Add support for MIQPs; implement max cut model

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==0.4.2`)
+    run(`$pip install miplearn==0.4.4`)
 end
 
 install_miplearn()

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__()

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) > 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_scalar("static_obj_offset", obj.constant)
+    h5.put_array("static_var_obj_coeffs", obj_coeffs_linear)
+    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")
-    slacks = abs.(lhs * x - rhs)
-    h5.put_array("mip_constr_slacks", slacks)
+    if lhs !== nothing
+        rhs = h5.get_array("static_constr_rhs")
+        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