def init_fixed_disjuncts(solve_data, config):
"""Initialize by solving the problem with the current disjunct values."""
# TODO error checking to make sure that the user gave proper disjuncts
# fix the disjuncts in the linear GDP and send for solution.
solve_data.mip_iteration += 1
linear_GDP = solve_data.linear_GDP.clone()
config.logger.info(
"Generating initial linear GDP approximation by "
"solving subproblem with original user-specified disjunct values.")
TransformationFactory('gdp.fix_disjuncts').apply_to(linear_GDP)
mip_result = solve_linear_GDP(linear_GDP, solve_data, config)
if mip_result:
_, mip_var_values = mip_result
# use the mip_var_values to create the NLP subproblem
nlp_model = solve_data.working_model.clone()
# copy in the discrete variable values
copy_and_fix_mip_values_to_nlp(nlp_model.GDPopt_utils.working_var_list,
mip_var_values, config)
TransformationFactory('gdp.fix_disjuncts').apply_to(nlp_model)
solve_data.nlp_iteration += 1
nlp_result = solve_NLP(nlp_model, solve_data, config)
nlp_feasible, nlp_var_values, nlp_duals = nlp_result
if nlp_feasible:
update_nlp_progress_indicators(nlp_model, solve_data, config)
add_outer_approximation_cuts(nlp_var_values, nlp_duals, solve_data,
config)
add_integer_cut(mip_var_values,
solve_data,
config,
feasible=nlp_feasible)
else:
config.logger.error('Linear GDP infeasible for initial user-specified '
'disjunct values. '
'Skipping initialization.')
def solve_LOA_subproblem(mip_var_values, solve_data, config):
"""Set up and solve the local LOA subproblem."""
nlp_model = solve_data.working_model.clone()
solve_data.nlp_iteration += 1
# copy in the discrete variable values
copy_and_fix_mip_values_to_nlp(nlp_model.GDPopt_utils.working_var_list,
mip_var_values, config)
TransformationFactory('gdp.fix_disjuncts').apply_to(nlp_model)
nlp_result = solve_NLP(nlp_model, solve_data, config)
if nlp_result.feasible: # NLP is feasible
update_nlp_progress_indicators(nlp_model, solve_data, config)
return nlp_result
def init_max_binaries(solve_data, config):
"""Initialize by maximizing binary variables and disjuncts.
This function activates as many binary variables and disjucts as
feasible.
"""
solve_data.mip_iteration += 1
linear_GDP = solve_data.linear_GDP.clone()
config.logger.info("Generating initial linear GDP approximation by "
"solving a subproblem that maximizes "
"the sum of all binary and logical variables.")
# Set up binary maximization objective
linear_GDP.GDPopt_utils.objective.deactivate()
binary_vars = (v for v in linear_GDP.component_data_objects(
ctype=Var, descend_into=(Block, Disjunct))
if v.is_binary() and not v.fixed)
linear_GDP.GDPopt_utils.max_binary_obj = Objective(expr=sum(binary_vars),
sense=maximize)
# Solve
mip_results = solve_linear_GDP(linear_GDP, solve_data, config)
if mip_results:
_, mip_var_values = mip_results
# use the mip_var_values to create the NLP subproblem
nlp_model = solve_data.working_model.clone()
# copy in the discrete variable values
copy_and_fix_mip_values_to_nlp(nlp_model.GDPopt_utils.working_var_list,
mip_var_values, config)
TransformationFactory('gdp.fix_disjuncts').apply_to(nlp_model)
solve_data.nlp_iteration += 1
nlp_result = solve_NLP(nlp_model, solve_data, config)
nlp_feasible, nlp_var_values, nlp_duals = nlp_result
if nlp_feasible:
update_nlp_progress_indicators(nlp_model, solve_data, config)
add_outer_approximation_cuts(nlp_var_values, nlp_duals, solve_data,
config)
add_integer_cut(mip_var_values,
solve_data,
config,
feasible=nlp_feasible)
else:
config.logger.info(
"Linear relaxation for initialization was infeasible. "
"Problem is infeasible.")
return False
def init_custom_disjuncts(solve_data, config):
"""Initialize by using user-specified custom disjuncts."""
# TODO error checking to make sure that the user gave proper disjuncts
for active_disjunct_set in config.custom_init_disjuncts:
# custom_init_disjuncts contains a list of sets, giving the disjuncts
# active at each initialization iteration
# fix the disjuncts in the linear GDP and send for solution.
solve_data.mip_iteration += 1
linear_GDP = solve_data.linear_GDP.clone()
config.logger.info(
"Generating initial linear GDP approximation by "
"solving subproblems with user-specified active disjuncts.")
for orig_disj, clone_disj in zip(
solve_data.original_model.GDPopt_utils.orig_disjuncts_list,
linear_GDP.GDPopt_utils.orig_disjuncts_list):
if orig_disj in active_disjunct_set:
clone_disj.indicator_var.fix(1)
mip_result = solve_linear_GDP(linear_GDP, solve_data, config)
if mip_result:
_, mip_var_values = mip_result
# use the mip_var_values to create the NLP subproblem
nlp_model = solve_data.working_model.clone()
# copy in the discrete variable values
copy_and_fix_mip_values_to_nlp(
nlp_model.GDPopt_utils.working_var_list, mip_var_values,
config)
TransformationFactory('gdp.fix_disjuncts').apply_to(nlp_model)
solve_data.nlp_iteration += 1
nlp_result = solve_NLP(nlp_model, solve_data, config)
nlp_feasible, nlp_var_values, nlp_duals = nlp_result
if nlp_feasible:
update_nlp_progress_indicators(nlp_model, solve_data, config)
add_outer_approximation_cuts(nlp_var_values, nlp_duals,
solve_data, config)
add_integer_cut(mip_var_values,
solve_data,
config,
feasible=nlp_feasible)
else:
config.logger.error('Linear GDP infeasible for user-specified '
'custom initialization disjunct set %s. '
'Skipping that set and continuing on.' %
list(disj.name
for disj in active_disjunct_set))
def init_set_covering(solve_data, config):
"""Initialize by solving problems to cover the set of all disjuncts.
The purpose of this initialization is to generate linearizations
corresponding to each of the disjuncts.
This work is based upon prototyping work done by Eloy Fernandez at
Carnegie Mellon University.
"""
config.logger.info(
"Generating initial linear GDP approximation by solving subproblems "
"to cover all nonlinear disjuncts.")
disjunct_needs_cover = list(
any(constr.body.polynomial_degree() not in (0, 1)
for constr in disj.component_data_objects(
ctype=Constraint, active=True, descend_into=True)) for disj in
solve_data.working_model.GDPopt_utils.working_disjuncts_list)
iter_count = 1
while (any(disjunct_needs_cover)
and iter_count <= config.set_cover_iterlim):
solve_data.mip_iteration += 1
linear_GDP = solve_data.linear_GDP.clone()
linear_GDP.GDPopt_utils.no_backtracking.activate()
# Solve set covering MIP
mip_results = solve_set_cover_MIP(linear_GDP, disjunct_needs_cover,
solve_data, config)
if not mip_results:
# problem is infeasible. break
return False
# solve local NLP
_, mip_var_values, mip_disjunct_values = mip_results
nlp_model = solve_data.working_model.clone()
copy_and_fix_mip_values_to_nlp(nlp_model.GDPopt_utils.working_var_list,
mip_var_values, config)
TransformationFactory('gdp.fix_disjuncts').apply_to(nlp_model)
solve_data.nlp_iteration += 1
nlp_result = solve_NLP(nlp_model, solve_data, config)
nlp_feasible, nlp_var_values, nlp_duals = nlp_result
if nlp_feasible:
# if successful, updated sets
active_disjuncts = list(
fabs(val - 1) <= config.integer_tolerance
for val in mip_disjunct_values)
disjunct_needs_cover = list((needed_cover and not was_active) for (
needed_cover,
was_active) in zip(disjunct_needs_cover, active_disjuncts))
update_nlp_progress_indicators(nlp_model, solve_data, config)
add_outer_approximation_cuts(nlp_var_values, nlp_duals, solve_data,
config)
add_integer_cut(mip_var_values,
solve_data,
config,
feasible=nlp_feasible)
iter_count += 1
if any(disjunct_needs_cover):
# Iteration limit was hit without a full covering of all nonlinear
# disjuncts
config.logger.warning(
'Iteration limit reached for set covering initialization '
'without covering all disjuncts.')
return False
return True