basis_sizes[i, :]
-                row = cut_rows[i]
-                if vars ∉ keys(vars_to_unique_basis_offset)
-                    offset = size(unique_basis_vars)[1] + 1
-                    vars_to_unique_basis_offset[vars] = offset
-                    unique_basis_vars = [unique_basis_vars; vars']
-                    unique_basis_sizes = [unique_basis_sizes; sizes']
-                    unique_basis_rows[offset] = Set()
                 end
-                offset = vars_to_unique_basis_offset[vars]
+                gmi_basis_sizes = h5.get_array("gmi_basis_sizes")
-                push!(unique_basis_rows[offset], row)
+                gmi_cut_basis = h5.get_array("gmi_cut_basis")
+                gmi_cut_row = h5.get_array("gmi_cut_row")
+                h5.close()
+            end
+            @timeit "combine basis" begin
+                nbasis, _ = size(gmi_basis_vars)
+                local_to_combined_offset = Dict()
+                for local_offset in 1:nbasis
+                    vars = gmi_basis_vars[local_offset, :]
+                    sizes = gmi_basis_sizes[local_offset, :]
+                    if vars ∉ keys(basis_vars_to_basis_offset)
+                        combined_offset = length(combined_basis_vars_list) + 1
+                        basis_vars_to_basis_offset[vars] = combined_offset
+                        push!(combined_basis_vars_list, vars)
+                        push!(combined_basis_sizes_list, sizes)
+                        push!(combined_cut_rows, Set{Int}())
+                    end
+                    combined_offset = basis_vars_to_basis_offset[vars]
+                    local_to_combined_offset[local_offset] = combined_offset
+                end
+            end
+            @timeit "combine rows" begin
+                ncuts = length(gmi_cut_row)
+                for i in 1:ncuts
+                    local_offset = gmi_cut_basis[i]
+                    combined_offset = local_to_combined_offset[local_offset]
+                    row = gmi_cut_row[i]
+                    push!(combined_cut_rows[combined_offset], row)
+                end
+            end
+            @timeit "convert lists to matrices" begin
+                combined_basis_vars = hcat(combined_basis_vars_list...)'
+                combined_basis_sizes = hcat(combined_basis_sizes_list...)'
             end
-            h5.close()
         end
     end
-
     @timeit "Compute tableaus and cuts" begin
         all_cuts = nothing
-        for (offset, rows) in unique_basis_rows
+        nbasis = length(combined_cut_rows)
+        for offset in 1:nbasis
+            rows = combined_cut_rows[offset]
             try
-                vbb, vnn, cbb, cnn = unique_basis_sizes[offset, :]
+                vbb, vnn, cbb, cnn = combined_basis_sizes[offset, :]
                 current_basis = Basis(;
-                    var_basic = unique_basis_vars[offset, 1:vbb],
+                    var_basic = combined_basis_vars[offset, 1:vbb],
-                    var_nonbasic = unique_basis_vars[offset, vbb+1:vbb+vnn],
+                    var_nonbasic = combined_basis_vars[offset, vbb+1:vbb+vnn],
-                    constr_basic = unique_basis_vars[offset, vbb+vnn+1:vbb+vnn+cbb],
+                    constr_basic = combined_basis_vars[offset, vbb+vnn+1:vbb+vnn+cbb],
-                    constr_nonbasic = unique_basis_vars[offset, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
+                    constr_nonbasic = combined_basis_vars[offset, vbb+vnn+cbb+1:vbb+vnn+cbb+cnn],
                 )
-
                 tableau = compute_tableau(data_s, current_basis; rows=collect(rows))
                 cuts_s = compute_gmi(data_s, tableau)
                 cuts = backwards(transforms, cuts_s)
@@ -599,15 +546,7 @@ function KnnDualGmiComponent_before_mip(data::_KnnDualGmiData, test_h5, model, _
 end
 
 function __init_gmi_dual__()
-    @pydef mutable struct Class1
+    @pydef mutable struct KnnDualGmiComponentPy
-        function fit(_, _) end
-        function before_mip(self, test_h5, model, stats)
-            ExpertDualGmiComponent_before_mip(test_h5, model.inner, stats)
-        end
-    end
-    copy!(ExpertDualGmiComponent, Class1)
-
-    @pydef mutable struct Class2
         function __init__(self; extractor, k = 3, strategy = "near")
             self.data = _KnnDualGmiData(; extractor, k, strategy)
         end
@@ -618,7 +557,23 @@ function __init_gmi_dual__()
             return @time KnnDualGmiComponent_before_mip(self.data, test_h5, model.inner, stats)
         end
     end
-    copy!(KnnDualGmiComponent, Class2)
+    copy!(KnnDualGmiComponent, KnnDualGmiComponentPy)
+
+    @pydef mutable struct ExpertDualGmiComponentPy
+        function __init__(self)
+            self.inner = KnnDualGmiComponentPy(
+                extractor=H5FieldsExtractor(instance_fields=["static_var_obj_coeffs"]),
+                k=1,
+            )
+        end
+        function fit(self, train_h5)
+        end
+        function before_mip(self, test_h5, model, stats)
+            self.inner.fit([test_h5])
+            return self.inner.before_mip(test_h5, model, stats)
+        end
+    end
+    copy!(ExpertDualGmiComponent, ExpertDualGmiComponentPy)
 end
 
 export collect_gmi_dual, expert_gmi_dual, ExpertDualGmiComponent, KnnDualGmiComponent

src/MIPLearn.jl

@@ -13,6 +13,7 @@ include("collectors.jl")
 include("components.jl")
 include("extractors.jl")
 include("io.jl")
+include("problems/maxcut.jl")
 include("problems/setcover.jl")
 include("problems/stab.jl")
 include("problems/tsp.jl")
@@ -24,6 +25,7 @@ function __init__()
     __init_components__()
     __init_extractors__()
     __init_io__()
+    __init_problems_maxcut__()
     __init_problems_setcover__()
     __init_problems_stab__()
     __init_problems_tsp__()
@@ -37,48 +39,48 @@ include("Cuts/Cuts.jl")
 # Precompilation
 # =============================================================================
 
-function __precompile_cuts__()
+# function __precompile_cuts__()
-    function build_model(mps_filename)
+#     function build_model(mps_filename)
-        model = read_from_file(mps_filename)
+#         model = read_from_file(mps_filename)
-        set_optimizer(model, SCIP.Optimizer)
+#         set_optimizer(model, SCIP.Optimizer)
-        return JumpModel(model)
+#         return JumpModel(model)
-    end
+#     end
-    BASEDIR = dirname(@__FILE__)
+#     BASEDIR = dirname(@__FILE__)
-    mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
+#     mps_filename = "$BASEDIR/../test/fixtures/bell5.mps.gz"
-    h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
+#     h5_filename = "$BASEDIR/../test/fixtures/bell5.h5"
-    collect_gmi_dual(
+#     collect_gmi_dual(
-        mps_filename;
+#         mps_filename;
-        optimizer=HiGHS.Optimizer,
+#         optimizer=HiGHS.Optimizer,
-        max_rounds = 10,
+#         max_rounds = 10,
-        max_cuts_per_round = 500,
+#         max_cuts_per_round = 500,
-    )
+#     )
-    knn = KnnDualGmiComponent(
+#     knn = KnnDualGmiComponent(
-        extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
+#         extractor = H5FieldsExtractor(instance_fields = ["static_var_obj_coeffs"]),
-        k = 2,
+#         k = 2,
-    )
+#     )
-    knn.fit([h5_filename, h5_filename])
+#     knn.fit([h5_filename, h5_filename])
-    solver = LearningSolver(
+#     solver = LearningSolver(
-        components = [
+#         components = [
-            ExpertPrimalComponent(action = SetWarmStart()),
+#             ExpertPrimalComponent(action = SetWarmStart()),
-            knn,
+#             knn,
-        ],
+#         ],
-        skip_lp = true,
+#         skip_lp = true,
-    )
+#     )
-    solver.optimize(mps_filename, build_model)
+#     solver.optimize(mps_filename, build_model)
-end
+# end
 
-@setup_workload begin
+# @setup_workload begin
-    using SCIP
+#     using SCIP
-    using HiGHS
+#     using HiGHS
-    using MIPLearn.Cuts
+#     using MIPLearn.Cuts
-    using PrecompileTools: @setup_workload, @compile_workload
+#     using PrecompileTools: @setup_workload, @compile_workload
 
-    __init__()
+#     __init__()
-    Cuts.__init__()
+#     Cuts.__init__()
 
-    @compile_workload begin
+#     @compile_workload begin
-        __precompile_cuts__()
+#         __precompile_cuts__()
-    end
+#     end
-end
+# end
 
 end # module

src/problems/maxcut.jl

@@ -0,0 +1,31 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using JuMP
+
+global MaxCutData = PyNULL()
+global MaxCutGenerator = PyNULL()
+
+function __init_problems_maxcut__()
+    copy!(MaxCutData, pyimport("miplearn.problems.maxcut").MaxCutData)
+    copy!(MaxCutGenerator, pyimport("miplearn.problems.maxcut").MaxCutGenerator)
+end
+
+function build_maxcut_model_jump(data::Any; optimizer)
+    if data isa String
+        data = read_pkl_gz(data)
+    end
+    nodes = collect(data.graph.nodes())
+    edges = collect(data.graph.edges())
+    model = Model(optimizer)
+    @variable(model, x[nodes], Bin)
+    @objective(
+        model,
+        Min,
+        sum(-data.weights[i] * x[e[1]] * (1 - x[e[2]]) for (i, e) in enumerate(edges))
+    )
+    return JumpModel(model)
+end
+
+export MaxCutData, MaxCutGenerator, build_maxcut_model_jump

src/solvers/jump.jl

@@ -89,14 +89,27 @@ function _extract_after_load_vars(model::JuMP.Model, h5)
         for v in vars
     ]
     types = [JuMP.is_binary(v) ? "B" : JuMP.is_integer(v) ? "I" : "C" for v in vars]
-    obj = objective_function(model, AffExpr)
+
-    obj_coeffs = [v ∈ keys(obj.terms) ? obj.terms[v] : 0.0 for v in vars]
+    # Linear obj terms
+    obj = objective_function(model, QuadExpr)
+    obj_coeffs_linear = [v ∈ keys(obj.aff.terms) ? obj.aff.terms[v] : 0.0 for v in vars]
+
+    # Quadratic obj terms
+    if length(obj.terms) > 0
+        nvars = length(vars)
+        obj_coeffs_quad = zeros(nvars, nvars)
+        for (pair, coeff) in obj.terms
+            obj_coeffs_quad[pair.a.index.value, pair.b.index.value] = coeff
+        end
+        h5.put_array("static_var_obj_coeffs_quad", obj_coeffs_quad)
+    end
+
     h5.put_array("static_var_names", to_str_array(JuMP.name.(vars)))
     h5.put_array("static_var_types", to_str_array(types))
     h5.put_array("static_var_lower_bounds", lb)
     h5.put_array("static_var_upper_bounds", ub)
-    h5.put_array("static_var_obj_coeffs", obj_coeffs)
+    h5.put_array("static_var_obj_coeffs", obj_coeffs_linear)
-    h5.put_scalar("static_obj_offset", obj.constant)
+    h5.put_scalar("static_obj_offset", obj.aff.constant)
 end
 
 function _extract_after_load_constrs(model::JuMP.Model, h5)
@@ -143,7 +156,7 @@ function _extract_after_load_constrs(model::JuMP.Model, h5)
         end
     end
     if isempty(names)
-        error("no model constraints found; note that MIPLearn ignores unnamed constraints")
+        return
     end
     lhs = sparse(lhs_rows, lhs_cols, lhs_values, length(rhs), JuMP.num_variables(model))
     h5.put_sparse("static_constr_lhs", lhs)
@@ -282,9 +295,11 @@ function _extract_after_mip(model::JuMP.Model, h5)
 
     # Slacks
     lhs = h5.get_sparse("static_constr_lhs")
-    rhs = h5.get_array("static_constr_rhs")
+    if lhs !== nothing
-    slacks = abs.(lhs * x - rhs)
+        rhs = h5.get_array("static_constr_rhs")
-    h5.put_array("mip_constr_slacks", slacks)
+        slacks = abs.(lhs * x - rhs)
+        h5.put_array("mip_constr_slacks", slacks)
+    end
 
     # Cuts and lazy constraints
     ext = model.ext[:miplearn]

test/src/MIPLearnT.jl

@@ -24,6 +24,7 @@ include("Cuts/tableau/test_gmi_dual.jl")
 include("problems/test_setcover.jl")
 include("problems/test_stab.jl")
 include("problems/test_tsp.jl")
+include("problems/test_maxcut.jl")
 include("solvers/test_jump.jl")
 include("test_io.jl")
 include("test_usage.jl")
@@ -37,6 +38,7 @@ function runtests()
         test_problems_setcover()
         test_problems_stab()
         test_problems_tsp()
+        test_problems_maxcut()
         test_solvers_jump()
         test_usage()
         test_cuts()

test/src/problems/test_maxcut.jl

@@ -0,0 +1,54 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+using PyCall
+
+function test_problems_maxcut()
+    np = pyimport("numpy")
+    random = pyimport("random")
+    scipy_stats = pyimport("scipy.stats")
+    randint = scipy_stats.randint
+    uniform = scipy_stats.uniform
+
+    # Set random seed
+    random.seed(42)
+    np.random.seed(42)
+
+    # Build random instance
+    data = MaxCutGenerator(
+        n = randint(low = 10, high = 11),
+        p = uniform(loc = 0.5, scale = 0.0),
+        fix_graph = false,
+    ).generate(
+        1,
+    )[1]
+
+    # Build model
+    model = build_maxcut_model_jump(data, optimizer = SCIP.Optimizer)
+
+    # Check static features
+    h5 = H5File(tempname(), "w")
+    model.extract_after_load(h5)
+    obj_linear = h5.get_array("static_var_obj_coeffs")
+    obj_quad = h5.get_array("static_var_obj_coeffs_quad")
+    @test obj_linear == [3.0, 1.0, 3.0, 1.0, -1.0, 0.0, -1.0, 0.0, -1.0, 0.0]
+    @test obj_quad == [
+        0.0 0.0 -1.0 1.0 -1.0 0.0 0.0 0.0 -1.0 -1.0
+        0.0 0.0 1.0 -1.0 0.0 -1.0 -1.0 0.0 0.0 1.0
+        0.0 0.0 0.0 0.0 0.0 -1.0 0.0 0.0 -1.0 -1.0
+        0.0 0.0 0.0 0.0 0.0 -1.0 1.0 -1.0 0.0 0.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -1.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
+        0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+    ]
+
+    # Check optimal solution
+    model.optimize()
+    model.extract_after_mip(h5)
+    @test h5.get_scalar("mip_obj_value") == -4
+    h5.close()
+end