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51 lines
1.6 KiB
51 lines
1.6 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, 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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import logging
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from pyomo import environ as pe
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from scipy.stats import randint
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from miplearn.solvers.pyomo.base import BasePyomoSolver
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logger = logging.getLogger(__name__)
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class GurobiPyomoSolver(BasePyomoSolver):
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"""
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An InternalSolver that uses Gurobi and the Pyomo modeling language.
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Parameters
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----------
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params: dict
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Dictionary of options to pass to the Pyomo solver. For example,
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{"Threads": 4} to set the number of threads.
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"""
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def __init__(self, params=None):
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super().__init__(
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solver_factory=pe.SolverFactory("gurobi_persistent"),
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params={
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"Seed": randint(low=0, high=1000).rvs(),
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},
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)
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def _extract_node_count(self, log):
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return max(1, int(self._pyomo_solver._solver_model.getAttr("NodeCount")))
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def _get_warm_start_regexp(self):
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return "MIP start with objective ([0-9.e+-]*)"
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def _get_node_count_regexp(self):
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return None
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def set_branching_priorities(self, priorities):
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from gurobipy import GRB
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for varname in priorities.keys():
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var = self._varname_to_var[varname]
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for (index, priority) in priorities[varname].items():
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gvar = self._pyomo_solver._pyomo_var_to_solver_var_map[var[index]]
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gvar.setAttr(GRB.Attr.BranchPriority, int(round(priority)))
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