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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 re
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import sys
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import logging
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from io import StringIO
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from . import RedirectOutput
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from .internal import InternalSolver
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logger = logging.getLogger(__name__)
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class GurobiSolver(InternalSolver):
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def __init__(self, params=None):
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if params is None:
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params = {
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"LazyConstraints": 1,
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"PreCrush": 1,
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}
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from gurobipy import GRB
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self.GRB = GRB
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self.instance = None
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self.model = None
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self.params = params
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self._all_vars = None
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self._bin_vars = None
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self._varname_to_var = None
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def set_instance(self, instance, model=None):
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if model is None:
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model = instance.to_model()
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self.instance = instance
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self.model = model
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self.model.update()
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self._update_vars()
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def _update_vars(self):
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self._all_vars = {}
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self._bin_vars = {}
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for var in self.model.getVars():
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m = re.search(r"([^[]*)\[(.*)\]", var.varName)
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if m is None:
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name = var.varName
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idx = [0]
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else:
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name = m.group(1)
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idx = tuple(int(k) if k.isdecimal() else k
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for k in m.group(2).split(","))
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if len(idx) == 1:
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idx = idx[0]
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if name not in self._all_vars:
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self._all_vars[name] = {}
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self._all_vars[name][idx] = var
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if var.vtype != 'C':
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if name not in self._bin_vars:
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self._bin_vars[name] = {}
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self._bin_vars[name][idx] = var
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def _apply_params(self):
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for (name, value) in self.params.items():
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self.model.setParam(name, value)
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def solve_lp(self, tee=False):
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self._apply_params()
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streams = [StringIO()]
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if tee:
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streams += [sys.stdout]
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for (varname, vardict) in self._bin_vars.items():
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for (idx, var) in vardict.items():
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var.vtype = self.GRB.CONTINUOUS
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var.lb = 0.0
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var.ub = 1.0
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with RedirectOutput(streams):
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self.model.optimize()
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for (varname, vardict) in self._bin_vars.items():
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for (idx, var) in vardict.items():
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var.vtype = self.GRB.BINARY
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log = streams[0].getvalue()
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return {
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"Optimal value": self.model.objVal,
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"Log": log
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}
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def solve(self, tee=False):
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all_vars = self.model.getVars()
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self.instance.found_violated_lazy_constraints = []
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self.instance.found_violated_user_cuts = []
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streams = [StringIO()]
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if tee:
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streams += [sys.stdout]
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def cb(cb_model, cb_where):
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try:
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# User cuts
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if cb_where == self.GRB.Callback.MIPNODE:
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logger.debug("Finding violated cutting planes...")
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violations = self.instance.find_violated_user_cuts(cb_model)
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self.instance.found_violated_user_cuts += violations
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logger.debug(" %d found" % len(violations))
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for v in violations:
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cut = self.instance.build_user_cut(cb_model, v)
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cb_model.cbCut(cut)
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# Lazy constraints
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if cb_where == self.GRB.Callback.MIPSOL:
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logger.debug("Finding violated lazy constraints...")
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violations = self.instance.find_violated_lazy_constraints(cb_model)
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self.instance.found_violated_lazy_constraints += violations
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logger.debug(" %d found" % len(violations))
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for v in violations:
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cut = self.instance.build_lazy_constraint(cb_model, v)
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cb_model.cbLazy(cut)
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except Exception as e:
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logger.error(e)
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with RedirectOutput(streams):
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self.model.optimize(cb)
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log = streams[0].getvalue()
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return {
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"Lower bound": self.model.objVal,
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"Upper bound": self.model.objBound,
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"Wallclock time": self.model.runtime,
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"Nodes": int(self.model.nodeCount),
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"Sense": ("min" if self.model.modelSense == 1 else "max"),
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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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def get_solution(self):
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solution = {}
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for (varname, vardict) in self._all_vars.items():
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solution[varname] = {}
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for (idx, var) in vardict.items():
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solution[varname][idx] = var.x
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return solution
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def add_constraint(self, constraint):
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self.model.addConstr(constraint)
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def set_warm_start(self, solution):
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count_fixed, count_total = 0, 0
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for (varname, vardict) in solution.items():
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for (idx, value) in vardict.items():
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count_total += 1
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if value is not None:
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count_fixed += 1
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self._all_vars[varname][idx].start = value
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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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for (varname, vardict) in self._all_vars:
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for (idx, var) in vardict.items():
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var[idx].start = self.GRB.UNDEFINED
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def fix(self, solution):
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for (varname, vardict) in solution.items():
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for (idx, value) in vardict.items():
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if value is None:
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continue
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var = self._all_vars[varname][idx]
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var.vtype = self.GRB.CONTINUOUS
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var.lb = value
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var.ub = value
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def set_branching_priorities(self, priorities):
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logger.warning("set_branching_priorities not implemented")
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def set_threads(self, threads):
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self.params["Threads"] = threads
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def set_time_limit(self, time_limit):
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self.params["TimeLimit"] = time_limit
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def set_node_limit(self, node_limit):
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self.params["NodeLimit"] = node_limit
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def set_gap_tolerance(self, gap_tolerance):
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self.params["MIPGap"] = gap_tolerance
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def _extract_warm_start_value(self, log):
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ws = self.__extract(log, "MIP start with objective ([0-9.e+-]*)")
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if ws is not None:
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ws = float(ws)
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return ws
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def __extract(self, 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 __getstate__(self):
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return self.params
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def __setstate__(self, state):
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self.params = state
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