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55 lines
1.7 KiB
55 lines
1.7 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from dataclasses import dataclass
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from typing import List, Union
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import gurobipy as gp
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import numpy as np
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from gurobipy import GRB, quicksum
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from networkx import Graph
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from scipy.stats import uniform, randint
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from scipy.stats.distributions import rv_frozen
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from .stab import MaxWeightStableSetGenerator
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from miplearn.solvers.gurobi import GurobiModel
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from ..io import read_pkl_gz
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@dataclass
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class MinWeightVertexCoverData:
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graph: Graph
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weights: np.ndarray
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class MinWeightVertexCoverGenerator:
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def __init__(
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self,
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w: rv_frozen = uniform(loc=10.0, scale=1.0),
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n: rv_frozen = randint(low=250, high=251),
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p: rv_frozen = uniform(loc=0.05, scale=0.0),
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fix_graph: bool = True,
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):
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self._generator = MaxWeightStableSetGenerator(w, n, p, fix_graph)
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def generate(self, n_samples: int) -> List[MinWeightVertexCoverData]:
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return [
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MinWeightVertexCoverData(s.graph, s.weights)
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for s in self._generator.generate(n_samples)
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]
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def build_vertexcover_model(data: Union[str, MinWeightVertexCoverData]) -> GurobiModel:
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if isinstance(data, str):
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data = read_pkl_gz(data)
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assert isinstance(data, MinWeightVertexCoverData)
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model = gp.Model()
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nodes = list(data.graph.nodes)
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x = model.addVars(nodes, vtype=GRB.BINARY, name="x")
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model.setObjective(quicksum(data.weights[i] * x[i] for i in nodes))
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for (v1, v2) in data.graph.edges:
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model.addConstr(x[v1] + x[v2] >= 1)
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model.update()
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return GurobiModel(model)
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