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GurobiModel: Capture static_var_obj_coeffs_quad

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This commit is contained in:
Alinson S. Xavier
2025-06-11 13:19:36 -05:00
parent 1c6912cc51
commit 2ca2794457
3 changed files with 82 additions and 10 deletions
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10
miplearn/problems/maxcut.py
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@@ -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(
- data.weights[i] * x[e[0]] * (1 - x[e[1]]) for (i, e) in enumerate(edges)
))
model.setObjective(
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)

7
miplearn/solvers/gurobi.py
View File

@@ -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()

75
tests/problems/test_maxcut.py
View File

@@ -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 miplearn.problems.maxcut import MaxCutGenerator, build_maxcut_model_gurobipy
from scipy.stats import randint, uniform
from miplearn.h5 import H5File
from miplearn.problems.maxcut import MaxCutGenerator, build_maxcut_model_gurobipy
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