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# 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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import re
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import sys
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from abc import ABC, abstractmethod
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from io import StringIO
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import pyomo
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import pyomo.environ as pe
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from pyomo.core import Var
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from . import RedirectOutput
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from .internal import InternalSolver
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from ..instance import Instance
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logger = logging.getLogger(__name__)
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class PyomoSolver(InternalSolver):
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"""
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Base class for all Pyomo-based InternalSolvers.
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Attributes
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----------
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instance: miplearn.Instance
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The MIPLearn instance currently loaded to the solver
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model: pyomo.core.ConcreteModel
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The Pyomo model currently loaded on the solver
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"""
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def __init__(self):
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self.instance = None
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self.model = None
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self._all_vars = None
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self._bin_vars = None
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self._is_warm_start_available = False
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self._pyomo_solver = None
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self._obj_sense = None
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self._varname_to_var = {}
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def solve_lp(self, tee=False):
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"""
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Solves the LP relaxation of the currently loaded instance.
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Parameters
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----------
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tee: bool
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If true, prints the solver log to the screen.
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Returns
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-------
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dict
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A dictionary of solver statistics containing the following keys:
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"Optimal value".
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"""
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for var in self._bin_vars:
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lb, ub = var.bounds
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var.setlb(lb)
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var.setub(ub)
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var.domain = pyomo.core.base.set_types.Reals
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self._pyomo_solver.update_var(var)
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results = self._pyomo_solver.solve(tee=tee)
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for var in self._bin_vars:
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var.domain = pyomo.core.base.set_types.Binary
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self._pyomo_solver.update_var(var)
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return {
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"Optimal value": results["Problem"][0]["Lower bound"],
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}
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def get_solution(self):
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"""
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Returns current solution found by the solver.
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If called after `solve`, returns the best primal solution found during
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the search. If called after `solve_lp`, returns the optimal solution
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to the LP relaxation.
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The solution is a dictionary `sol`, where the optimal value of `var[idx]`
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is given by `sol[var][idx]`.
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"""
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solution = {}
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for var in self.model.component_objects(Var):
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solution[str(var)] = {}
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for index in var:
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solution[str(var)][index] = var[index].value
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return solution
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def set_warm_start(self, solution):
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"""
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Sets the warm start to be used by the solver.
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The solution should be a dictionary following the same format as the
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one produced by `get_solution`. Only one warm start is currently
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supported. Calling this function when a warm start already exists will
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remove the previous warm start.
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"""
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self.clear_warm_start()
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count_total, count_fixed = 0, 0
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for var_name in solution:
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var = self._varname_to_var[var_name]
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for index in solution[var_name]:
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count_total += 1
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var[index].value = solution[var_name][index]
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if solution[var_name][index] is not None:
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count_fixed += 1
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if count_fixed > 0:
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self._is_warm_start_available = True
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logger.info("Setting start values for %d variables (out of %d)" %
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(count_fixed, count_total))
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def clear_warm_start(self):
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"""
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Removes any existing warm start from the solver.
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"""
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for var in self._all_vars:
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if not var.fixed:
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var.value = None
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self._is_warm_start_available = False
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def set_instance(self, instance, model=None):
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"""
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Loads the given instance into the solver.
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Parameters
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----------
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instance: miplearn.Instance
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The instance to be loaded.
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model: pyomo.core.ConcreteModel
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The corresponding Pyomo model. If not provided, it will be
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generated by calling `instance.to_model()`.
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"""
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if model is None:
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model = instance.to_model()
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assert isinstance(instance, Instance)
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assert isinstance(model, pe.ConcreteModel)
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self.instance = instance
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self.model = model
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self._pyomo_solver.set_instance(model)
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# Update objective sense
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self._obj_sense = "max"
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if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
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self._obj_sense = "min"
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# Update variables
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self._all_vars = []
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self._bin_vars = []
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self._varname_to_var = {}
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for var in model.component_objects(Var):
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self._varname_to_var[var.name] = var
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for idx in var:
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self._all_vars += [var[idx]]
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if var[idx].domain == pyomo.core.base.set_types.Binary:
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self._bin_vars += [var[idx]]
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def fix(self, solution):
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"""
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Fixes the values of a subset of decision variables.
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The values should be provided in the dictionary format generated by
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`get_solution`. Missing values in the solution indicate variables
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that should be left free.
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"""
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count_total, count_fixed = 0, 0
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for varname in solution:
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for index in solution[varname]:
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var = self._varname_to_var[varname]
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count_total += 1
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if solution[varname][index] is None:
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continue
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count_fixed += 1
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var[index].fix(solution[varname][index])
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self._pyomo_solver.update_var(var[index])
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logger.info("Fixing values for %d variables (out of %d)" %
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(count_fixed, count_total))
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def add_constraint(self, constraint):
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"""
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Adds a single constraint to the model.
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"""
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self._pyomo_solver.add_constraint(constraint)
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def solve(self, tee=False):
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"""
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Solves the currently loaded instance.
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Parameters
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----------
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tee: bool
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If true, prints the solver log to the screen.
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Returns
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-------
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dict
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A dictionary of solver statistics containing the following keys:
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"Lower bound", "Upper bound", "Wallclock time", "Nodes", "Sense",
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"Log" and "Warm start value".
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"""
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total_wallclock_time = 0
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streams = [StringIO()]
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if tee:
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streams += [sys.stdout]
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self.instance.found_violations = []
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while True:
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logger.debug("Solving MIP...")
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with RedirectOutput(streams):
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results = self._pyomo_solver.solve(tee=True,
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warmstart=self._is_warm_start_available)
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total_wallclock_time += results["Solver"][0]["Wallclock time"]
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if not hasattr(self.instance, "find_violations"):
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break
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logger.debug("Finding violated constraints...")
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violations = self.instance.find_violations(self.model)
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if len(violations) == 0:
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break
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self.instance.found_violations += violations
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logger.debug(" %d violations found" % len(violations))
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for v in violations:
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cut = self.instance.build_lazy_constraint(self.model, v)
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self.add_constraint(cut)
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log = streams[0].getvalue()
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return {
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"Lower bound": results["Problem"][0]["Lower bound"],
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"Upper bound": results["Problem"][0]["Upper bound"],
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"Wallclock time": total_wallclock_time,
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"Nodes": self._extract_node_count(log),
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"Sense": self._obj_sense,
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"Log": log,
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"Warm start value": self._extract_warm_start_value(log),
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}
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@staticmethod
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def __extract(log, regexp, default=None):
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value = default
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for line in log.splitlines():
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matches = re.findall(regexp, line)
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if len(matches) == 0:
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continue
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value = matches[0]
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return value
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def _extract_warm_start_value(self, log):
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"""
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Extracts and returns the objective value of the user-provided MIP start
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from the provided solver log. If more than one value is found, returns
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the last one. If no value is present in the logs, returns None.
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"""
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value = self.__extract(log, self._get_warm_start_regexp())
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if value is not None:
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value = float(value)
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return value
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def _extract_node_count(self, log):
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"""
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Extracts and returns the number of explored branch-and-bound nodes.
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"""
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return int(self.__extract(log,
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self._get_node_count_regexp(),
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default=1))
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def set_threads(self, threads):
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key = self._get_threads_option_name()
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self._pyomo_solver.options[key] = threads
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def set_time_limit(self, time_limit):
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key = self._get_time_limit_option_name()
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self._pyomo_solver.options[key] = time_limit
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def set_node_limit(self, node_limit):
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key = self._get_node_limit_option_name()
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self._pyomo_solver.options[key] = node_limit
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def set_gap_tolerance(self, gap_tolerance):
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key = self._get_gap_tolerance_option_name()
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self._pyomo_solver.options[key] = gap_tolerance
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@abstractmethod
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def _get_warm_start_regexp(self):
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pass
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@abstractmethod
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def _get_node_count_regexp(self):
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pass
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@abstractmethod
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def _get_threads_option_name(self):
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pass
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@abstractmethod
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def _get_time_limit_option_name(self):
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pass
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@abstractmethod
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def _get_node_limit_option_name(self):
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pass
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@abstractmethod
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def _get_gap_tolerance_option_name(self):
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pass
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