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51 lines
1.7 KiB
51 lines
1.7 KiB
# MIPLearn: A Machine-Learning Framework for Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from .solvers import LearningSolver
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from .core import Parameters
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import numpy as np
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import pyomo.environ as pe
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import networkx as nx
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class MaxStableSetGenerator:
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"""Class that generates random instances of the Maximum Stable Set (MSS) Problem."""
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def __init__(self, n_vertices, density=0.1, seed=42):
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self.graph = nx.generators.random_graphs.binomial_graph(n_vertices, density, seed)
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self.base_weights = np.random.rand(self.graph.number_of_nodes()) * 10
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def generate(self):
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perturbation = np.random.rand(self.graph.number_of_nodes()) * 0.1
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weights = self.base_weights + perturbation
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return MaxStableSetParameters(self.graph, weights)
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class MaxStableSetParameters(Parameters):
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def __init__(self, graph, weights):
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self.graph = graph
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self.weights = weights
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def to_model(self):
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nodes = list(self.graph.nodes)
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edges = list(self.graph.edges)
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model = m = pe.ConcreteModel()
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m.x = pe.Var(nodes, domain=pe.Binary)
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m.OBJ = pe.Objective(rule=lambda m : sum(m.x[v] * self.weights[v] for v in nodes),
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sense=pe.maximize)
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m.edge_eqs = pe.ConstraintList()
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for edge in edges:
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m.edge_eqs.add(m.x[edge[0]] + m.x[edge[1]] <= 1)
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return m
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def to_array(self):
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return self.weights
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def test_stab():
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generator = MaxStableSetGenerator(n_vertices=100)
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for k in range(5):
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params = generator.generate()
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solver = LearningSolver()
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solver.solve(params) |