GurobiModel: Capture static_var_obj_coeffs_quad

miplearn/problems/maxcut.py

@@ -80,6 +80,7 @@ class MaxCutGenerator:
     def _generate_graph(self) -> Graph:
         return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())
 
+
 def build_maxcut_model_gurobipy(
     data: Union[str, MaxCutData],
     params: Optional[dict[str, Any]] = None,
@@ -97,13 +98,16 @@ def build_maxcut_model_gurobipy(
     x = model.addVars(nodes, vtype=gp.GRB.BINARY, name="x")
 
     # Add the objective function
-    model.setObjective(quicksum(
+    model.setObjective(
-        - data.weights[i] * x[e[0]] * (1 - x[e[1]]) for (i, e) in enumerate(edges)
+        quicksum(
-    ))
+            -data.weights[i] * x[e[0]] * (1 - x[e[1]]) for (i, e) in enumerate(edges)
+        )
+    )
 
     model.update()
     return GurobiModel(model)
 
+
 def _maxcut_read(data: Union[str, MaxCutData]) -> MaxCutData:
     if isinstance(data, str):
         data = read_pkl_gz(data)

miplearn/solvers/gurobi.py

@@ -264,6 +264,13 @@ class GurobiModel(AbstractModel):
             h5.put_array(
                 h5_field, np.array(self.inner.getAttr(gp_field, gp_vars), dtype=float)
             )
+        obj = self.inner.getObjective()
+        if isinstance(obj, gp.QuadExpr):
+            nvars = len(self.inner.getVars())
+            obj_q = np.zeros((nvars, nvars))
+            for i in range(obj.size()):
+                obj_q[obj.getVar1(i).index, obj.getVar2(i).index] = obj.getCoeff(i)
+            h5.put_array("static_var_obj_coeffs_quad", obj_q)
 
     def _extract_after_load_constrs(self, h5: H5File) -> None:
         gp_constrs = self.inner.getConstrs()

tests/problems/test_maxcut.py

@@ -1,17 +1,22 @@
 #  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.
+
 import random
+from tempfile import TemporaryDirectory
 
 import numpy as np
+from scipy.stats import randint, uniform
 
+from miplearn.h5 import H5File
 from miplearn.problems.maxcut import MaxCutGenerator, build_maxcut_model_gurobipy
-from scipy.stats import randint, uniform
+
 
 def _set_seed():
     random.seed(42)
     np.random.seed(42)
 
+
 def test_maxcut_generator_not_fixed() -> None:
     _set_seed()
     gen = MaxCutGenerator(
@@ -22,12 +27,20 @@ def test_maxcut_generator_not_fixed() -> None:
     data = gen.generate(3)
     assert len(data) == 3
     assert list(data[0].graph.nodes()) == [0, 1, 2, 3, 4]
-    assert list(data[0].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
+    assert list(data[0].graph.edges()) == [
+        (0, 2),
+        (0, 3),
+        (0, 4),
+        (2, 3),
+        (2, 4),
+        (3, 4),
+    ]
     assert data[0].weights.tolist() == [-1, 1, -1, -1, -1, 1]
     assert list(data[1].graph.nodes()) == [0, 1, 2, 3, 4]
     assert list(data[1].graph.edges()) == [(0, 1), (0, 3), (0, 4), (1, 4), (3, 4)]
     assert data[1].weights.tolist() == [-1, -1, -1, 1, -1]
 
+
 def test_maxcut_generator_fixed() -> None:
     random.seed(42)
     np.random.seed(42)
@@ -39,19 +52,67 @@ def test_maxcut_generator_fixed() -> None:
     data = gen.generate(3)
     assert len(data) == 3
     assert list(data[0].graph.nodes()) == [0, 1, 2, 3, 4]
-    assert list(data[0].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
+    assert list(data[0].graph.edges()) == [
+        (0, 2),
+        (0, 3),
+        (0, 4),
+        (2, 3),
+        (2, 4),
+        (3, 4),
+    ]
     assert data[0].weights.tolist() == [-1, 1, -1, -1, -1, 1]
     assert list(data[1].graph.nodes()) == [0, 1, 2, 3, 4]
-    assert list(data[1].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
+    assert list(data[1].graph.edges()) == [
+        (0, 2),
+        (0, 3),
+        (0, 4),
+        (2, 3),
+        (2, 4),
+        (3, 4),
+    ]
     assert data[1].weights.tolist() == [-1, -1, -1, 1, -1, -1]
 
+
 def test_maxcut_model():
     _set_seed()
     data = MaxCutGenerator(
-        n=randint(low=20, high=21),
+        n=randint(low=10, high=11),
         p=uniform(loc=0.5, scale=0.0),
         fix_graph=True,
     ).generate(1)[0]
     model = build_maxcut_model_gurobipy(data)
+
+    with TemporaryDirectory() as tempdir:
+        with H5File(f"{tempdir}/data.h5", "w") as h5:
+            model.extract_after_load(h5)
+            obj_lin = h5.get_array("static_var_obj_coeffs")
+            assert obj_lin is not None
+            assert obj_lin.tolist() == [
+                3.0,
+                1.0,
+                3.0,
+                1.0,
+                -1.0,
+                0.0,
+                -1.0,
+                0.0,
+                -1.0,
+                0.0,
+            ]
+            obj_quad = h5.get_array("static_var_obj_coeffs_quad")
+            assert obj_quad is not None
+            assert obj_quad.tolist() == [
+                [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],
+            ]
+
     model.optimize()
-    assert model.inner.ObjVal == -26
+    assert model.inner.ObjVal == -4