def add_namespace_to(cls, model, time):
"""
"""
name = DynamicBase.get_namespace_name()
derived_name = cls.namespace_name
if hasattr(model, name):
# Return if namespace has already been added. Don't throw an error
# as this is expected if the user, say wants to use the same model
# for NMPC and MHE.
return
if time.model() != model.model():
raise ValueError(
'time must belong to same top-level model as model')
model.add_component(name, Block())
namespace = getattr(model, name)
derived_namespace = getattr(model, derived_name)
def get_time():
return time
namespace.get_time = get_time
derived_namespace.get_time = namespace.get_time
# Validate discretization scheme and get ncp:
namespace.ncp = dyn_config.get_ncp(time)
namespace.variables_categorized = False
def remove_namespace_from(cls, model):
"""
"""
# TODO: add remove_namespace_from to derived classes
name = DynamicBase.get_namespace_name()
if not hasattr(model, name):
raise RuntimeError(
'Trying to delete block %s that does not exist on model' %
name)
model.del_component(name, Block())
def build_Block_with_objects():
"""Build an empty Block"""
obj = Block(concrete=True)
obj.construct()
obj.x = Var()
obj.x._domain = None
obj.c = Constraint()
obj.o = Objective()
return obj
def create_subsystem_block(constraints, variables=None, include_fixed=False):
""" This function creates a block to serve as a subsystem with the
specified variables and constraints. To satisfy certain writers, other
variables that appear in the constraints must be added to the block as
well. We call these the "input vars." They may be thought of as
parameters in the subsystem, but we do not fix them here as it is not
obvious that this is desired.
Arguments
---------
constraints: List
List of Pyomo constraint data objects
variables: List
List of Pyomo var data objects
include_fixed: Bool
Indicates whether fixed variables should be attached to the block.
This is useful if they may be unfixed at some point.
Returns
-------
Block containing references to the specified constraints and variables,
as well as other variables present in the constraints
"""
if variables is None:
variables = []
block = Block(concrete=True)
block.vars = Reference(variables)
block.cons = Reference(constraints)
var_set = ComponentSet(variables)
input_vars = []
for con in constraints:
for var in identify_variables(con.expr, include_fixed=include_fixed):
if var not in var_set:
input_vars.append(var)
var_set.add(var)
block.input_vars = Reference(input_vars)
add_local_external_functions(block)
return block
def build_Block_with_objects():
"""Build an empty Block"""
obj = Block(concrete=True)
obj.construct()
obj.x = Var()
obj.x._domain = None
obj.c = Constraint()
obj.o = Objective()
return obj
def get_numeric_incidence_matrix(variables, constraints):
"""
This function gets the numeric incidence matrix (Jacobian) of Pyomo
constraints with respect to variables.
"""
# NOTE: There are several ways to get a numeric incidence matrix
# from a Pyomo model. This function implements a somewhat roundabout
# method, which is to construct a dummy Block with the necessary
# variables and constraints, then construct a PyNumero PyomoNLP
# from the block and have PyNumero evaluate the desired Jacobian
# via ASL.
comps = list(variables) + list(constraints)
_check_unindexed(comps)
M, N = len(constraints), len(variables)
_block = Block()
_block.construct()
_block.obj = Objective(expr=0)
_block.vars = Reference(variables)
_block.cons = Reference(constraints)
var_set = ComponentSet(variables)
other_vars = []
for con in constraints:
for var in identify_variables(con.body, include_fixed=False):
# Fixed vars will be ignored by the nl file write, so
# there is no point to including them here.
# A different method of assembling this matrix, e.g.
# Pyomo's automatic differentiation, could support taking
# derivatives with respect to fixed variables.
if var not in var_set:
other_vars.append(var)
var_set.add(var)
# These variables are necessary due to the nl writer's philosophy
# about what constitutes a model. Note that we take derivatives with
# respect to them even though this is not necessary. We could fix them
# here to avoid doing this extra work, but that would alter the user's
# model, which we would rather not do.
_block.other_vars = Reference(other_vars)
_nlp = PyomoNLP(_block)
return _nlp.extract_submatrix_jacobian(variables, constraints)
def build_indexed_BlockWVars():
model = build_indexed_BlockWVars.model
model.indexed_Block = Block(
model.ndx, rule=build_indexed_BlockWVars.indexed_Block_rule)
return model.indexed_Block
def _reset():
build_indexed_BlockWVars.model = Block(concrete=True)
build_indexed_BlockWVars.model.ndx = RangeSet(0, N - 1)
build_indexed_BlockWVars.indexed_Block_rule = _indexed_Block_rule
def build_Block():
"""Build a Block with a few components."""
obj = Block(concrete=True)
obj.construct()
return obj
def _reset():
build_indexed_Constraint.model = Block(concrete=True)
build_indexed_Constraint.model.ndx = RangeSet(0, N - 1)
build_indexed_Constraint.rule = _con_rule
def _reset():
build_indexed_Var.model = Block(concrete=True)
build_indexed_Var.model.ndx = RangeSet(0, N - 1)
build_indexed_Var.bounds_rule = _bounds_rule
build_indexed_Var.initialize_rule = _initialize_rule
def build_Block():
"""Build a Block with a few components."""
obj = Block(concrete=True)
obj.construct()
return obj
def build_BlockData():
"""Build a _BlockData with a few components."""
obj = _BlockData(build_BlockData.owner)
obj._component = None
return obj
build_BlockData.owner = Block()
def build_block():
b = block()
b._activate_large_storage_mode()
return b
build_small_block = block
def build_Block_with_objects():
"""Build an empty Block"""
obj = Block(concrete=True)
obj.construct()
def solve(self, model, first_stage_variables, second_stage_variables,
uncertain_params, uncertainty_set, local_solver, global_solver,
**kwds):
"""Solve the model.
Parameters
----------
model: ConcreteModel
A ``ConcreteModel`` object representing the deterministic
model, cast as a minimization problem.
first_stage_variables: List[Var]
The list of ``Var`` objects referenced in ``model``
representing the design variables.
second_stage_variables: List[Var]
The list of ``Var`` objects referenced in ``model``
representing the control variables.
uncertain_params: List[Param]
The list of ``Param`` objects referenced in ``model``
representing the uncertain parameters. MUST be ``mutable``.
Assumes entries are provided in consistent order with the
entries of 'nominal_uncertain_param_vals' input.
uncertainty_set: UncertaintySet
``UncertaintySet`` object representing the uncertainty space
that the final solutions will be robust against.
local_solver: Solver
``Solver`` object to utilize as the primary local NLP solver.
global_solver: Solver
``Solver`` object to utilize as the primary global NLP solver.
"""
# === Add the explicit arguments to the config
config = self.CONFIG(kwds.pop('options', {}))
config.first_stage_variables = first_stage_variables
config.second_stage_variables = second_stage_variables
config.uncertain_params = uncertain_params
config.uncertainty_set = uncertainty_set
config.local_solver = local_solver
config.global_solver = global_solver
dev_options = kwds.pop('dev_options', {})
config.set_value(kwds)
config.set_value(dev_options)
model = model
# === Validate kwarg inputs
validate_kwarg_inputs(model, config)
# === Validate ability of grcs RO solver to handle this model
if not model_is_valid(model):
raise AttributeError(
"This model structure is not currently handled by the ROSolver."
)
# === Define nominal point if not specified
if len(config.nominal_uncertain_param_vals) == 0:
config.nominal_uncertain_param_vals = list(
p.value for p in config.uncertain_params)
elif len(config.nominal_uncertain_param_vals) != len(
config.uncertain_params):
raise AttributeError(
"The nominal_uncertain_param_vals list must be the same length"
"as the uncertain_params list")
# === Create data containers
model_data = ROSolveResults()
model_data.timing = Bunch()
# === Set up logger for logging results
with time_code(model_data.timing, 'total', is_main_timer=True):
config.progress_logger.setLevel(logging.INFO)
# === PREAMBLE
output_logger(config=config,
preamble=True,
version=str(self.version()))
# === DISCLAIMER
output_logger(config=config, disclaimer=True)
# === A block to hold list-type data to make cloning easy
util = Block(concrete=True)
util.first_stage_variables = config.first_stage_variables
util.second_stage_variables = config.second_stage_variables
util.uncertain_params = config.uncertain_params
model_data.util_block = unique_component_name(model, 'util')
model.add_component(model_data.util_block, util)
# Note: model.component(model_data.util_block) is util
# === Validate uncertainty set happens here, requires util block for Cardinality and FactorModel sets
validate_uncertainty_set(config=config)
# === Deactivate objective on model
for o in model.component_data_objects(Objective):
o.deactivate()
# === Leads to a logger warning here for inactive obj when cloning
model_data.original_model = model
# === For keeping track of variables after cloning
cname = unique_component_name(model_data.original_model,
'tmp_var_list')
src_vars = list(
model_data.original_model.component_data_objects(Var))
setattr(model_data.original_model, cname, src_vars)
model_data.working_model = model_data.original_model.clone()
# === Add objective expressions
identify_objective_functions(model_data.working_model, config)
# === Put model in standard form
transform_to_standard_form(model_data.working_model)
# === Replace variable bounds depending on uncertain params with
# explicit inequality constraints
replace_uncertain_bounds_with_constraints(
model_data.working_model,
model_data.working_model.util.uncertain_params)
# === Add decision rule information
add_decision_rule_variables(model_data, config)
add_decision_rule_constraints(model_data, config)
# === Move bounds on control variables to explicit ineq constraints
wm_util = model_data.working_model
# === Assuming all other Var objects in the model are state variables
fsv = ComponentSet(
model_data.working_model.util.first_stage_variables)
ssv = ComponentSet(
model_data.working_model.util.second_stage_variables)
sv = ComponentSet()
model_data.working_model.util.state_vars = []
for v in model_data.working_model.component_data_objects(Var):
if v not in fsv and v not in ssv and v not in sv:
model_data.working_model.util.state_vars.append(v)
sv.add(v)
# Bounds on second stage variables and state variables are separation objectives,
# they are brought in this was as explicit constraints
for c in model_data.working_model.util.second_stage_variables:
turn_bounds_to_constraints(c, wm_util, config)
for c in model_data.working_model.util.state_vars:
turn_bounds_to_constraints(c, wm_util, config)
# === Make control_variable_bounds array
wm_util.ssv_bounds = []
for c in model_data.working_model.component_data_objects(
Constraint, descend_into=True):
if "bound_con" in c.name:
wm_util.ssv_bounds.append(c)
# === Solve and load solution into model
pyros_soln, final_iter_separation_solns = ROSolver_iterative_solve(
model_data, config)
return_soln = ROSolveResults()
if pyros_soln is not None and final_iter_separation_solns is not None:
if config.load_solution and \
(pyros_soln.pyros_termination_condition is pyrosTerminationCondition.robust_optimal or
pyros_soln.pyros_termination_condition is pyrosTerminationCondition.robust_feasible):
load_final_solution(model_data, pyros_soln.master_soln,
config)
# === Return time info
model_data.total_cpu_time = get_main_elapsed_time(
model_data.timing)
iterations = pyros_soln.total_iters + 1
# === Return config to user
return_soln.config = config
# Report the negative of the objective value if it was originally maximize, since we use the minimize form in the algorithm
if next(model.component_data_objects(
Objective)).sense == maximize:
negation = -1
else:
negation = 1
if config.objective_focus == ObjectiveType.nominal:
return_soln.final_objective_value = negation * value(
pyros_soln.master_soln.master_model.obj)
elif config.objective_focus == ObjectiveType.worst_case:
return_soln.final_objective_value = negation * value(
pyros_soln.master_soln.master_model.zeta)
return_soln.pyros_termination_condition = pyros_soln.pyros_termination_condition
return_soln.time = model_data.total_cpu_time
return_soln.iterations = iterations
# === Remove util block
model.del_component(model_data.util_block)
del pyros_soln.util_block
del pyros_soln.working_model
else:
return_soln.pyros_termination_condition = pyrosTerminationCondition.robust_infeasible
return_soln.final_objective_value = None
return_soln.time = get_main_elapsed_time(model_data.timing)
return_soln.iterations = 0
return return_soln
def __init__(self, *args, **kwds):
kwds.setdefault('ctype', ExternalGreyBoxBlock)
self._init_model = Initializer(kwds.pop('external_model', None))
Block.__init__(self, *args, **kwds)
def build_Block():
"""Build a Block with a few components."""
obj = Block(concrete=True)
obj.construct()
return obj