# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization # Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved. # Released under the modified BSD license. See COPYING.md for more details. import networkx as nx import numpy as np from miplearn.problems.vertexcover import ( MinWeightVertexCoverData, build_vertexcover_model_gurobipy, ) def test_stab() -> None: data = MinWeightVertexCoverData( graph=nx.cycle_graph(5), weights=np.array([1.0, 1.0, 1.0, 1.0, 1.0]), ) model = build_vertexcover_model_gurobipy(data) model.optimize() assert model.inner.objVal == 3.0