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 apply(self, **kwds):
instance = kwds.pop('instance')
# Not sure why the ModifyInstance callback started passing the
# model along with the instance. We will ignore it.
model = kwds.pop('model', None)
xform = TransformationFactory('gdp.bigm')
return xform.apply_to(instance, **kwds)
def apply(self, **kwds):
instance = kwds.pop('instance')
# Not sure why the ModifyInstance callback started passing the
# model along with the instance. We will ignore it.
model = kwds.pop('model', None)
xform = TransformationFactory('gdp.chull')
return xform.apply_to(instance, **kwds)
def _apply_solver(self):
start_time = time.time()
instance = self._instance
xfrm = TransformationFactory('romodel.nominal')
xfrm.apply_to(instance)
if not self.options.solver:
# Use glpk instead
solver = 'gurobi'
else:
solver = self.options.solver
with pyomo.opt.SolverFactory(solver) as opt:
self.results = []
results = opt.solve(instance,
tee=self._tee,
timelimit=self._timelimit)
self.results.append(results)
stop_time = time.time()
self.wall_time = stop_time - start_time
self.results_obj = self._setup_results_obj()
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt, '_rc', None),
log=getattr(opt, '_log', None))
def _apply_solver(self):
start_time = time.time()
# construct the high-point problem (LL feasible, no LL objective)
# s0 <- solve the high-point
# if s0 infeasible then return high_point_infeasible
xfrm = TransformationFactory('pao.bilevel.highpoint')
xfrm.apply_to(self._instance)
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver:
solver = 'ipopt'
for c in self._instance.component_objects(Block, descend_into=False):
if '_hp' in c.name:
c.activate()
opt = pyomo.opt.SolverFactory(solver)
results = opt.solve(c)
_check_termination_condition(results)
c.deactivate()
# s1 <- solve the optimistic bilevel (linear/linear) problem (call solver3)
# if s1 infeasible then return optimistic_infeasible
opt = BilevelSolver3()
opt.options.solver = solver
results = opt.solve(self._instance)
_check_termination_condition(results)
def test_reformulation(self, name, model, reformulation):
""" Tests bilevel reformulation and checks whether the derivation is equivalent
to the known solution in the reformulation/*.out file
Parameters
----------
name : `string`
model: `string`
reformulation: `string`
"""
from importlib.machinery import SourceFileLoader
namespace = SourceFileLoader(name, model).load_module()
instance = namespace.pyomo_create_model()
xfrm = TransformationFactory('pao.duality.linear_dual')
for submodel in instance.component_objects(SubModel,
descend_into=True):
instance.reclassify_component_type(submodel, Block)
dualmodel = xfrm._create_using(instance, block=submodel.name)
break
with open(join(aux_dir, name + '_linear_mpec.out'), 'w') as ofile:
dualmodel.pprint(ostream=ofile)
self.assertFileEqualsBaseline(join(aux_dir, name + '_linear_mpec.out'),
reformulation,
tolerance=1e-5)
def help_transformations():
import pyomo.environ
from pyomo.core import TransformationFactory
wrapper = textwrap.TextWrapper()
wrapper.initial_indent = ' '
wrapper.subsequent_indent = ' '
print("")
print("Pyomo Model Transformations")
print("---------------------------")
for xform in sorted(TransformationFactory):
print(" " + xform)
_doc = TransformationFactory.doc(xform) or ""
# Ideally, the Factory would ensure that the doc string
# indicated deprecation, but as @deprecated() is Pyomo
# functionality and the Factory comes directly from PyUtilib,
# PyUtilib probably shouldn't contain Pyomo-specific processing.
# The next best thing is to ensure that the deprecation status
# is indicated here.
_init_doc = TransformationFactory.get_class(xform).__init__.__doc__ \
or ""
if _init_doc.strip().startswith(
'DEPRECATED') and 'DEPRECAT' not in _doc:
_doc = ' '.join(('[DEPRECATED]', _doc))
if _doc:
print(wrapper.fill(_doc))
def _apply_solver(self):
start_time = time.time()
instance = self._instance
transformations = [
'romodel.ellipsoidal', 'romodel.polyhedral', 'romodel.unknown'
]
for transform in transformations:
xfrm = TransformationFactory(transform)
xfrm.apply_to(instance)
if not self.options.solver:
solver = 'gurobi'
else:
solver = self.options.solver
with pyomo.opt.SolverFactory(solver) as opt:
self.results = []
opt.options = self.options
results = opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit)
self.results.append(results)
stop_time = time.time()
self.wall_time = stop_time - start_time
self.results_obj = self._setup_results_obj()
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt, '_rc', None),
log=getattr(opt, '_log', None))
def _test(self, tname, M):
ofile = currdir + tname + '_nlxfrm.out'
bfile = currdir + tname + '_nlxfrm.nl'
xfrm = TransformationFactory('mpec.nl')
xfrm.apply_to(M)
M.write(ofile, format=ProblemFormat.nl)
if not os.path.exists(bfile):
os.rename(ofile, bfile)
self.assertFileEqualsBaseline(ofile, bfile)
def _apply_solver(self):
start_time = time.time()
#
# Transform instance
#
xfrm = TransformationFactory('mpec.simple_nonlinear')
xfrm.apply_to(self._instance)
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver: #pragma:nocover
self.options.solver = solver = 'ipopt'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
self.results = []
epsilon_final = self.options.get('epsilon_final', 1e-7)
epsilon = self.options.get('epsilon_initial', epsilon_final)
while (True):
self._instance.mpec_bound.value = epsilon
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
res = opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit)
self.results.append(res)
epsilon /= 10.0
if epsilon < epsilon_final:
break
#
# Reclassify the Complementarity components
#
from pyomo.mpec import Complementarity
for cuid in self._instance._transformation_data[
'mpec.simple_nonlinear'].compl_cuids:
cobj = cuid.find_component(self._instance)
cobj.parent_block().reclassify_component_type(
cobj, Complementarity)
#
# Update timing
#
stop_time = time.time()
self.wall_time = stop_time - start_time
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt, '_rc', None),
log=getattr(opt, '_log', None))
def _test(self, tname, M):
ofile = currdir + tname + '_nlxfrm.out'
bfile = currdir + tname + '_nlxfrm.nl'
xfrm = TransformationFactory('mpec.nl')
xfrm.apply_to(M)
M.write(ofile, format=ProblemFormat.nl)
if not os.path.exists(bfile):
os.rename(ofile, bfile)
self.assertTrue(cmp(ofile, bfile),
msg="Files %s and %s differ" % (ofile, bfile))
def _apply_solver(self):
start_time = time.time()
#
# Transform instance
#
xfrm = TransformationFactory('mpec.simple_nonlinear')
xfrm.apply_to(self._instance)
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver: #pragma:nocover
self.options.solver = solver = 'ipopt'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
self.results = []
epsilon_final = self.options.get('epsilon_final', 1e-7)
epsilon = self.options.get('epsilon_initial', epsilon_final)
while (True):
self._instance.mpec_bound.value = epsilon
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
res = opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit)
self.results.append(res)
epsilon /= 10.0
if epsilon < epsilon_final:
break
#
# Reclassify the Complementarity components
#
from pyomo.mpec import Complementarity
for cuid in self._instance._transformation_data['mpec.simple_nonlinear'].compl_cuids:
cobj = cuid.find_component(self._instance)
cobj.parent_block().reclassify_component_type(cobj, Complementarity)
#
# Update timing
#
stop_time = time.time()
self.wall_time = stop_time - start_time
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt,'_rc', None),
log=getattr(opt,'_log',None))
def _test(self, tname, M):
ofile = currdir + tname + '_%s.out' % str(self.xfrm)
bfile = currdir + tname + '_%s.txt' % str(self.xfrm)
if self.xfrm is not None:
xfrm = TransformationFactory(self.xfrm)
xfrm.apply_to(M)
with capture_output(ofile):
self._print(M)
if not os.path.exists(bfile):
os.rename(ofile, bfile)
self.assertFileEqualsBaseline(ofile, bfile)
def help_transformations():
import pyomo.environ
from pyomo.core import TransformationFactory
wrapper = textwrap.TextWrapper()
wrapper.initial_indent = ' '
wrapper.subsequent_indent = ' '
print("")
print("Pyomo Model Transformations")
print("---------------------------")
for xform in sorted(TransformationFactory.services()):
print(" " + xform)
print(wrapper.fill(TransformationFactory.doc(xform)))
def test_active_parent_disjunct_target(self):
m = ConcreteModel()
m.d1 = Disjunct()
m.d1.sub1 = Disjunct()
m.d1.sub2 = Disjunct()
m.d1.disj = Disjunction(expr=[m.d1.sub1, m.d1.sub2])
TransformationFactory('gdp.bigm').apply_to(m, targets=m.d1.disj)
m.d1.indicator_var.fix(1)
TransformationFactory('gdp.reclassify').apply_to(m)
self.assertIs(m.d1.type(), Block)
self.assertIs(m.d1.sub1.type(), Block)
self.assertIs(m.d1.sub2.type(), Block)
def help_transformations():
import pyomo.environ
from pyomo.core import TransformationFactory
wrapper = textwrap.TextWrapper()
wrapper.initial_indent = ' '
wrapper.subsequent_indent = ' '
print("")
print("Pyomo Model Transformations")
print("---------------------------")
for xform in sorted(TransformationFactory.services()):
print(" "+xform)
print(wrapper.fill(TransformationFactory.doc(xform)))
def initialize_model(m,nfe):
u_profile = {0:-0.06}
m.u_input = Suffix(direction=Suffix.LOCAL)
m.u_input[m.u]=u_profile
sim = Simulator(m,package='scipy')
tsim, profiles = sim.simulate(numpoints=100, varying_inputs=m.u_input)
discretizer = TransformationFactory('dae.collocation')
discretizer.apply_to(m, nfe=nfe, ncp=1, scheme='LAGRANGE-RADAU')
sim.initialize_model()
def initialize_model(m, nfe):
u_profile = {0: -0.06}
m.u_input = Suffix(direction=Suffix.LOCAL)
m.u_input[m.u] = u_profile
sim = Simulator(m, package='scipy')
tsim, profiles = sim.simulate(numpoints=100, varying_inputs=m.u_input)
discretizer = TransformationFactory('dae.collocation')
discretizer.apply_to(m, nfe=nfe, ncp=1, scheme='LAGRANGE-RADAU')
sim.initialize_model()
def _apply_solver(self):
start_time = time.time()
#
# Transform the instance
#
xfrm = TransformationFactory('bilevel.linear_mpec')
xfrm.apply_to(self._instance)
xfrm = TransformationFactory('mpec.simple_nonlinear')
xfrm.apply_to(self._instance,
mpec_bound=self.options.get('mpec_bound', 1e-7))
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver:
solver = 'glpk'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
#
self.results = []
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
self.results.append(
opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit))
#
# Load the result back into the original model
#
##self._instance.load(self.results[0], ignore_invalid_labels=True)
#
stop_time = time.time()
self.wall_time = stop_time - start_time
#
# Deactivate the block that contains the optimality conditions,
# and reactivate SubModel
#
submodel = self._instance._transformation_data[
'bilevel.linear_mpec'].submodel_cuid.find_component(self._instance)
for (name, data) in submodel.component_map(active=False).items():
if not isinstance(data, Var) and not isinstance(data, Set):
data.activate()
# TODO: delete this subblock
self._instance._transformation_data[
'bilevel.linear_mpec'].block_cuid.find_component(
self._instance).deactivate()
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt, '_rc', None),
log=getattr(opt, '_log', None))
def initialize_model(m, n_sim, n_nfe, n_ncp):
vp_profile = {0: 0.75}
vt_profile = {0: 0.75}
m.u_input = Suffix(direction=Suffix.LOCAL)
m.u_input[m.vp] = vp_profile
m.u_input[m.vt] = vt_profile
sim = Simulator(m, package='scipy')
tsim, profiles = sim.simulate(numpoints=n_sim, varying_inputs=m.u_input)
discretizer = TransformationFactory('dae.collocation')
discretizer.apply_to(m, nfe=n_nfe, ncp=n_ncp, scheme='LAGRANGE-RADAU')
sim.initialize_model()
def solve_NLP_subproblem(solve_data, config):
""" Solves fixed NLP with fixed working model binaries
Sets up local working model `fix_nlp`
Fixes binaries
Sets continuous variables to initial var values
Precomputes dual values
Deactivates trivial constraints
Solves NLP model
Returns the fixed-NLP model and the solver results
"""
fix_nlp = solve_data.working_model.clone()
MindtPy = fix_nlp.MindtPy_utils
main_objective = next(
fix_nlp.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
TransformationFactory('core.fix_discrete').apply_to(fix_nlp)
# 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()
fix_nlp.tmp_duals = ComponentMap()
for c in fix_nlp.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
fix_nlp.tmp_duals[c] = sign_adjust * max(
0, sign_adjust * (rhs - value(c.body)))
# TODO check sign_adjust
TransformationFactory('contrib.deactivate_trivial_constraints')\
.apply_to(fix_nlp, tmp=True, ignore_infeasible=True)
# Solve the NLP
with SuppressInfeasibleWarning():
results = SolverFactory(config.nlp_solver).solve(
fix_nlp, **config.nlp_solver_args)
return fix_nlp, results
def add_affine_cuts(nlp_result, solve_data, config):
with time_code(solve_data.timing, "affine cut generation"):
m = solve_data.linear_GDP
if config.calc_disjunctive_bounds:
with time_code(solve_data.timing, "disjunctive variable bounding"):
TransformationFactory(
'contrib.compute_disj_var_bounds').apply_to(
m,
solver=config.mip_solver
if config.obbt_disjunctive_bounds else None)
config.logger.info("Adding affine cuts.")
GDPopt = m.GDPopt_utils
counter = 0
for var, val in zip(GDPopt.variable_list, nlp_result.var_values):
if val is not None and not var.fixed:
var.value = val
for constr in constraints_in_True_disjuncts(m, config):
# Note: this includes constraints that are deactivated in the current model (linear_GDP)
disjunctive_var_bounds = disjunctive_bounds(constr.parent_block())
if constr.body.polynomial_degree() in (1, 0):
continue
vars_in_constr = list(identify_variables(constr.body))
if any(var.value is None for var in vars_in_constr):
continue # a variable has no values
# mcpp stuff
mc_eqn = mc(constr.body, disjunctive_var_bounds)
# mc_eqn = mc(constr.body)
ccSlope = mc_eqn.subcc()
cvSlope = mc_eqn.subcv()
ccStart = mc_eqn.concave()
cvStart = mc_eqn.convex()
ub_int = min(
constr.upper,
mc_eqn.upper()) if constr.has_ub() else mc_eqn.upper()
lb_int = max(
constr.lower,
mc_eqn.lower()) if constr.has_lb() else mc_eqn.lower()
parent_block = constr.parent_block()
# Create a block on which to put outer approximation cuts.
aff_utils = parent_block.component('GDPopt_aff')
if aff_utils is None:
aff_utils = parent_block.GDPopt_aff = Block(
doc="Block holding affine constraints")
aff_utils.GDPopt_aff_cons = ConstraintList()
aff_cuts = aff_utils.GDPopt_aff_cons
concave_cut = sum(ccSlope[var] * (var - var.value)
for var in vars_in_constr) + ccStart >= lb_int
convex_cut = sum(cvSlope[var] * (var - var.value)
for var in vars_in_constr) + cvStart <= ub_int
aff_cuts.add(expr=concave_cut)
aff_cuts.add(expr=convex_cut)
counter += 2
config.logger.info("Added %s affine cuts" % counter)
def _presolve(self, *args, **kwds):
if (not isinstance(args[0], six.string_types)) and \
(not isinstance(args[0], IBlock)):
self._instance = args[0]
xfrm = TransformationFactory('mpec.nl')
xfrm.apply_to(self._instance)
if len(self._instance._transformation_data['mpec.nl'].compl_cuids) == 0:
# There were no complementarity conditions
# so we don't hold onto the instance
self._instance = None
else:
args = (self._instance,)
else:
self._instance = None
#
SystemCallSolver._presolve(self, *args, **kwds)
def _apply_to(self, instance, bigM=None, **kwds):
log_level = logger.getEffectiveLevel()
try:
assert not NAME_BUFFER
self._config = self.CONFIG(kwds.pop('options', {}))
self._config.set_value(kwds)
if self._config.verbose and log_level > logging.INFO:
logger.setLevel(logging.INFO)
self.verbose = True
elif log_level <= logging.INFO:
self.verbose = True
else:
self.verbose = False
(instance_rBigM, cuts_obj, instance_rHull, var_info,
transBlockName) = self._setup_subproblems( instance, bigM,
self._config.\
tighten_relaxation)
self._generate_cuttingplanes( instance_rBigM, cuts_obj,
instance_rHull, var_info,
transBlockName)
# restore integrality
TransformationFactory('core.relax_integer_vars').apply_to(instance,
undo=True)
finally:
del self._config
del self.verbose
# clear the global name buffer
NAME_BUFFER.clear()
# restore logging level
logger.setLevel(log_level)
def solve_master_feasibility_problem(model_data, config):
"""
Solve a slack variable based feasibility model for the master problem
"""
model = model_data.master_model.clone()
for o in model.component_data_objects(Objective):
o.deactivate()
TransformationFactory("core.add_slack_variables").apply_to(model)
solver = config.global_solver
if not solver.available():
raise RuntimeError("NLP solver %s is not available." % config.solver)
try:
results = solver.solve(model, tee=config.tee)
except ValueError as err:
if 'Cannot load a SolverResults object with bad status: error' in str(
err):
results.solver.termination_condition = tc.error
results.solver.message = str(err)
else:
raise
if check_optimal_termination(results) and value(
model._core_add_slack_variables._slack_objective) <= 0:
# If this led to a feasible solution, continue with this model
# Load solution into master
for v in model.component_data_objects(Var):
master_v = model_data.master_model.find_component(v)
if master_v is not None:
master_v.set_value(v.value, skip_validation=True)
return results
def init_rNLP(solve_data, config):
"""Initialize by solving the rNLP (relaxed binary variables)."""
solve_data.nlp_iter += 1
m = solve_data.working_model.clone()
config.logger.info("NLP %s: Solve relaxed integrality" %
(solve_data.nlp_iter, ))
MindtPy = m.MindtPy_utils
TransformationFactory('core.relax_integrality').apply_to(m)
with SuppressInfeasibleWarning():
results = SolverFactory(config.nlp_solver).solve(
m, **config.nlp_solver_args)
subprob_terminate_cond = results.solver.termination_condition
if subprob_terminate_cond is tc.optimal:
main_objective = next(m.component_data_objects(Objective, active=True))
nlp_solution_values = list(v.value for v in MindtPy.variable_list)
dual_values = list(m.dual[c] for c in MindtPy.constraint_list)
# Add OA cut
if main_objective.sense == minimize:
solve_data.LB = value(main_objective.expr)
else:
solve_data.UB = value(main_objective.expr)
config.logger.info('NLP %s: OBJ: %s LB: %s UB: %s' %
(solve_data.nlp_iter, value(main_objective.expr),
solve_data.LB, solve_data.UB))
if config.strategy == 'OA':
add_oa_cut(nlp_solution_values, dual_values, solve_data, config)
elif subprob_terminate_cond is tc.infeasible:
# TODO fail? try something else?
config.logger.info('Initial relaxed NLP problem is infeasible. '
'Problem may be infeasible.')
else:
raise ValueError(
'MindtPy unable to handle relaxed NLP termination condition '
'of %s. Solver message: %s' %
(subprob_terminate_cond, results.solver.message))
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
config.logger.info(
"Generating initial linear GDP approximation by "
"solving subproblem with original user-specified disjunct values.")
linear_GDP = solve_data.linear_GDP.clone()
TransformationFactory('gdp.fix_disjuncts').apply_to(linear_GDP)
mip_result = solve_data.mip_solve_function(linear_GDP, solve_data, config)
if mip_result.feasible:
nlp_result = solve_data.nlp_solve_function(mip_result.var_values,
solve_data, config)
if nlp_result.feasible:
solve_data.cut_generation_function(nlp_result, solve_data, config)
solve_data.integer_cut_function(mip_result.var_values,
solve_data,
config,
feasible=nlp_result.feasible)
else:
config.logger.error('Linear GDP infeasible for initial user-specified '
'disjunct values. '
'Skipping initialization.')
def solve_local_NLP(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
for var, val in zip(nlp_model.GDPopt_utils.variable_list, mip_var_values):
if val is None:
continue
if var.is_continuous():
var.value = val
elif ((fabs(val) > config.integer_tolerance
and fabs(val - 1) > config.integer_tolerance)):
raise ValueError("Binary variable %s value %s is not "
"within tolerance %s of 0 or 1." %
(var.name, var.value, config.integer_tolerance))
else:
# variable is binary and within tolerances
if config.round_discrete_vars:
var.fix(int(round(val)))
else:
var.fix(val)
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_subproblem_progress_indicators(nlp_model, solve_data, config)
return nlp_result
def subproblem_solve(gdp, config):
subproblem = gdp.clone()
TransformationFactory('gdp.bigm').apply_to(subproblem)
main_obj = next(
subproblem.component_data_objects(Objective, active=True))
obj_sign = 1 if main_obj.sense == minimize else -1
try:
result = SolverFactory(config.solver).solve(
subproblem, **config.solver_args)
except RuntimeError as e:
config.logger.warning(
"Solver encountered RuntimeError. Treating as infeasible. "
"Msg: %s\n%s" % (str(e), traceback.format_exc()))
var_values = [
v.value for v in subproblem.GDPbb_utils.variable_list
]
return obj_sign * float('inf'), SolverResults(), var_values
var_values = [v.value for v in subproblem.GDPbb_utils.variable_list]
term_cond = result.solver.termination_condition
if result.solver.status is SolverStatus.ok and any(
term_cond == valid_cond
for valid_cond in (tc.optimal, tc.locallyOptimal,
tc.feasible)):
return value(main_obj.expr), result, var_values
elif term_cond == tc.unbounded:
return obj_sign * float('-inf'), result, var_values
elif term_cond == tc.infeasible:
return obj_sign * float('inf'), result, var_values
else:
config.logger.warning("Unknown termination condition of %s" %
term_cond)
return obj_sign * float('inf'), result, var_values
def solve_global_subproblem(mip_result, solve_data, config):
subprob = solve_data.working_model.clone()
solve_data.nlp_iteration += 1
# copy in the discrete variable values
for disj, val in zip(subprob.GDPopt_utils.disjunct_list,
mip_result.disjunct_values):
rounded_val = int(round(val))
if (fabs(val - rounded_val) > config.integer_tolerance
or rounded_val not in (0, 1)):
raise ValueError("Disjunct %s indicator value %s is not "
"within tolerance %s of 0 or 1." %
(disj.name, val.value, config.integer_tolerance))
else:
if config.round_discrete_vars:
disj.indicator_var.fix(rounded_val)
else:
disj.indicator_var.fix(val)
if config.force_subproblem_nlp:
# We also need to copy over the discrete variable values
for var, val in zip(subprob.GDPopt_utils.variable_list,
mip_result.var_values):
if var.is_continuous():
continue
rounded_val = int(round(val))
if fabs(val - rounded_val) > config.integer_tolerance:
raise ValueError("Discrete variable %s value %s is not "
"within tolerance %s of %s." %
(var.name, var.value,
config.integer_tolerance, rounded_val))
else:
# variable is binary and within tolerances
if config.round_discrete_vars:
var.fix(rounded_val)
else:
var.fix(val)
TransformationFactory('gdp.fix_disjuncts').apply_to(subprob)
subprob.dual.deactivate() # global solvers may not give dual info
if config.subproblem_presolve:
try:
preprocess_subproblem(subprob, config)
except InfeasibleConstraintException as e:
# FBBT found the problem to be infeasible
return get_infeasible_result_object(
subprob, "Preprocessing determined problem to be infeasible.")
unfixed_discrete_vars = detect_unfixed_discrete_vars(subprob)
if config.force_subproblem_nlp and len(unfixed_discrete_vars) > 0:
raise RuntimeError(
"Unfixed discrete variables found on the NLP subproblem.")
elif len(unfixed_discrete_vars) == 0:
subprob_result = solve_NLP(subprob, solve_data, config)
else:
subprob_result = solve_MINLP(subprob, solve_data, config)
if subprob_result.feasible: # NLP is feasible
update_subproblem_progress_indicators(subprob, solve_data, config)
return subprob_result
def _apply_solver(self):
start_time = time.time()
instance = self._instance
# Reformulate adjustable variables
if not self.options.adjustable:
adjustable = 'romodel.adjustable.ldr'
else:
adjustable = self.options.adjustable
xfrm = TransformationFactory(adjustable)
xfrm.apply_to(instance)
# Reformulate uncertain parameters
transformations = [
'romodel.ellipsoidal', 'romodel.polyhedral', 'romodel.gp',
'romodel.warpedgp', 'romodel.unknown'
]
transformation_kwargs = {
'romodel.ellipsoidal': [],
'romodel.polyhedral': [],
'romodel.gp': [],
'romodel.warpedgp': ['initialize_wolfe'],
'romodel.unknown': []
}
for transform in transformations:
xfrm = TransformationFactory(transform)
kwargs = {}
for kw in transformation_kwargs[transform]:
if self.options[kw]:
kwargs[kw] = self.options[kw]
xfrm.apply_to(instance, **kwargs)
instance.transformation_time = time.time() - start_time
if not self.options.solver:
solver = 'gurobi'
else:
solver = self.options.solver
with pyomo.opt.SolverFactory(solver) as opt:
self.results = []
opt.options = self.options
results = opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit)
self.results.append(results)
stop_time = time.time()
self.wall_time = stop_time - start_time
self.termination_condition = results.solver.termination_condition
self.results_obj = self._setup_results_obj()
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt, '_rc', None),
log=getattr(opt, '_log', None))
def _apply_solver(self):
start_time = time.time()
#
# Cache the instance
#
xfrm = TransformationFactory('bilevel.linear_mpec')
xfrm.apply_to(self._instance)
xfrm = TransformationFactory('mpec.simple_disjunction')
xfrm.apply_to(self._instance)
xfrm = TransformationFactory('gdp.bigm')
xfrm.apply_to(self._instance, bigM=self.options.get('bigM',100000))
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver:
solver = 'glpk'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
#
self.results = []
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
self.results.append(opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit))
#
stop_time = time.time()
self.wall_time = stop_time - start_time
#
# Deactivate the block that contains the optimality conditions,
# and reactivate SubModel
#
submodel = self._instance._transformation_data['bilevel.linear_mpec'].submodel_cuid. find_component(self._instance)
for (name, data) in submodel.component_map(active=False).items():
if not isinstance(data,Var) and not isinstance(data,Set):
data.activate()
# TODO: delete this subblock
self._instance._transformation_data['bilevel.linear_mpec'].block_cuid.find_component(self._instance).deactivate()
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt,'_rc', None),
log=getattr(opt,'_log',None))
def initialize_model(m,n_sim,n_nfe,n_ncp):
vp_profile = {0:0.75}
vt_profile = {0:0.75}
m.u_input = Suffix(direction=Suffix.LOCAL)
m.u_input[m.vp] = vp_profile
m.u_input[m.vt] = vt_profile
sim = Simulator(m, package='scipy')
tsim, profiles = sim.simulate(numpoints=n_sim, varying_inputs=m.u_input)
discretizer = TransformationFactory('dae.collocation')
discretizer.apply_to(m,nfe=n_nfe,ncp=n_ncp,scheme='LAGRANGE-RADAU')
sim.initialize_model()
def _apply_solver(self):
start_time = time.time()
instance = self._instance
xfrm = TransformationFactory('romodel.generators')
xfrm.apply_to(instance)
tdata = instance._transformation_data['romodel.generators']
generators = tdata.generators
# Need to set this up for main and sub solver
if not self.options.solver:
# Use glpk instead
solver = 'gurobi'
else:
solver = self.options.solver
with pyomo.opt.SolverFactory(solver) as opt:
self.results = []
feasible = {}
# Solve nominal problem
opt.options = self.options
results = opt.solve(instance,
tee=self._tee,
timelimit=self._timelimit)
# Add initial cut to check feasibility
for g in generators:
feasible[g.name] = g.add_cut()
# Keep adding cuts until feasible
while not all(feasible.values()):
results = opt.solve(instance,
tee=self._tee,
timelimit=self._timelimit)
for g in generators:
# Only add cut if uncertain constraint isnt feasible
if not feasible[g.name]:
feasible[g.name] = g.add_cut()
self.results.append(results)
stop_time = time.time()
self.wall_time = stop_time - start_time
self.results_obj = self._setup_results_obj()
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt, '_rc', None),
log=getattr(opt, '_log', None))
def _apply_solver(self):
start_time = time.time()
#
# Transform instance
#
xfrm = TransformationFactory('mpec.simple_disjunction')
xfrm.apply_to(self._instance)
xfrm = TransformationFactory('gdp.bigm')
xfrm.apply_to(self._instance, bigM=self.options.get('bigM', 10**6))
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver: #pragma:nocover
self.options.solver = solver = 'glpk'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
self.results = opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit)
#
# Reclassify the Complementarity components
#
from pyomo.mpec import Complementarity
for cuid in self._instance._transformation_data[
'mpec.simple_disjunction'].compl_cuids:
cobj = cuid.find_component(self._instance)
cobj.parent_block().reclassify_component_type(
cobj, Complementarity)
#
# Transform the result back into the original model
#
##self._instance.solutions.load_from(self.results, ignore_invalid_labels=True)
#
# Update timing
#
stop_time = time.time()
self.wall_time = stop_time - start_time
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt, '_rc', None),
log=getattr(opt, '_log', None))
def test_active_parent_block(self):
m = ConcreteModel()
m.d1 = Block()
m.d1.sub1 = Disjunct()
m.d1.sub2 = Disjunct()
m.d1.disj = Disjunction(expr=[m.d1.sub1, m.d1.sub2])
with self.assertRaises(GDP_Error):
TransformationFactory('gdp.reclassify').apply_to(m)
def _apply_solver(self):
start_time = time.time()
#
# Transform instance
#
xfrm = TransformationFactory('mpec.simple_disjunction')
xfrm.apply_to(self._instance)
xfrm = TransformationFactory('gdp.bigm')
xfrm.apply_to(self._instance, default_bigM=self.options.get('bigM',10**6))
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver: #pragma:nocover
self.options.solver = solver = 'glpk'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
self.results = opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit)
#
# Reclassify the Complementarity components
#
from pyomo.mpec import Complementarity
for cuid in self._instance._transformation_data['mpec.simple_disjunction'].compl_cuids:
cobj = cuid.find_component(self._instance)
cobj.parent_block().reclassify_component_type(cobj, Complementarity)
#
# Transform the result back into the original model
#
##self._instance.solutions.load_from(self.results, ignore_invalid_labels=True)
#
# Update timing
#
stop_time = time.time()
self.wall_time = stop_time - start_time
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt,'_rc', None),
log=getattr(opt,'_log',None))
def _apply_solver(self):
start_time = time.time()
#
# Transform the instance
#
xfrm = TransformationFactory('bilevel.linear_mpec')
xfrm.apply_to(self._instance)
xfrm = TransformationFactory('mpec.simple_nonlinear')
xfrm.apply_to(self._instance, mpec_bound=1e-7)
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver:
solver = 'glpk'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
#
self.results = []
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
self.results.append(opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit))
#
# Load the result back into the original model
#
##self._instance.load(self.results[0], ignore_invalid_labels=True)
#
stop_time = time.time()
self.wall_time = stop_time - start_time
#
# Deactivate the block that contains the optimality conditions,
# and reactivate SubModel
#
self._instance._transformation_data['bilevel.linear_mpec'].submodel_cuid.find_component(self._instance).activate()
self._instance._transformation_data['bilevel.linear_mpec'].block_cuid.find_component(self._instance).deactivate()
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt,'_rc', None), log=getattr(opt,'_log',None))
def _apply_solver(self):
start_time = time.time()
#
# Cache the instance
#
xfrm = TransformationFactory('bilevel.linear_dual')
xfrm.apply_to(self._instance)
#
# Verify whether the objective is linear
#
nonlinear=False
for odata in self._instance.component_objects(Objective, active=True):
nonlinear = odata.expr.polynomial_degree() != 1
# Stop after the first objective
break
#
# Apply an additional transformation to remap bilinear terms
#
if nonlinear:
gdp_xfrm = TransformationFactory("gdp.bilinear")
gdp_xfrm.apply_to(self._instance)
mip_xfrm = TransformationFactory("gdp.bigm")
mip_xfrm.apply_to(self._instance, default_bigM=self.options.get('bigM',100000))
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver:
solver = 'glpk'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
self.results = []
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
self.results.append(opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit))
#print("POST-SOLVE - BEGIN")
#self._instance.write("tmp.lp", io_options={"symbolic_solver_labels":True})
#self._instance.pprint()
#self._instance.display()
#print("POST-SOLVE - END")
#
# If the problem was bilinear, then reactivate the original data
#
if nonlinear:
i = 0
for v in self._instance.bilinear_data_.vlist.itervalues():
#print(v)
#print(v.cname())
#print(type(v))
#print(v.value)
if abs(v.value) <= 1e-7:
self._instance.bilinear_data_.vlist_boolean[i] = 0
else:
self._instance.bilinear_data_.vlist_boolean[i] = 1
i = i + 1
#
self._instance.bilinear_data_.deactivate()
#
# Transform the result back into the original model
#
tdata = self._instance._transformation_data['bilevel.linear_dual']
unfixed_cuids = set()
# Copy variable values and fix them
for vuid in tdata.fixed:
for index_, data_ in vuid.find_component_on(self._instance).iteritems():
if not data_.fixed:
data_.value = self._instance.find_component(data_).value
data_.fixed = True
unfixed_cuids.add(ComponentUID(data_))
# Reclassify the SubModel components and resolve
for name_ in tdata.submodel:
submodel = getattr(self._instance, name_)
submodel.activate()
for (name, data) in submodel.component_map(active=False).items():
if not isinstance(data,Var) and not isinstance(data,Set):
data.activate()
dual_submodel = getattr(self._instance, name_+'_dual')
dual_submodel.deactivate()
pyomo.util.PyomoAPIFactory('pyomo.repn.compute_canonical_repn')({}, model=submodel)
self._instance.reclassify_component_type(name_, Block)
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt_inner:
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
results = opt_inner.solve(self._instance, tee=self._tee, timelimit=self._timelimit)
#select=None)
# Unfix variables
for vuid in tdata.fixed:
for index_, data_ in vuid.find_component_on(self._instance).iteritems():
if ComponentUID(data_) in unfixed_cuids:
data_.fixed = False
#
self._instance.solutions.select(0, ignore_fixed_vars=True)
self.results.append(results)
#
stop_time = time.time()
self.wall_time = stop_time - start_time
self.results_obj = self._setup_results_obj()
#
# Reactivate top level objective
# and reclassify the submodel
#
for oname, odata in self._instance.component_map(Objective).items():
odata.activate()
# TODO: rework the Block logic to allow for searching SubModel objects for variables, etc.
#data_.parent_component().parent_block().reclassify_component_type(name_, SubModel)
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt,'_rc', None),
log=getattr(opt,'_log',None))
def _apply_solver(self):
start_time = time.time()
#
# Cache the instance
#
xfrm = TransformationFactory('bilevel.linear_dual')
xfrm.apply_to(self._instance)
#
# Apply an additional transformation to remap bilinear terms
#
if self.options.transform is None:
xfrm = None
else:
xfrm = TransformationFactory(self.options.transform)
xfrm.apply_to(self._instance)
#
# Solve with a specified solver
#
solver = self.options.solver
if not self.options.solver:
solver = 'glpk'
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt:
self.results = []
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
self.results.append(opt.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit))
#
# Transform the result back into the original model
#
tdata = self._instance._transformation_data['bilevel.linear_dual']
unfixed_cuids = set()
# Copy variable values and fix them
for vuid in tdata.fixed:
for index_, data_ in vuid.find_component_on(self._instance).iteritems():
if not data_.fixed:
data_.value = self._instance.find_component(data_).value
data_.fixed = True
unfixed_cuids.add(ComponentUID(data_))
# Reclassify the SubModel components and resolve
for name_ in tdata.submodel:
submodel = getattr(self._instance, name_)
submodel.activate()
dual_submodel = getattr(self._instance, name_+'_dual')
dual_submodel.deactivate()
pyomo.util.PyomoAPIFactory('pyomo.repn.compute_canonical_repn')({}, model=submodel)
self._instance.reclassify_component_type(name_, Block)
# use the with block here so that deactivation of the
# solver plugin always occurs thereby avoiding memory
# leaks caused by plugins!
with pyomo.opt.SolverFactory(solver) as opt_inner:
#
# **NOTE: It would be better to override _presolve on the
# base class of this solver as you might be
# missing a number of keywords that were passed
# into the solve method (e.g., none of the
# io_options are getting relayed to the subsolver
# here).
#
self.results.append(opt_inner.solve(self._instance,
tee=self._tee,
timelimit=self._timelimit,
select=None))
self._instance.solutions.select(0, ignore_fixed_vars=True)
data_.parent_component().parent_block().reclassify_component_type(name_, SubModel)
# Unfix variables
for vuid in tdata.fixed:
for index_, data_ in vuid.find_component_on(self._instance).iteritems():
if ComponentUID(data_) in unfixed_cuids:
data_.fixed = False
stop_time = time.time()
self.wall_time = stop_time - start_time
# Reactivate top level objective
for oname, odata in self._instance.component_map(Objective).items():
odata.activate()
#
# Return the sub-solver return condition value and log
#
return pyutilib.misc.Bunch(rc=getattr(opt,'_rc', None),
log=getattr(opt,'_log',None))
def _setup_subproblems(self, instance, bigM):
# create transformation block
transBlockName, transBlock = self._add_relaxation_block(
instance,
'_pyomo_gdp_cuttingplane_relaxation')
# We store a list of all vars so that we can efficiently
# generate maps among the subproblems
transBlock.all_vars = list(v for v in instance.component_data_objects(
Var,
descend_into=(Block, Disjunct),
sort=SortComponents.deterministic) if not v.is_fixed())
# we'll store all the cuts we add together
transBlock.cuts = Constraint(Any)
# get bigM and chull relaxations
bigMRelaxation = TransformationFactory('gdp.bigm')
chullRelaxation = TransformationFactory('gdp.chull')
relaxIntegrality = TransformationFactory('core.relax_integrality')
# HACK: for the current writers, we need to also apply gdp.reclassify so
# that the indicator variables stay where they are in the big M model
# (since that is what we are eventually going to solve after we add our
# cuts).
reclassify = TransformationFactory('gdp.reclassify')
#
# Generalte the CHull relaxation (used for the separation
# problem to generate cutting planes
#
instance_rCHull = chullRelaxation.create_using(instance)
# This relies on relaxIntegrality relaxing variables on deactivated
# blocks, which should be fine.
reclassify.apply_to(instance_rCHull)
relaxIntegrality.apply_to(instance_rCHull)
#
# Reformulate the instance using the BigM relaxation (this will
# be the final instance returned to the user)
#
bigMRelaxation.apply_to(instance, bigM=bigM)
reclassify.apply_to(instance)
#
# Generate the continuous relaxation of the BigM transformation
#
instance_rBigM = relaxIntegrality.create_using(instance)
#
# Add the xstar parameter for the CHull problem
#
transBlock_rCHull = instance_rCHull.component(transBlockName)
#
# this will hold the solution to rbigm each time we solve it. We
# add it to the transformation block so that we don't have to
# worry about name conflicts.
transBlock_rCHull.xstar = Param(
range(len(transBlock.all_vars)), mutable=True, default=None)
transBlock_rBigM = instance_rBigM.component(transBlockName)
#
# Generate the mapping between the variables on all the
# instances and the xstar parameter
#
var_info = tuple(
(v,
transBlock_rBigM.all_vars[i],
transBlock_rCHull.all_vars[i],
transBlock_rCHull.xstar[i])
for i,v in enumerate(transBlock.all_vars))
#
# Add the separation objective to the chull subproblem
#
self._add_separation_objective(var_info, transBlock_rCHull)
return instance_rBigM, instance_rCHull, var_info, transBlockName
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 get_dual_values(solver, model):
if id(model) not in get_dual_values.discrete_stage2_vars:
# 1st attempt to get duals: we need to see if the model has
# discrete variables (solvers won't give duals if there are
# still active discrete variables)
try:
get_dual_values.discrete_stage2_vars[id(model)] = False
return get_dual_values(solver, model)
except:
get_dual_values.discrete_stage2_vars[id(model)] = True
# Find the discrete variables to populate the list
return get_dual_values(solver, model)
duals = {}
_con = model._interscenario_plugin.fixed_variables_constraint
if get_dual_values.discrete_stage2_vars[id(model)]:
# Fix all discrete variables
xfrm = TransformationFactory('core.relax_discrete')
if PYOMO_4_0:
xfrm.apply(model, inplace=True)
else:
xfrm.apply_to(model)
# Note: preprocessing is only necessary if we are changing a
# fixed/freed variable.
if FALLBACK_ON_BRUTE_FORCE_PREPROCESS:
model.preprocess()
else:
_map = {}
preprocess_block_constraints(
model._interscenario_plugin, idMap=_map)
#SOLVE
results = solver.solve(model, warmstart=True)
ss = results.solver.status
tc = results.solver.termination_condition
#self.timeInSolver += results['Solver'][0]['Time']
if ss == SolverStatus.ok and tc in _acceptable_termination_conditions:
state = ''
elif tc in _infeasible_termination_conditions:
state = 'INFEASIBLE'
else:
state = 'NONOPTIMAL'
if state:
logger.warning(
"Resolving subproblem model with relaxed second-stage "
"discrete variables failed (%s). "
"Dual values not available." % (state,) )
else:
# Get the duals
if PYOMO_4_0:
model.load(results)
else:
model.solutions.load_from(results)
#model.dual.pprint()
for varid in model._interscenario_plugin.STAGE1VAR:
duals[varid] = model.dual[_con[varid]]
# Free the discrete second-stage variables
if PYOMO_4_0:
xfrm.apply(model, inplace=True, undo=True)
else:
xfrm.apply_to(model, undo=True)
else:
# return the duals
for varid in model._interscenario_plugin.STAGE1VAR:
duals[varid] = model.dual[_con[varid]]
return duals
def solve_separation_problem(solver, model, fallback):
xfrm = TransformationFactory('core.relax_discrete')
if PYOMO_4_0:
xfrm.apply(model, inplace=True)
else:
xfrm.apply_to(model)
_block = model._interscenario_plugin
# Switch objectives
_block.original_obj().deactivate()
_block.separation_obj.activate()
#_block.separation_variables.unfix()
_par = _block.fixed_variable_values
_sep = _block.separation_variables
allow_slack = _block.allow_slack
if allow_slack:
epsilon = _block.epsilon
for idx in _sep:
_sep[idx].setlb(None)
_sep[idx].setub(None)
else:
_sep.unfix()
# Note: preprocessing is only necessary if we are changing a
# fixed/freed variable.
if FALLBACK_ON_BRUTE_FORCE_PREPROCESS:
model.preprocess()
else:
_map = {}
preprocess_block_objectives(_block, idMap=_map)
preprocess_block_constraints(_block, idMap=_map)
#SOLVE
output_buffer = StringIO()
pyutilib.misc.setup_redirect(output_buffer)
try:
results = solver.solve(model, tee=True)
except:
logger.warning("Exception raised solving the interscenario "
"evaluation subproblem")
logger.warning("Solver log:\n%s" % output_buffer.getvalue())
raise
finally:
pyutilib.misc.reset_redirect()
ss = results.solver.status
tc = results.solver.termination_condition
#self.timeInSolver += results['Solver'][0]['Time']
if ss == SolverStatus.ok and tc in _acceptable_termination_conditions:
state = ''
if PYOMO_4_0:
model.load(results)
else:
model.solutions.load_from(results)
elif tc in _infeasible_termination_conditions:
state = 'INFEASIBLE'
ans = "!!!!"
else:
state = 'NONOPTIMAL'
ans = "????"
if state:
if fallback:
#logger.warning("Initial attempt to solve the interscenario cut "
# "separation subproblem failed with the default "
# "solver (%s)." % (state,) )
pass
else:
logger.warning("Solving the interscenario cut separation "
"subproblem failed (%s)." % (state,) )
logger.warning("Solver log:\n%s" % output_buffer.getvalue())
else:
cut = dict((vid, (value(_sep[vid]), value(_par[vid])))
for vid in _block.STAGE1VAR)
obj = value(_block.separation_obj)
ans = (math.sqrt(obj), cut)
output_buffer.close()
# Restore the objective
_block.original_obj().activate()
_block.separation_obj.deactivate()
# Turn off the separation variables
if allow_slack:
for idx in _sep:
_sep[idx].setlb(-epsilon)
_sep[idx].setub(epsilon)
else:
_sep.fix(0)
if PYOMO_4_0:
xfrm.apply(model, inplace=True, undo=True)
else:
xfrm.apply_to(model, undo=True)
if FALLBACK_ON_BRUTE_FORCE_PREPROCESS:
pass
else:
_map = {}
preprocess_block_objectives(_block, idMap=_map)
return ans
