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Pyomo: implement build_maxcut_model; add support for quadratic objectives

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This commit is contained in:
Alinson S. Xavier
2025-06-11 14:23:10 -05:00
parent 2ca2794457
commit daa801b5e9
3 changed files with 136 additions and 57 deletions
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81
tests/problems/test_maxcut.py
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@@ -9,7 +9,11 @@ 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 miplearn.problems.maxcut import (
MaxCutGenerator,
build_maxcut_model_gurobipy,
build_maxcut_model_pyomo,
)
def _set_seed():
@@ -80,39 +84,42 @@ def test_maxcut_model():
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 == -4
for build_model in [
build_maxcut_model_gurobipy,
build_maxcut_model_pyomo,
]:
model = build_model(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()
model.extract_after_mip(h5)
assert h5.get_scalar("mip_obj_value") == -4