def handle_NLP_subproblem_optimal(fixed_nlp, solve_data, config):
"""Copies result to working model, updates bound, adds OA and integer cut,
stores best solution if new one is best"""
copy_var_list_values(
fixed_nlp.MindtPy_utils.variable_list,
solve_data.working_model.MindtPy_utils.variable_list,
config)
for c in fixed_nlp.tmp_duals:
if fixed_nlp.dual.get(c, None) is None:
fixed_nlp.dual[c] = fixed_nlp.tmp_duals[c]
dual_values = list(fixed_nlp.dual[c]
for c in fixed_nlp.MindtPy_utils.constraint_list)
main_objective = next(
fixed_nlp.component_data_objects(Objective, active=True))
if main_objective.sense == minimize:
solve_data.UB = min(value(main_objective.expr), solve_data.UB)
solve_data.solution_improved = solve_data.UB < solve_data.UB_progress[-1]
solve_data.UB_progress.append(solve_data.UB)
else:
solve_data.LB = max(value(main_objective.expr), solve_data.LB)
solve_data.solution_improved = solve_data.LB > solve_data.LB_progress[-1]
solve_data.LB_progress.append(solve_data.LB)
config.logger.info(
'NLP {}: OBJ: {} LB: {} UB: {}'
.format(solve_data.nlp_iter,
value(main_objective.expr),
solve_data.LB, solve_data.UB))
if solve_data.solution_improved:
solve_data.best_solution_found = fixed_nlp.clone()
# Add the linear cut
if config.strategy == 'OA':
copy_var_list_values(fixed_nlp.MindtPy_utils.variable_list,
solve_data.mip.MindtPy_utils.variable_list,
config)
add_oa_cuts(solve_data.mip, dual_values, solve_data, config)
elif config.strategy == 'PSC':
add_psc_cut(solve_data, config)
elif config.strategy == 'GBD':
add_gbd_cut(solve_data, config)
# This adds an integer cut to the feasible_integer_cuts
# ConstraintList, which is not activated by default. However, it
# may be activated as needed in certain situations or for certain
# values of option flags.
var_values = list(v.value for v in fixed_nlp.MindtPy_utils.variable_list)
if config.add_integer_cuts:
add_int_cut(var_values, solve_data, config, feasible=True)
config.call_after_subproblem_feasible(fixed_nlp, solve_data)
def handle_NLP_subproblem_other_termination(fix_nlp, termination_condition,
solve_data, config):
"""Case that fix-NLP is neither optimal nor infeasible (i.e. max_iterations)"""
if termination_condition is tc.maxIterations:
# TODO try something else? Reinitialize with different initial value?
config.logger.info(
'NLP subproblem failed to converge within iteration limit.')
var_values = list(v.value for v in fix_nlp.MindtPy_utils.variable_list)
if config.add_integer_cuts:
add_int_cut(var_values, solve_data,
config) # excludes current discrete option
else:
raise ValueError('MindtPy unable to handle NLP subproblem termination '
'condition of {}'.format(termination_condition))
def handle_NLP_subproblem_infeasible(fix_nlp, solve_data, config):
"""Solve feasibility problem, add cut according to strategy.
The solution of the feasibility problem is copied to the working model.
"""
# TODO try something else? Reinitialize with different initial
# value?
config.logger.info('NLP subproblem was locally infeasible.')
for c in fix_nlp.component_data_objects(ctype=Constraint):
rhs = ((0 if c.upper is None else c.upper) +
(0 if c.lower is None else c.lower))
sign_adjust = 1 if value(c.upper) is None else -1
fix_nlp.dual[c] = (sign_adjust *
max(0, sign_adjust * (rhs - value(c.body))))
dual_values = list(fix_nlp.dual[c]
for c in fix_nlp.MindtPy_utils.constraint_list)
if config.strategy == 'PSC' or config.strategy == 'GBD':
for var in fix_nlp.component_data_objects(ctype=Var,
descend_into=True):
fix_nlp.ipopt_zL_out[var] = 0
fix_nlp.ipopt_zU_out[var] = 0
if var.ub is not None and abs(var.ub -
value(var)) < config.bound_tolerance:
fix_nlp.ipopt_zL_out[var] = 1
elif var.lb is not None and abs(value(var) -
var.lb) < config.bound_tolerance:
fix_nlp.ipopt_zU_out[var] = -1
elif config.strategy == 'OA':
config.logger.info('Solving feasibility problem')
if config.initial_feas:
# add_feas_slacks(fix_nlp, solve_data)
# config.initial_feas = False
feas_NLP, feas_NLP_results = solve_NLP_feas(solve_data, config)
copy_var_list_values(feas_NLP.MindtPy_utils.variable_list,
solve_data.mip.MindtPy_utils.variable_list,
config)
add_oa_cuts(solve_data.mip, dual_values, solve_data, config)
# Add an integer cut to exclude this discrete option
var_values = list(v.value for v in fix_nlp.MindtPy_utils.variable_list)
if config.add_integer_cuts:
add_int_cut(var_values, solve_data,
config) # excludes current discrete option
def solve_NLP_subproblem(solve_data, config):
m = solve_data.working_model.clone()
MindtPy = m.MindtPy_utils
main_objective = next(m.component_data_objects(Objective, active=True))
solve_data.nlp_iter += 1
config.logger.info('NLP %s: Solve subproblem for fixed binaries.' %
(solve_data.nlp_iter, ))
# Set up NLP
for v in MindtPy.variable_list:
if v.is_binary():
v.fix(int(round(value(v))))
# restore original variable values
for nlp_var, orig_val in zip(MindtPy.variable_list,
solve_data.initial_var_values):
if not nlp_var.fixed and not nlp_var.is_binary():
nlp_var.value = orig_val
MindtPy.MindtPy_linear_cuts.deactivate()
m.tmp_duals = ComponentMap()
for c in m.component_data_objects(ctype=Constraint,
active=True,
descend_into=True):
rhs = ((0 if c.upper is None else c.upper) +
(0 if c.lower is None else c.lower))
sign_adjust = 1 if value(c.upper) is None else -1
m.tmp_duals[c] = sign_adjust * max(0,
sign_adjust * (rhs - value(c.body)))
# TODO check sign_adjust
t = TransformationFactory('contrib.deactivate_trivial_constraints')
t.apply_to(m, tmp=True, ignore_infeasible=True)
# Solve the NLP
# m.pprint() # print nlp problem for debugging
with SuppressInfeasibleWarning():
results = SolverFactory(config.nlp_solver).solve(
m, **config.nlp_solver_args)
var_values = list(v.value for v in MindtPy.variable_list)
subprob_terminate_cond = results.solver.termination_condition
if subprob_terminate_cond is tc.optimal:
copy_var_list_values(
m.MindtPy_utils.variable_list,
solve_data.working_model.MindtPy_utils.variable_list, config)
for c in m.tmp_duals:
if m.dual.get(c, None) is None:
m.dual[c] = m.tmp_duals[c]
duals = list(m.dual[c] for c in MindtPy.constraint_list)
if main_objective.sense == minimize:
solve_data.UB = min(value(main_objective.expr), solve_data.UB)
solve_data.solution_improved = solve_data.UB < solve_data.UB_progress[
-1]
solve_data.UB_progress.append(solve_data.UB)
else:
solve_data.LB = max(value(main_objective.expr), solve_data.LB)
solve_data.solution_improved = solve_data.LB > solve_data.LB_progress[
-1]
solve_data.LB_progress.append(solve_data.LB)
config.logger.info('NLP {}: OBJ: {} LB: {} UB: {}'.format(
solve_data.nlp_iter, value(main_objective.expr), solve_data.LB,
solve_data.UB))
if solve_data.solution_improved:
solve_data.best_solution_found = m.clone()
# Add the linear cut
if config.strategy == 'OA':
add_oa_cut(var_values, duals, solve_data, config)
elif config.strategy == 'PSC':
add_psc_cut(solve_data, config)
elif config.strategy == 'GBD':
add_gbd_cut(solve_data, config)
# This adds an integer cut to the feasible_integer_cuts
# ConstraintList, which is not activated by default. However, it
# may be activated as needed in certain situations or for certain
# values of option flags.
add_int_cut(var_values, solve_data, config, feasible=True)
config.call_after_subproblem_feasible(m, solve_data)
elif subprob_terminate_cond is tc.infeasible:
# TODO try something else? Reinitialize with different initial
# value?
config.logger.info('NLP subproblem was locally infeasible.')
for c in m.component_data_objects(ctype=Constraint,
active=True,
descend_into=True):
rhs = ((0 if c.upper is None else c.upper) +
(0 if c.lower is None else c.lower))
sign_adjust = 1 if value(c.upper) is None else -1
m.dual[c] = sign_adjust * max(0, sign_adjust *
(rhs - value(c.body)))
for var in m.component_data_objects(ctype=Var, descend_into=True):
if config.strategy == 'PSC' or config.strategy == 'GBD':
m.ipopt_zL_out[var] = 0
m.ipopt_zU_out[var] = 0
if var.ub is not None and abs(
var.ub - value(var)) < config.bound_tolerance:
m.ipopt_zL_out[var] = 1
elif var.lb is not None and abs(
value(var) - var.lb) < config.bound_tolerance:
m.ipopt_zU_out[var] = -1
# m.pprint() #print infeasible nlp problem for debugging
if config.strategy == 'OA':
config.logger.info('Solving feasibility problem')
if config.initial_feas:
# add_feas_slacks(m, solve_data)
# config.initial_feas = False
var_values, duals = solve_NLP_feas(solve_data, config)
add_oa_cut(var_values, duals, solve_data, config)
# Add an integer cut to exclude this discrete option
add_int_cut(var_values, solve_data, config)
elif subprob_terminate_cond is tc.maxIterations:
# TODO try something else? Reinitialize with different initial
# value?
config.logger.info(
'NLP subproblem failed to converge within iteration limit.')
# Add an integer cut to exclude this discrete option
add_int_cut(solve_data, config)
else:
raise ValueError('MindtPy unable to handle NLP subproblem termination '
'condition of {}'.format(subprob_terminate_cond))
# Call the NLP post-solve callback
config.call_after_subproblem_solve(m, solve_data)
def solve_NLP_subproblem(solve_data, config):
m = solve_data.working_model.clone()
MindtPy = m.MindtPy_utils
main_objective = next(m.component_data_objects(Objective, active=True))
solve_data.nlp_iter += 1
config.logger.info('NLP %s: Solve subproblem for fixed binaries.'
% (solve_data.nlp_iter,))
# Set up NLP
for v in MindtPy.variable_list:
if v.is_binary():
v.fix(int(round(value(v))))
# restore original variable values
for nlp_var, orig_val in zip(
MindtPy.variable_list,
solve_data.initial_var_values):
if not nlp_var.fixed and not nlp_var.is_binary():
nlp_var.value = orig_val
MindtPy.MindtPy_linear_cuts.deactivate()
m.tmp_duals = ComponentMap()
for c in m.component_data_objects(ctype=Constraint, active=True,
descend_into=True):
rhs = ((0 if c.upper is None else c.upper) +
(0 if c.lower is None else c.lower))
sign_adjust = 1 if value(c.upper) is None else -1
m.tmp_duals[c] = sign_adjust * max(0,
sign_adjust * (rhs - value(c.body)))
# TODO check sign_adjust
t = TransformationFactory('contrib.deactivate_trivial_constraints')
t.apply_to(m, tmp=True, ignore_infeasible=True)
# Solve the NLP
# m.pprint() # print nlp problem for debugging
with SuppressInfeasibleWarning():
results = SolverFactory(config.nlp_solver).solve(
m, **config.nlp_solver_args)
var_values = list(v.value for v in MindtPy.variable_list)
subprob_terminate_cond = results.solver.termination_condition
if subprob_terminate_cond is tc.optimal:
copy_var_list_values(
m.MindtPy_utils.variable_list,
solve_data.working_model.MindtPy_utils.variable_list,
config)
for c in m.tmp_duals:
if m.dual.get(c, None) is None:
m.dual[c] = m.tmp_duals[c]
duals = list(m.dual[c] for c in MindtPy.constraint_list)
if main_objective.sense == minimize:
solve_data.UB = min(value(main_objective.expr), solve_data.UB)
solve_data.solution_improved = solve_data.UB < solve_data.UB_progress[-1]
solve_data.UB_progress.append(solve_data.UB)
else:
solve_data.LB = max(value(main_objective.expr), solve_data.LB)
solve_data.solution_improved = solve_data.LB > solve_data.LB_progress[-1]
solve_data.LB_progress.append(solve_data.LB)
config.logger.info(
'NLP {}: OBJ: {} LB: {} UB: {}'
.format(solve_data.nlp_iter, value(main_objective.expr), solve_data.LB, solve_data.UB))
if solve_data.solution_improved:
solve_data.best_solution_found = m.clone()
# Add the linear cut
if config.strategy == 'OA':
add_oa_cut(var_values, duals, solve_data, config)
elif config.strategy == 'PSC':
add_psc_cut(solve_data, config)
elif config.strategy == 'GBD':
add_gbd_cut(solve_data, config)
# This adds an integer cut to the feasible_integer_cuts
# ConstraintList, which is not activated by default. However, it
# may be activated as needed in certain situations or for certain
# values of option flags.
add_int_cut(var_values, solve_data, config, feasible=True)
config.call_after_subproblem_feasible(m, solve_data)
elif subprob_terminate_cond is tc.infeasible:
# TODO try something else? Reinitialize with different initial
# value?
config.logger.info('NLP subproblem was locally infeasible.')
for c in m.component_data_objects(ctype=Constraint, active=True,
descend_into=True):
rhs = ((0 if c.upper is None else c.upper) +
(0 if c.lower is None else c.lower))
sign_adjust = 1 if value(c.upper) is None else -1
m.dual[c] = sign_adjust * max(0,
sign_adjust * (rhs - value(c.body)))
for var in m.component_data_objects(ctype=Var,
descend_into=True):
if config.strategy == 'PSC' or config.strategy == 'GBD':
m.ipopt_zL_out[var] = 0
m.ipopt_zU_out[var] = 0
if var.ub is not None and abs(var.ub - value(var)) < config.bound_tolerance:
m.ipopt_zL_out[var] = 1
elif var.lb is not None and abs(value(var) - var.lb) < config.bound_tolerance:
m.ipopt_zU_out[var] = -1
# m.pprint() #print infeasible nlp problem for debugging
if config.strategy == 'OA':
config.logger.info('Solving feasibility problem')
if config.initial_feas:
# add_feas_slacks(m, solve_data)
# config.initial_feas = False
var_values, duals = solve_NLP_feas(solve_data, config)
add_oa_cut(var_values, duals, solve_data, config)
# Add an integer cut to exclude this discrete option
add_int_cut(var_values, solve_data, config)
elif subprob_terminate_cond is tc.maxIterations:
# TODO try something else? Reinitialize with different initial
# value?
config.logger.info('NLP subproblem failed to converge within iteration limit.')
# Add an integer cut to exclude this discrete option
add_int_cut(solve_data, config)
else:
raise ValueError(
'MindtPy unable to handle NLP subproblem termination '
'condition of {}'.format(subprob_terminate_cond))
# Call the NLP post-solve callback
config.call_after_subproblem_solve(m, solve_data)