Add MaxCut problem

tests/problems/test_maxcut.py

@@ -0,0 +1,57 @@
+#  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
+
+import numpy as np
+
+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(
+        n=randint(low=5, high=6),
+        p=uniform(loc=0.5, scale=0.0),
+        fix_graph=False,
+    )
+    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 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)
+    gen = MaxCutGenerator(
+        n=randint(low=5, high=6),
+        p=uniform(loc=0.5, scale=0.0),
+        fix_graph=True,
+    )
+    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 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 data[1].weights.tolist() == [-1, -1, -1, 1, -1, -1]
+
+def test_maxcut_model():
+    _set_seed()
+    data = MaxCutGenerator(
+        n=randint(low=20, high=21),
+        p=uniform(loc=0.5, scale=0.0),
+        fix_graph=True,
+    ).generate(1)[0]
+    model = build_maxcut_model_gurobipy(data)
+    model.optimize()
+    assert model.inner.ObjVal == -26