You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
117 lines
4.2 KiB
117 lines
4.2 KiB
# 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.
|
|
|
|
from dataclasses import dataclass
|
|
from typing import List, Union
|
|
|
|
import gurobipy as gp
|
|
import networkx as nx
|
|
import numpy as np
|
|
import pyomo.environ as pe
|
|
from gurobipy import GRB, quicksum
|
|
from networkx import Graph
|
|
from scipy.stats import uniform, randint
|
|
from scipy.stats.distributions import rv_frozen
|
|
|
|
from miplearn.io import read_pkl_gz
|
|
from miplearn.solvers.gurobi import GurobiModel
|
|
from miplearn.solvers.pyomo import PyomoModel
|
|
|
|
|
|
@dataclass
|
|
class MaxWeightStableSetData:
|
|
graph: Graph
|
|
weights: np.ndarray
|
|
|
|
|
|
class MaxWeightStableSetGenerator:
|
|
"""Random instance generator for the Maximum-Weight Stable Set Problem.
|
|
|
|
The generator has two modes of operation. When `fix_graph=True` is provided,
|
|
one random Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$
|
|
and $p$ are sampled from user-provided probability distributions `n` and `p`. To
|
|
generate each instance, the generator independently samples each $w_v$ from the
|
|
user-provided probability distribution `w`.
|
|
|
|
When `fix_graph=False`, a new random graph is generated for each instance; the
|
|
remaining parameters are sampled in the same way.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
w: rv_frozen = uniform(loc=10.0, scale=1.0),
|
|
n: rv_frozen = randint(low=250, high=251),
|
|
p: rv_frozen = uniform(loc=0.05, scale=0.0),
|
|
fix_graph: bool = True,
|
|
):
|
|
"""Initialize the problem generator.
|
|
|
|
Parameters
|
|
----------
|
|
w: rv_continuous
|
|
Probability distribution for vertex weights.
|
|
n: rv_discrete
|
|
Probability distribution for parameter $n$ in Erdős-Rényi model.
|
|
p: rv_continuous
|
|
Probability distribution for parameter $p$ in Erdős-Rényi model.
|
|
"""
|
|
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
|
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
|
assert isinstance(p, rv_frozen), "p should be a SciPy probability distribution"
|
|
self.w = w
|
|
self.n = n
|
|
self.p = p
|
|
self.fix_graph = fix_graph
|
|
self.graph = None
|
|
if fix_graph:
|
|
self.graph = self._generate_graph()
|
|
|
|
def generate(self, n_samples: int) -> List[MaxWeightStableSetData]:
|
|
def _sample() -> MaxWeightStableSetData:
|
|
if self.graph is not None:
|
|
graph = self.graph
|
|
else:
|
|
graph = self._generate_graph()
|
|
weights = np.round(self.w.rvs(graph.number_of_nodes()), 2)
|
|
return MaxWeightStableSetData(graph, weights)
|
|
|
|
return [_sample() for _ in range(n_samples)]
|
|
|
|
def _generate_graph(self) -> Graph:
|
|
return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())
|
|
|
|
|
|
def build_stab_model_gurobipy(data: MaxWeightStableSetData) -> GurobiModel:
|
|
data = _read_stab_data(data)
|
|
model = gp.Model()
|
|
nodes = list(data.graph.nodes)
|
|
x = model.addVars(nodes, vtype=GRB.BINARY, name="x")
|
|
model.setObjective(quicksum(-data.weights[i] * x[i] for i in nodes))
|
|
for clique in nx.find_cliques(data.graph):
|
|
model.addConstr(quicksum(x[i] for i in clique) <= 1)
|
|
model.update()
|
|
return GurobiModel(model)
|
|
|
|
|
|
def build_stab_model_pyomo(
|
|
data: MaxWeightStableSetData,
|
|
solver: str = "gurobi_persistent",
|
|
) -> PyomoModel:
|
|
data = _read_stab_data(data)
|
|
model = pe.ConcreteModel()
|
|
nodes = pe.Set(initialize=list(data.graph.nodes))
|
|
model.x = pe.Var(nodes, domain=pe.Boolean, name="x")
|
|
model.obj = pe.Objective(expr=sum([-data.weights[i] * model.x[i] for i in nodes]))
|
|
model.clique_eqs = pe.ConstraintList()
|
|
for clique in nx.find_cliques(data.graph):
|
|
model.clique_eqs.add(expr=sum(model.x[i] for i in clique) <= 1)
|
|
return PyomoModel(model, solver)
|
|
|
|
|
|
def _read_stab_data(data: Union[str, MaxWeightStableSetData]) -> MaxWeightStableSetData:
|
|
if isinstance(data, str):
|
|
data = read_pkl_gz(data)
|
|
assert isinstance(data, MaxWeightStableSetData)
|
|
return data
|