Source code for pyomo.contrib.multistart.multi

#  ___________________________________________________________________________
#
#  Pyomo: Python Optimization Modeling Objects
#  Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
#  Under the terms of Contract DE-NA0003525 with National Technology and
#  Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
#  rights in this software.
#  This software is distributed under the 3-clause BSD License.
#  ___________________________________________________________________________


from __future__ import division

import logging

from pyomo.common.config import (
    ConfigBlock, ConfigValue, In, add_docstring_list
)
from pyomo.common.modeling import unique_component_name
from pyomo.contrib.multistart.high_conf_stop import should_stop
from pyomo.contrib.multistart.reinit import reinitialize_variables, strategies
from pyomo.core import Objective, Var, minimize, value
from pyomo.opt import SolverFactory, SolverStatus
from pyomo.opt import TerminationCondition as tc

logger = logging.getLogger('pyomo.contrib.multistart')


[docs]@SolverFactory.register('multistart', doc='MultiStart solver for NLPs') class MultiStart(object): """Solver wrapper that initializes at multiple starting points. # TODO: also return appropriate duals For theoretical underpinning, see https://www.semanticscholar.org/paper/How-many-random-restarts-are-enough-Dick-Wong/55b248b398a03dc1ac9a65437f88b835554329e0 Keyword arguments below are specified for the ``solve`` function. """ CONFIG = ConfigBlock("MultiStart") CONFIG.declare("strategy", ConfigValue( default="rand", domain=In(strategies.keys()), description="Specify the restart strategy. Defaults to rand.", doc="""Specify the restart strategy. - "rand": random choice between variable bounds - "midpoint_guess_and_bound": midpoint between current value and farthest bound - "rand_guess_and_bound": random choice between current value and farthest bound - "rand_distributed": random choice among evenly distributed values - "midpoint": exact midpoint between the bounds. If using this option, multiple iterations are useless. """ )) CONFIG.declare("solver", ConfigValue( default="ipopt", description="solver to use, defaults to ipopt" )) CONFIG.declare("solver_args", ConfigValue( default={}, description="Dictionary of keyword arguments to pass to the solver." )) CONFIG.declare("iterations", ConfigValue( default=10, description="Specify the number of iterations, defaults to 10. " "If -1 is specified, the high confidence stopping rule will be used" )) CONFIG.declare("stopping_mass", ConfigValue( default=0.5, description="Maximum allowable estimated missing mass of optima.", doc="""Maximum allowable estimated missing mass of optima for the high confidence stopping rule, only used with the random strategy. The lower the parameter, the stricter the rule. Value bounded in (0, 1].""" )) CONFIG.declare("stopping_delta", ConfigValue( default=0.5, description="1 minus the confidence level required for the stopping rule.", doc="""1 minus the confidence level required for the stopping rule for the high confidence stopping rule, only used with the random strategy. The lower the parameter, the stricter the rule. Value bounded in (0, 1].""" )) CONFIG.declare("suppress_unbounded_warning", ConfigValue( default=False, domain=bool, description="True to suppress warning for skipping unbounded variables." )) CONFIG.declare("HCS_max_iterations", ConfigValue( default=1000, description="Maximum number of iterations before interrupting the high confidence stopping rule." )) CONFIG.declare("HCS_tolerance", ConfigValue( default=0, description="Tolerance on HCS objective value equality. Defaults to Python float equality precision." )) __doc__ = add_docstring_list(__doc__, CONFIG)
[docs] def available(self, exception_flag=True): """Check if solver is available. TODO: For now, it is always available. However, sub-solvers may not always be available, and so this should reflect that possibility. """ return True
def license_is_valid(self): return True def solve(self, model, **kwds): # initialize keyword args config = self.CONFIG(kwds.pop('options', {})) config.set_value(kwds) # initialize the solver solver = SolverFactory(config.solver) # Model sense objectives = model.component_data_objects(Objective, active=True) obj = next(objectives, None) #Check model validity if next(objectives, None) is not None: raise RuntimeError( "Multistart solver is unable to handle model with multiple active objectives.") if obj is None: raise RuntimeError( "Multistart solver is unable to handle model with no active objective.") if obj.polynomial_degree()==0: raise RuntimeError( "Multistart solver received model with constant objective") # store objective values and objective/result information for best # solution obtained objectives = [] obj_sign = 1 if obj.sense == minimize else -1 best_objective = float('inf') * obj_sign best_model = model best_result = None try: # create temporary variable list for value transfer tmp_var_list_name = unique_component_name(model, "_vars_list") setattr(model, tmp_var_list_name, list(model.component_data_objects( ctype=Var, descend_into=True))) best_result = result = solver.solve(model, **config.solver_args) if (result.solver.status is SolverStatus.ok and result.solver.termination_condition is tc.optimal): obj_val = value(obj.expr) best_objective = obj_val objectives.append(obj_val) num_iter = 0 max_iter = config.iterations # if HCS rule is specified, reinitialize completely randomly until # rule specifies stopping using_HCS = config.iterations == -1 HCS_completed = False if using_HCS: assert config.strategy == "rand", \ "High confidence stopping rule requires rand strategy." max_iter = config.HCS_max_iterations while num_iter < max_iter: if using_HCS and should_stop( objectives, config.stopping_mass, config.stopping_delta, config.HCS_tolerance): HCS_completed = True break num_iter += 1 # at first iteration, solve the originally passed model m = model.clone() if num_iter > 1 else model reinitialize_variables(m, config) result = solver.solve(m, **config.solver_args) if (result.solver.status is SolverStatus.ok and result.solver.termination_condition is tc.optimal): model_objectives = m.component_data_objects(Objective, active=True) mobj = next(model_objectives) obj_val = value(mobj.expr) objectives.append(obj_val) if obj_val * obj_sign < obj_sign * best_objective: # objective has improved best_objective = obj_val best_model = m best_result = result if num_iter == 1: # if it's the first iteration, set the best_model and # best_result regardless of solution status in case the # model is infeasible. best_model = m best_result = result if using_HCS and not HCS_completed: logger.warning( "High confidence stopping rule was unable to complete " "after %s iterations. To increase this limit, change the " "HCS_max_iterations flag." % num_iter) # if no better result was found than initial solve, then return # that without needing to copy variables. if best_model is model: return best_result # reassign the given models vars to the new models vars orig_var_list = getattr(model, tmp_var_list_name) best_soln_var_list = getattr(best_model, tmp_var_list_name) for orig_var, new_var in zip(orig_var_list, best_soln_var_list): if not orig_var.is_fixed(): orig_var.value = new_var.value return best_result finally: # Remove temporary variable list delattr(model, tmp_var_list_name) def __enter__(self): return self def __exit__(self, t, v, traceback): pass