def MindtPy_iteration_loop(solve_data, config):
"""Main loop for MindtPy Algorithms.
This is the outermost function for the algorithms in this package; this function controls the progression of
solving the model.
Args:
solve_data (MindtPySolveData): data container that holds solve-instance data.
config (ConfigBlock): the specific configurations for MindtPy.
Raises:
ValueError: the strategy value is not correct or not included.
"""
last_iter_cuts = False
while solve_data.mip_iter < config.iteration_limit:
solve_data.mip_subiter = 0
# solve MILP main problem
if config.strategy in {'OA', 'GOA', 'ECP'}:
main_mip, main_mip_results = solve_main(solve_data, config)
if main_mip_results is not None:
if not config.single_tree:
if main_mip_results.solver.termination_condition is tc.optimal:
handle_main_optimal(main_mip, solve_data, config)
elif main_mip_results.solver.termination_condition is tc.infeasible:
handle_main_infeasible(main_mip, solve_data, config)
last_iter_cuts = True
break
else:
handle_main_other_conditions(main_mip,
main_mip_results,
solve_data, config)
# Call the MILP post-solve callback
with time_code(solve_data.timing, 'Call after main solve'):
config.call_after_main_solve(main_mip, solve_data)
else:
config.logger.info('Algorithm should terminate here.')
break
else:
raise ValueError()
# regularization is activated after the first feasible solution is found.
if config.add_regularization is not None and solve_data.best_solution_found is not None and not config.single_tree:
# the main problem might be unbounded, regularization is activated only when a valid bound is provided.
if (solve_data.objective_sense == minimize and solve_data.LB !=
float('-inf')) or (solve_data.objective_sense == maximize
and solve_data.UB != float('inf')):
main_mip, main_mip_results = solve_main(
solve_data, config, regularization_problem=True)
handle_regularization_main_tc(main_mip, main_mip_results,
solve_data, config)
# TODO: add descriptions for the following code
if config.add_regularization is not None and config.single_tree:
solve_data.curr_int_sol = get_integer_solution(solve_data.mip,
string_zero=True)
copy_var_list_values(
main_mip.MindtPy_utils.variable_list,
solve_data.working_model.MindtPy_utils.variable_list, config)
if solve_data.curr_int_sol not in set(solve_data.integer_list):
fixed_nlp, fixed_nlp_result = solve_subproblem(
solve_data, config)
handle_nlp_subproblem_tc(fixed_nlp, fixed_nlp_result,
solve_data, config)
if algorithm_should_terminate(solve_data, config, check_cycling=True):
last_iter_cuts = False
break
if not config.single_tree and config.strategy != 'ECP': # if we don't use lazy callback, i.e. LP_NLP
# Solve NLP subproblem
# The constraint linearization happens in the handlers
if not config.solution_pool:
fixed_nlp, fixed_nlp_result = solve_subproblem(
solve_data, config)
handle_nlp_subproblem_tc(fixed_nlp, fixed_nlp_result,
solve_data, config)
# Call the NLP post-solve callback
with time_code(solve_data.timing,
'Call after subproblem solve'):
config.call_after_subproblem_solve(fixed_nlp, solve_data)
if algorithm_should_terminate(solve_data,
config,
check_cycling=False):
last_iter_cuts = True
break
else:
if config.mip_solver == 'cplex_persistent':
solution_pool_names = main_mip_results._solver_model.solution.pool.get_names(
)
elif config.mip_solver == 'gurobi_persistent':
solution_pool_names = list(
range(main_mip_results._solver_model.SolCount))
# list to store the name and objective value of the solutions in the solution pool
solution_name_obj = []
for name in solution_pool_names:
if config.mip_solver == 'cplex_persistent':
obj = main_mip_results._solver_model.solution.pool.get_objective_value(
name)
elif config.mip_solver == 'gurobi_persistent':
main_mip_results._solver_model.setParam(
gurobipy.GRB.Param.SolutionNumber, name)
obj = main_mip_results._solver_model.PoolObjVal
solution_name_obj.append([name, obj])
solution_name_obj.sort(
key=itemgetter(1),
reverse=solve_data.objective_sense == maximize)
counter = 0
for name, _ in solution_name_obj:
# the optimal solution of the main problem has been added to integer_list above
# so we should skip checking cycling for the first solution in the solution pool
if counter >= 1:
copy_var_list_values_from_solution_pool(
solve_data.mip.MindtPy_utils.variable_list,
solve_data.working_model.MindtPy_utils.
variable_list,
config,
solver_model=main_mip_results._solver_model,
var_map=main_mip_results.
_pyomo_var_to_solver_var_map,
solution_name=name)
solve_data.curr_int_sol = get_integer_solution(
solve_data.working_model)
if solve_data.curr_int_sol in set(
solve_data.integer_list):
config.logger.info(
'The same combination has been explored and will be skipped here.'
)
continue
else:
solve_data.integer_list.append(
solve_data.curr_int_sol)
counter += 1
fixed_nlp, fixed_nlp_result = solve_subproblem(
solve_data, config)
handle_nlp_subproblem_tc(fixed_nlp, fixed_nlp_result,
solve_data, config)
# Call the NLP post-solve callback
with time_code(solve_data.timing,
'Call after subproblem solve'):
config.call_after_subproblem_solve(
fixed_nlp, solve_data)
if algorithm_should_terminate(solve_data,
config,
check_cycling=False):
last_iter_cuts = True
break
if counter >= config.num_solution_iteration:
break
if config.strategy == 'ECP':
add_ecp_cuts(solve_data.mip, solve_data, config)
# if config.strategy == 'PSC':
# # If the hybrid algorithm is not making progress, switch to OA.
# progress_required = 1E-6
# if solve_data.objective_sense == minimize:
# log = solve_data.LB_progress
# sign_adjust = 1
# else:
# log = solve_data.UB_progress
# sign_adjust = -1
# # Maximum number of iterations in which the lower (optimistic)
# # bound does not improve before switching to OA
# max_nonimprove_iter = 5
# making_progress = True
# # TODO-romeo Unnecessary for OA and ROA, right?
# for i in range(1, max_nonimprove_iter + 1):
# try:
# if (sign_adjust * log[-i]
# <= (log[-i - 1] + progress_required)
# * sign_adjust):
# making_progress = False
# else:
# making_progress = True
# break
# except IndexError:
# # Not enough history yet, keep going.
# making_progress = True
# break
# if not making_progress and (
# config.strategy == 'hPSC' or
# config.strategy == 'PSC'):
# config.logger.info(
# 'Not making enough progress for {} iterations. '
# 'Switching to OA.'.format(max_nonimprove_iter))
# config.strategy = 'OA'
# if add_no_good_cuts is True, the bound obtained in the last iteration is no reliable.
# we correct it after the iteration.
if (
config.add_no_good_cuts or config.use_tabu_list
) and config.strategy != 'FP' and not solve_data.should_terminate and config.add_regularization is None:
fix_dual_bound(solve_data, config, last_iter_cuts)
config.logger.info(
' ============================================================================================='
)
def test_handle_termination_condition(self):
"""Test the outer approximation decomposition algorithm."""
model = SimpleMINLP()
config = _get_MindtPy_config()
solve_data = set_up_solve_data(model, config)
with time_code(solve_data.timing, 'total', is_main_timer=True), \
create_utility_block(solve_data.working_model, 'MindtPy_utils', solve_data):
MindtPy = solve_data.working_model.MindtPy_utils
MindtPy = solve_data.working_model.MindtPy_utils
setup_results_object(solve_data, config)
process_objective(
solve_data,
config,
move_linear_objective=(config.init_strategy == 'FP' or
config.add_regularization is not None),
use_mcpp=config.use_mcpp,
update_var_con_list=config.add_regularization is None)
feas = MindtPy.feas_opt = Block()
feas.deactivate()
feas.feas_constraints = ConstraintList(
doc='Feasibility Problem Constraints')
lin = MindtPy.cuts = Block()
lin.deactivate()
if config.feasibility_norm == 'L1' or config.feasibility_norm == 'L2':
feas.nl_constraint_set = RangeSet(
len(MindtPy.nonlinear_constraint_list),
doc='Integer index set over the nonlinear constraints.')
# Create slack variables for feasibility problem
feas.slack_var = Var(feas.nl_constraint_set,
domain=NonNegativeReals,
initialize=1)
else:
feas.slack_var = Var(domain=NonNegativeReals, initialize=1)
# no-good cuts exclude particular discrete decisions
lin.no_good_cuts = ConstraintList(doc='no-good cuts')
fixed_nlp = solve_data.working_model.clone()
TransformationFactory('core.fix_integer_vars').apply_to(fixed_nlp)
MindtPy_initialize_main(solve_data, config)
# test handle_subproblem_other_termination
termination_condition = tc.maxIterations
config.add_no_good_cuts = True
handle_subproblem_other_termination(fixed_nlp,
termination_condition,
solve_data, config)
self.assertEqual(
len(solve_data.mip.MindtPy_utils.cuts.no_good_cuts), 1)
# test handle_main_other_conditions
main_mip, main_mip_results = solve_main(solve_data, config)
main_mip_results.solver.termination_condition = tc.infeasible
handle_main_other_conditions(solve_data.mip, main_mip_results,
solve_data, config)
self.assertIs(solve_data.results.solver.termination_condition,
tc.feasible)
main_mip_results.solver.termination_condition = tc.unbounded
handle_main_other_conditions(solve_data.mip, main_mip_results,
solve_data, config)
self.assertIn(main_mip.MindtPy_utils.objective_bound,
main_mip.component_data_objects(ctype=Constraint))
main_mip.MindtPy_utils.del_component('objective_bound')
main_mip_results.solver.termination_condition = tc.infeasibleOrUnbounded
handle_main_other_conditions(solve_data.mip, main_mip_results,
solve_data, config)
self.assertIn(main_mip.MindtPy_utils.objective_bound,
main_mip.component_data_objects(ctype=Constraint))
main_mip_results.solver.termination_condition = tc.maxTimeLimit
handle_main_other_conditions(solve_data.mip, main_mip_results,
solve_data, config)
self.assertIs(solve_data.results.solver.termination_condition,
tc.maxTimeLimit)
main_mip_results.solver.termination_condition = tc.other
main_mip_results.solution.status = SolutionStatus.feasible
handle_main_other_conditions(solve_data.mip, main_mip_results,
solve_data, config)
for v1, v2 in zip(
main_mip.MindtPy_utils.variable_list,
solve_data.working_model.MindtPy_utils.variable_list):
self.assertEqual(v1.value, v2.value)
# test handle_feasibility_subproblem_tc
feas_subproblem = solve_data.working_model.clone()
add_feas_slacks(feas_subproblem, config)
MindtPy = feas_subproblem.MindtPy_utils
MindtPy.feas_opt.activate()
if config.feasibility_norm == 'L1':
MindtPy.feas_obj = Objective(expr=sum(
s for s in MindtPy.feas_opt.slack_var[...]),
sense=minimize)
elif config.feasibility_norm == 'L2':
MindtPy.feas_obj = Objective(expr=sum(
s * s for s in MindtPy.feas_opt.slack_var[...]),
sense=minimize)
else:
MindtPy.feas_obj = Objective(expr=MindtPy.feas_opt.slack_var,
sense=minimize)
handle_feasibility_subproblem_tc(tc.optimal, MindtPy, solve_data,
config)
handle_feasibility_subproblem_tc(tc.infeasible, MindtPy,
solve_data, config)
self.assertIs(solve_data.should_terminate, True)
self.assertIs(solve_data.results.solver.status, SolverStatus.error)
solve_data.should_terminate = False
solve_data.results.solver.status = None
handle_feasibility_subproblem_tc(tc.maxIterations, MindtPy,
solve_data, config)
self.assertIs(solve_data.should_terminate, True)
self.assertIs(solve_data.results.solver.status, SolverStatus.error)
solve_data.should_terminate = False
solve_data.results.solver.status = None
handle_feasibility_subproblem_tc(tc.solverFailure, MindtPy,
solve_data, config)
self.assertIs(solve_data.should_terminate, True)
self.assertIs(solve_data.results.solver.status, SolverStatus.error)
# test NLP subproblem infeasible
solve_data.working_model.Y[1].value = 0
solve_data.working_model.Y[2].value = 0
solve_data.working_model.Y[3].value = 0
fixed_nlp, fixed_nlp_results = solve_subproblem(solve_data, config)
solve_data.working_model.Y[1].value = None
solve_data.working_model.Y[2].value = None
solve_data.working_model.Y[3].value = None
# test handle_nlp_subproblem_tc
fixed_nlp_results.solver.termination_condition = tc.maxTimeLimit
handle_nlp_subproblem_tc(fixed_nlp, fixed_nlp_results, solve_data,
config)
self.assertIs(solve_data.should_terminate, True)
self.assertIs(solve_data.results.solver.termination_condition,
tc.maxTimeLimit)
fixed_nlp_results.solver.termination_condition = tc.maxEvaluations
handle_nlp_subproblem_tc(fixed_nlp, fixed_nlp_results, solve_data,
config)
self.assertIs(solve_data.should_terminate, True)
self.assertIs(solve_data.results.solver.termination_condition,
tc.maxEvaluations)
fixed_nlp_results.solver.termination_condition = tc.maxIterations
handle_nlp_subproblem_tc(fixed_nlp, fixed_nlp_results, solve_data,
config)
self.assertIs(solve_data.should_terminate, True)
self.assertIs(solve_data.results.solver.termination_condition,
tc.maxEvaluations)
# test handle_fp_main_tc
config.init_strategy = 'FP'
solve_data.fp_iter = 1
init_rNLP(solve_data, config)
feas_main, feas_main_results = solve_main(solve_data,
config,
fp=True)
feas_main_results.solver.termination_condition = tc.optimal
fp_should_terminate = handle_fp_main_tc(feas_main_results,
solve_data, config)
self.assertIs(fp_should_terminate, False)
feas_main_results.solver.termination_condition = tc.maxTimeLimit
fp_should_terminate = handle_fp_main_tc(feas_main_results,
solve_data, config)
self.assertIs(fp_should_terminate, True)
self.assertIs(solve_data.results.solver.termination_condition,
tc.maxTimeLimit)
feas_main_results.solver.termination_condition = tc.infeasible
fp_should_terminate = handle_fp_main_tc(feas_main_results,
solve_data, config)
self.assertIs(fp_should_terminate, True)
feas_main_results.solver.termination_condition = tc.unbounded
fp_should_terminate = handle_fp_main_tc(feas_main_results,
solve_data, config)
self.assertIs(fp_should_terminate, True)
feas_main_results.solver.termination_condition = tc.other
feas_main_results.solution.status = SolutionStatus.feasible
fp_should_terminate = handle_fp_main_tc(feas_main_results,
solve_data, config)
self.assertIs(fp_should_terminate, False)
feas_main_results.solver.termination_condition = tc.solverFailure
fp_should_terminate = handle_fp_main_tc(feas_main_results,
solve_data, config)
self.assertIs(fp_should_terminate, True)
# test generate_norm_constraint
fp_nlp = solve_data.working_model.clone()
config.fp_main_norm = 'L1'
generate_norm_constraint(fp_nlp, solve_data, config)
self.assertIsNotNone(
fp_nlp.MindtPy_utils.find_component('L1_norm_constraint'))
config.fp_main_norm = 'L2'
generate_norm_constraint(fp_nlp, solve_data, config)
self.assertIsNotNone(fp_nlp.find_component('norm_constraint'))
fp_nlp.del_component('norm_constraint')
config.fp_main_norm = 'L_infinity'
generate_norm_constraint(fp_nlp, solve_data, config)
self.assertIsNotNone(fp_nlp.find_component('norm_constraint'))
# test set_solver_options
config.mip_solver = 'gams'
config.threads = 1
opt = SolverFactory(config.mip_solver)
set_solver_options(opt,
solve_data,
config,
'mip',
regularization=False)
config.mip_solver = 'gurobi'
config.mip_regularization_solver = 'gurobi'
config.regularization_mip_threads = 1
opt = SolverFactory(config.mip_solver)
set_solver_options(opt,
solve_data,
config,
'mip',
regularization=True)
config.nlp_solver = 'gams'
config.nlp_solver_args['solver'] = 'ipopt'
set_solver_options(opt,
solve_data,
config,
'nlp',
regularization=False)
config.nlp_solver_args['solver'] = 'ipopth'
set_solver_options(opt,
solve_data,
config,
'nlp',
regularization=False)
config.nlp_solver_args['solver'] = 'conopt'
set_solver_options(opt,
solve_data,
config,
'nlp',
regularization=False)
config.nlp_solver_args['solver'] = 'msnlp'
set_solver_options(opt,
solve_data,
config,
'nlp',
regularization=False)
config.nlp_solver_args['solver'] = 'baron'
set_solver_options(opt,
solve_data,
config,
'nlp',
regularization=False)
# test algorithm_should_terminate
solve_data.should_terminate = True
solve_data.UB = float('inf')
self.assertIs(
algorithm_should_terminate(solve_data,
config,
check_cycling=False), True)
self.assertIs(solve_data.results.solver.termination_condition,
tc.noSolution)
solve_data.UB = 100
self.assertIs(
algorithm_should_terminate(solve_data,
config,
check_cycling=False), True)
self.assertIs(solve_data.results.solver.termination_condition,
tc.feasible)
solve_data.objective_sense = maximize
solve_data.LB = float('-inf')
self.assertIs(
algorithm_should_terminate(solve_data,
config,
check_cycling=False), True)
self.assertIs(solve_data.results.solver.termination_condition,
tc.noSolution)
solve_data.LB = 100
self.assertIs(
algorithm_should_terminate(solve_data,
config,
check_cycling=False), True)
self.assertIs(solve_data.results.solver.termination_condition,
tc.feasible)
def MindtPy_iteration_loop(solve_data, config):
"""
Main loop for MindtPy Algorithms
This is the outermost function for the algorithms in this package; this function controls the progression of
solving the model.
Parameters
----------
solve_data: MindtPy Data Container
data container that holds solve-instance data
config: ConfigBlock
contains the specific configurations for the algorithm
"""
last_iter_cuts = False
while solve_data.mip_iter < config.iteration_limit:
config.logger.info(
'---MindtPy main Iteration %s---'
% (solve_data.mip_iter+1))
solve_data.mip_subiter = 0
# solve MILP main problem
if config.strategy in {'OA', 'GOA', 'ECP'}:
main_mip, main_mip_results = solve_main(solve_data, config)
if main_mip_results is not None:
if not config.single_tree:
if main_mip_results.solver.termination_condition is tc.optimal:
handle_main_optimal(main_mip, solve_data, config)
elif main_mip_results.solver.termination_condition is tc.infeasible:
handle_main_infeasible(main_mip, solve_data, config)
last_iter_cuts = True
break
else:
handle_main_other_conditions(
main_mip, main_mip_results, solve_data, config)
# Call the MILP post-solve callback
with time_code(solve_data.timing, 'Call after main solve'):
config.call_after_main_solve(main_mip, solve_data)
else:
config.logger.info('Algorithm should terminate here.')
break
else:
raise NotImplementedError()
# regularization is activated after the first feasible solution is found.
if config.add_regularization is not None and solve_data.best_solution_found is not None and not config.single_tree:
# the main problem might be unbounded, regularization is activated only when a valid bound is provided.
if (solve_data.objective_sense == minimize and solve_data.LB != float('-inf')) or (solve_data.objective_sense == maximize and solve_data.UB != float('inf')):
main_mip, main_mip_results = solve_main(
solve_data, config, regularization_problem=True)
handle_regularization_main_tc(
main_mip, main_mip_results, solve_data, config)
if config.add_regularization is not None and config.single_tree:
solve_data.curr_int_sol = get_integer_solution(
solve_data.mip, string_zero=True)
copy_var_list_values(
main_mip.MindtPy_utils.variable_list,
solve_data.working_model.MindtPy_utils.variable_list,
config)
if solve_data.curr_int_sol not in set(solve_data.integer_list):
fixed_nlp, fixed_nlp_result = solve_subproblem(
solve_data, config)
handle_nlp_subproblem_tc(
fixed_nlp, fixed_nlp_result, solve_data, config)
if algorithm_should_terminate(solve_data, config, check_cycling=True):
last_iter_cuts = False
break
if not config.single_tree and config.strategy != 'ECP': # if we don't use lazy callback, i.e. LP_NLP
# Solve NLP subproblem
# The constraint linearization happens in the handlers
fixed_nlp, fixed_nlp_result = solve_subproblem(solve_data, config)
handle_nlp_subproblem_tc(
fixed_nlp, fixed_nlp_result, solve_data, config)
# Call the NLP post-solve callback
with time_code(solve_data.timing, 'Call after subproblem solve'):
config.call_after_subproblem_solve(fixed_nlp, solve_data)
if algorithm_should_terminate(solve_data, config, check_cycling=False):
last_iter_cuts = True
break
if config.strategy == 'ECP':
add_ecp_cuts(solve_data.mip, solve_data, config)
# if config.strategy == 'PSC':
# # If the hybrid algorithm is not making progress, switch to OA.
# progress_required = 1E-6
# if solve_data.objective_sense == minimize:
# log = solve_data.LB_progress
# sign_adjust = 1
# else:
# log = solve_data.UB_progress
# sign_adjust = -1
# # Maximum number of iterations in which the lower (optimistic)
# # bound does not improve before switching to OA
# max_nonimprove_iter = 5
# making_progress = True
# # TODO-romeo Unneccesary for OA and ROA, right?
# for i in range(1, max_nonimprove_iter + 1):
# try:
# if (sign_adjust * log[-i]
# <= (log[-i - 1] + progress_required)
# * sign_adjust):
# making_progress = False
# else:
# making_progress = True
# break
# except IndexError:
# # Not enough history yet, keep going.
# making_progress = True
# break
# if not making_progress and (
# config.strategy == 'hPSC' or
# config.strategy == 'PSC'):
# config.logger.info(
# 'Not making enough progress for {} iterations. '
# 'Switching to OA.'.format(max_nonimprove_iter))
# config.strategy = 'OA'
# if add_no_good_cuts is True, the bound obtained in the last iteration is no reliable.
# we correct it after the iteration.
if (config.add_no_good_cuts or config.use_tabu_list) and config.strategy is not 'FP' and not solve_data.should_terminate and config.add_regularization is None:
bound_fix(solve_data, config, last_iter_cuts)