def _solve_impl(self,
sp,
output_solver_log=False,
keep_solver_files=False,
symbolic_solver_labels=False):
"""
Solve a stochastic program by building the extensive
form and calling and a Pyomo solver.
See the 'solve' method on the base class for
additional keyword documentation.
Args:
sp: The stochastic program to solve. Pyro based
managers are not accepted. All scenario
models must be managed locally.
output_solver_log (bool): Stream the solver
output during the solve.
keep_solver_files (bool): Retain temporary solver
input and output files after the solve completes.
symbolic_solver_labels (bool): Generate solver
input files using human-readable symbols
(makes debugging easier).
Returns: A results object with information about the solution.
"""
if isinstance(sp,
(ScenarioTreeManagerClientPyro,
ScenarioTreeManagerSolverClientPyro)):
raise TypeError("The EF solver does not handle "
"Pyro-based scenario tree managers")
orig_parents = {}
if sp.scenario_tree.contains_bundles():
for scenario in sp.scenario_tree.scenarios:
if scenario._instance._parent is not None:
orig_parents[scenario] = scenario._instance._parent
scenario._instance._parent = None
assert not scenario._instance_objective.active
scenario._instance_objective.activate()
try:
with ExtensiveFormAlgorithm(sp, self._options) as ef:
ef.build_ef()
ef.solve(
output_solver_log=output_solver_log,
keep_solver_files=keep_solver_files,
symbolic_solver_labels=symbolic_solver_labels,
check_status=False)
finally:
for scenario, parent in orig_parents.items():
scenario._instance._parent = parent
assert scenario._instance_objective.active
scenario._instance_objective.deactivate()
results = SPSolverResults()
results.objective = ef.objective
results.bound = ef.bound
results.status = ef.solution_status
results.solver.status = ef.solver_status
results.solver.termination_condition = ef.termination_condition
results.solver.message = ef.termination_message
results.solver.time = ef.time
results.solver.pyomo_time = ef.pyomo_time
results.xhat = None
if ef.solution_status is not None:
xhat = results.xhat = {}
for stage in sp.scenario_tree.stages[:-1]:
for node in stage.nodes:
node_xhat = xhat[node.name] = {}
reference_scenario = node.scenarios[0]
node_x = reference_scenario._x[node.name]
for id_ in node._variable_ids:
node_xhat[id_] = node_x[id_]
# TODO: stage costs
#for stagenum, stage in enumerate(sp.scenario_tree.stages[:-1]):
# cost_variable_name, cost_variable_index = \
# stage._cost_variable
# stage_cost_obj = \
# instance.find_component(cost_variable_name)[cost_variable_index]
# if not stage_cost_obj.is_expression_type():
# refvar = ComponentUID(stage_cost_obj,cuid_buffer=tmp).\
# find_component(reference_model)
# refvar.value = stage_cost_obj.value
# refvar.stale = stage_cost_obj.stale
return results
def _solve_impl(self,
sp,
output_solver_log=False,
verbose=False,
logfile=None,
**kwds):
"""
Solve a stochastic program with the SchurIpopt solver.
See the 'solve' method on the base class for
additional keyword documentation.
Args:
sp: The stochastic program to solve.
output_solver_log (bool): Stream the solver
output during the solve.
logfile: The name of the logfile to save the
solver output into.
verbose: Report verbose status information to
aid debugging.
**kwds: Passed to the DDSIP file writer as I/O
options (e.g., symbolic_solver_labels=True).
Returns: A results object with information about the solution.
"""
#
# Setup the SchurIpopt working directory
#
problem_list_filename = "PySP_Subproblems.txt"
working_directory = self._create_tempdir("workdir",
dir=os.getcwd())
if logfile is None:
logfile = os.path.join(working_directory,
"schuripopt.log")
self._add_tempfile("logfile", logfile)
else:
self._files["logfile"] = logfile
if verbose:
print("Schuripopt solver working directory: %s"
% (working_directory))
print("Schuripopt solver logfile: %s"
% (logfile))
#
# Launch SchurIpopt from the worker processes
# (assumed to be launched together using mpirun)
#
status = self._launch_solver(
sp,
working_directory,
logfile=logfile,
output_solver_log=output_solver_log,
verbose=verbose,
io_options=kwds)
objective = 0.0
solver_status = set()
solver_message = set()
termination_condition = set()
solution_status = set()
if status.solve_type == "bundles":
assert sp.scenario_tree.contains_bundles()
assert len(status.objective) == \
len(sp.scenario_tree.bundles)
for bundle in sp.scenario_tree.bundles:
if objective is not None:
if isinstance(status.objective[bundle.name], UndefinedData):
objective = None
else:
objective += bundle.probability * \
status.objective[bundle.name]
solver_status.add(status.solver_status[bundle.name])
solver_message.add(status.solver_message[bundle.name])
termination_condition.add(status.termination_condition[bundle.name])
if isinstance(status.solution_status[bundle.name], UndefinedData):
solution_status.add(None)
else:
solution_status.add(status.solution_status[bundle.name])
else:
assert status.solve_type == "scenarios"
assert len(status.objective) == \
len(sp.scenario_tree.scenarios)
for scenario in sp.scenario_tree.scenarios:
if objective is not None:
if isinstance(status.objective[scenario.name], UndefinedData):
objective = None
else:
objective += scenario.probability * \
status.objective[scenario.name]
solver_status.add(status.solver_status[scenario.name])
solver_message.add(status.solver_message[scenario.name])
termination_condition.add(status.termination_condition[scenario.name])
if isinstance(status.solution_status[scenario.name], UndefinedData):
solution_status.add(None)
else:
solution_status.add(status.solution_status[scenario.name])
assert len(solver_status) == 1
assert len(solver_message) == 1
assert len(termination_condition) == 1
assert len(solution_status) == 1
results = SPSolverResults()
results.objective = None
results.bound = None
results.status = solution_status.pop()
results.solver.status = solver_status.pop()
results.solver.termination_condition = termination_condition.pop()
results.solver.message = solver_message.pop()
results.solver.time = max(status.solve_time.values())
results.solver.pyomo_time = \
max(status.pyomo_solve_time.values())
results.xhat = None
if str(results.solver.status) == "ok" and \
str(results.solver.termination_condition) == "optimal":
results.objective = objective
xhat = results.xhat = {}
for stage in sp.scenario_tree.stages[:-1]:
for node in stage.nodes:
worker_name = sp.get_worker_for_scenario(
node.scenarios[0].name)
node_solution = sp.invoke_function_on_worker(
worker_name,
"EXTERNAL_collect_solution",
thisfile,
invocation_type=InvocationType.Single,
function_args=(node.name,))
node_xhat = xhat[node.name] = {}
for id_ in node_solution:
node_xhat[repr(id_)] = node_solution[id_][0]
return results
def _solve_impl(self,
sp,
output_solver_log=False,
keep_solver_files=False,
symbolic_solver_labels=False):
"""
Solve a stochastic program by building the extensive
form and calling and a Pyomo solver.
See the 'solve' method on the base class for
additional keyword documentation.
Args:
sp: The stochastic program to solve. Pyro based
managers are not accepted. All scenario
models must be managed locally.
output_solver_log (bool): Stream the solver
output during the solve.
keep_solver_files (bool): Retain temporary solver
input and output files after the solve completes.
symbolic_solver_labels (bool): Generate solver
input files using human-readable symbols
(makes debugging easier).
Returns: A results object with information about the solution.
"""
if isinstance(sp, (ScenarioTreeManagerClientPyro,
ScenarioTreeManagerSolverClientPyro)):
raise TypeError("The EF solver does not handle "
"Pyro-based scenario tree managers")
orig_parents = {}
if sp.scenario_tree.contains_bundles():
for scenario in sp.scenario_tree.scenarios:
if scenario._instance._parent is not None:
orig_parents[scenario] = scenario._instance._parent
scenario._instance._parent = None
assert not scenario._instance_objective.active
scenario._instance_objective.activate()
try:
with ExtensiveFormAlgorithm(sp, self._options) as ef:
ef.build_ef()
ef.solve(output_solver_log=output_solver_log,
keep_solver_files=keep_solver_files,
symbolic_solver_labels=symbolic_solver_labels,
check_status=False)
finally:
for scenario, parent in orig_parents.items():
scenario._instance._parent = parent
assert scenario._instance_objective.active
scenario._instance_objective.deactivate()
results = SPSolverResults()
results.objective = ef.objective
results.bound = ef.bound
results.status = ef.solution_status
results.solver.status = ef.solver_status
results.solver.termination_condition = ef.termination_condition
results.solver.message = ef.termination_message
results.solver.time = ef.time
results.solver.pyomo_time = ef.pyomo_time
results.xhat = None
if ef.solution_status is not None:
xhat = results.xhat = {}
for stage in sp.scenario_tree.stages[:-1]:
for node in stage.nodes:
node_xhat = xhat[node.name] = {}
reference_scenario = node.scenarios[0]
node_x = reference_scenario._x[node.name]
for id_ in node._variable_ids:
node_xhat[id_] = node_x[id_]
# TODO: stage costs
#for stagenum, stage in enumerate(sp.scenario_tree.stages[:-1]):
# cost_variable_name, cost_variable_index = \
# stage._cost_variable
# stage_cost_obj = \
# instance.find_component(cost_variable_name)[cost_variable_index]
# if not stage_cost_obj.is_expression_type():
# refvar = ComponentUID(stage_cost_obj,cuid_buffer=tmp).\
# find_component(reference_model)
# refvar.value = stage_cost_obj.value
# refvar.stale = stage_cost_obj.stale
return results
def _solve_impl(self,
sp,
rho=1.0,
y_init=0.0,
z_init=0.0,
output_solver_log=False):
if len(sp.scenario_tree.stages) > 2:
raise ValueError("ADMM solver does not yet handle more "
"than 2 time-stages")
start_time = time.time()
scenario_tree = sp.scenario_tree
num_scenarios = len(scenario_tree.scenarios)
num_stages = len(scenario_tree.stages)
num_na_nodes = 0
num_na_variables = 0
num_na_continuous_variables = 0
num_na_binary_variables = 0
num_na_integer_variables = 0
for stage in sp.scenario_tree.stages[:-1]:
for tree_node in stage.nodes:
num_na_nodes += 1
num_na_variables += len(tree_node._standard_variable_ids)
for id_ in tree_node._standard_variable_ids:
if tree_node.is_variable_binary(id_):
num_na_binary_variables += 1
elif tree_node.is_variable_integer(id_):
num_na_integer_variables += 1
else:
num_na_continuous_variables += 1
# print("-"*20)
# print("Problem Statistics".center(20))
# print("-"*20)
# print("Total number of scenarios.................: %10s"
# % (num_scenarios))
# print("Total number of time stages...............: %10s"
# % (num_stages))
# print("Total number of non-anticipative nodes....: %10s"
# % (num_na_nodes))
# print("Total number of non-anticipative variables: %10s\n#"
# " continuous: %10s\n#"
# " binary: %10s\n#"
# " integer: %10s"
# % (num_na_variables,
# num_na_continuous_variables,
# num_na_binary_variables,
# num_na_integer_variables))
rel_tol_primal = \
self.get_option("primal_residual_relative_tolerance")
rel_tol_dual = \
self.get_option("dual_residual_relative_tolerance")
max_iterations = \
self.get_option("max_iterations")
self.objective_history = OrderedDict()
self.primal_residual_history = OrderedDict()
self.dual_residual_history = OrderedDict()
self.iterations = 0
if output_solver_log:
print("")
label_cols = ("{0:^4} {1:>16} {2:>8} {3:>8} {4:>12}".format(
"iter", "objective", "pr_res", "du_res", "lg(||rho||)"))
with ADMMAlgorithm(sp, self._options) as admm:
rho, x, y, z = admm.initialize_algorithm_data(rho_init=rho,
y_init=y_init,
z_init=z_init)
rho_strategy = RhoStrategyFactory(self.get_option("rho_strategy"),
self._options)
rho_strategy.initialize(sp, x, y, z, rho)
for i in xrange(max_iterations):
objective = \
admm.run_x_update(x, y, z, rho)
(unscaled_primal_residual,
unscaled_dual_residual,
x_scale,
z_scale) = \
admm.run_z_update(x, y, z, rho)
y_scale = \
admm.run_y_update(x, y, z, rho)
# we've completed another iteration
self.iterations += 1
# check for convergence
primal_rel_scale = max(1.0, x_scale, z_scale)
dual_rel_scale = max(1.0, y_scale)
primal_residual = unscaled_primal_residual / \
math.sqrt(num_scenarios) / \
primal_rel_scale
dual_residual = unscaled_dual_residual / \
math.sqrt(num_na_variables) / \
dual_rel_scale
self.objective_history[i] = \
objective
self.primal_residual_history[i] = \
primal_residual
self.dual_residual_history[i] = \
dual_residual
if output_solver_log:
if (i % 10) == 0:
print(label_cols)
print("%4d %16.7e %8.2e %8.2e %12.2e" %
(i, objective, primal_residual, dual_residual,
math.log(admm.compute_nodevector_norm(rho))))
if (primal_residual < rel_tol_primal) and \
(dual_residual < rel_tol_dual):
if output_solver_log:
print("\nNumber of Iterations....: %s" %
(self.iterations))
break
else:
rho_strategy.update_rho(sp, x, y, z, rho)
else:
if output_solver_log:
print("\nMaximum number of iterations reached: %s" %
(max_iterations))
if output_solver_log:
print("")
print(" {0:^24} {1:^24}".\
format("(scaled)", "(unscaled)"))
print("Objective..........: {0:^24} {1:^24.16e}".\
format("-", objective))
print("Primal residual....: {0:^24.16e} {1:^24.16e}".\
format(primal_residual, unscaled_primal_residual))
print("Dual residual......: {0:^24.16e} {1:^24.16e}".\
format(dual_residual, unscaled_dual_residual))
unscaled_err = unscaled_primal_residual + \
unscaled_dual_residual
err = primal_residual + dual_residual
print("Overall error......: {0:^24.16e} {1:^24.16e}".\
format(err, unscaled_err))
results = SPSolverResults()
results.objective = objective
results.xhat = z
return results
def _solve_impl(self,
sp,
rho=1.0,
y_init=0.0,
z_init=0.0,
output_solver_log=False):
if len(sp.scenario_tree.stages) > 2:
raise ValueError(
"ADMM solver does not yet handle more "
"than 2 time-stages")
start_time = time.time()
scenario_tree = sp.scenario_tree
num_scenarios = len(scenario_tree.scenarios)
num_stages = len(scenario_tree.stages)
num_na_nodes = 0
num_na_variables = 0
num_na_continuous_variables = 0
num_na_binary_variables = 0
num_na_integer_variables = 0
for stage in sp.scenario_tree.stages[:-1]:
for tree_node in stage.nodes:
num_na_nodes += 1
num_na_variables += len(tree_node._standard_variable_ids)
for id_ in tree_node._standard_variable_ids:
if tree_node.is_variable_binary(id_):
num_na_binary_variables += 1
elif tree_node.is_variable_integer(id_):
num_na_integer_variables += 1
else:
num_na_continuous_variables += 1
# print("-"*20)
# print("Problem Statistics".center(20))
# print("-"*20)
# print("Total number of scenarios.................: %10s"
# % (num_scenarios))
# print("Total number of time stages...............: %10s"
# % (num_stages))
# print("Total number of non-anticipative nodes....: %10s"
# % (num_na_nodes))
# print("Total number of non-anticipative variables: %10s\n#"
# " continuous: %10s\n#"
# " binary: %10s\n#"
# " integer: %10s"
# % (num_na_variables,
# num_na_continuous_variables,
# num_na_binary_variables,
# num_na_integer_variables))
rel_tol_primal = \
self.get_option("primal_residual_relative_tolerance")
rel_tol_dual = \
self.get_option("dual_residual_relative_tolerance")
max_iterations = \
self.get_option("max_iterations")
self.objective_history = OrderedDict()
self.primal_residual_history = OrderedDict()
self.dual_residual_history = OrderedDict()
self.iterations = 0
if output_solver_log:
print("")
label_cols = ("{0:^4} {1:>16} {2:>8} {3:>8} {4:>12}".format(
"iter","objective","pr_res","du_res","lg(||rho||)"))
with ADMMAlgorithm(sp, self._options) as admm:
rho, x, y, z = admm.initialize_algorithm_data(rho_init=rho,
y_init=y_init,
z_init=z_init)
rho_strategy = RhoStrategyFactory(
self.get_option("rho_strategy"),
self._options)
rho_strategy.initialize(sp, x, y, z, rho)
for i in xrange(max_iterations):
objective = \
admm.run_x_update(x, y, z, rho)
(unscaled_primal_residual,
unscaled_dual_residual,
x_scale,
z_scale) = \
admm.run_z_update(x, y, z, rho)
y_scale = \
admm.run_y_update(x, y, z, rho)
# we've completed another iteration
self.iterations += 1
# check for convergence
primal_rel_scale = max(1.0, x_scale, z_scale)
dual_rel_scale = max(1.0, y_scale)
primal_residual = unscaled_primal_residual / \
math.sqrt(num_scenarios) / \
primal_rel_scale
dual_residual = unscaled_dual_residual / \
math.sqrt(num_na_variables) / \
dual_rel_scale
self.objective_history[i] = \
objective
self.primal_residual_history[i] = \
primal_residual
self.dual_residual_history[i] = \
dual_residual
if output_solver_log:
if (i % 10) == 0:
print(label_cols)
print("%4d %16.7e %8.2e %8.2e %12.2e"
% (i,
objective,
primal_residual,
dual_residual,
math.log(admm.compute_nodevector_norm(rho))))
if (primal_residual < rel_tol_primal) and \
(dual_residual < rel_tol_dual):
if output_solver_log:
print("\nNumber of Iterations....: %s"
% (self.iterations))
break
else:
rho_strategy.update_rho(sp, x, y, z, rho)
else:
if output_solver_log:
print("\nMaximum number of iterations reached: %s"
% (max_iterations))
if output_solver_log:
print("")
print(" {0:^24} {1:^24}".\
format("(scaled)", "(unscaled)"))
print("Objective..........: {0:^24} {1:^24.16e}".\
format("-", objective))
print("Primal residual....: {0:^24.16e} {1:^24.16e}".\
format(primal_residual, unscaled_primal_residual))
print("Dual residual......: {0:^24.16e} {1:^24.16e}".\
format(dual_residual, unscaled_dual_residual))
unscaled_err = unscaled_primal_residual + \
unscaled_dual_residual
err = primal_residual + dual_residual
print("Overall error......: {0:^24.16e} {1:^24.16e}".\
format(err, unscaled_err))
results = SPSolverResults()
results.objective = objective
results.xhat = z
return results
def _solve_impl(self,
sp,
output_solver_log=False,
verbose=False,
logfile=None,
**kwds):
"""
Solve a stochastic program with the SchurIpopt solver.
See the 'solve' method on the base class for
additional keyword documentation.
Args:
sp: The stochastic program to solve.
output_solver_log (bool): Stream the solver
output during the solve.
logfile: The name of the logfile to save the
solver output into.
verbose: Report verbose status information to
aid debugging.
**kwds: Passed to the DDSIP file writer as I/O
options (e.g., symbolic_solver_labels=True).
Returns: A results object with information about the solution.
"""
#
# Setup the SchurIpopt working directory
#
problem_list_filename = "PySP_Subproblems.txt"
working_directory = self._create_tempdir("workdir",
dir=os.getcwd())
if logfile is None:
logfile = os.path.join(working_directory,
"schuripopt.log")
self._add_tempfile("logfile", logfile)
else:
self._files["logfile"] = logfile
if verbose:
print("Schuripopt solver working directory: %s"
% (working_directory))
print("Schuripopt solver logfile: %s"
% (logfile))
#
# Launch SchurIpopt from the worker processes
# (assumed to be launched together using mpirun)
#
status = self._launch_solver(
sp,
working_directory,
logfile=logfile,
output_solver_log=output_solver_log,
verbose=verbose,
io_options=kwds)
objective = 0.0
solver_status = set()
solver_message = set()
termination_condition = set()
solution_status = set()
if status.solve_type == "bundles":
assert sp.scenario_tree.contains_bundles()
assert len(status.objective) == \
len(sp.scenario_tree.bundles)
for bundle in sp.scenario_tree.bundles:
if objective is not None:
if isinstance(status.objective[bundle.name], UndefinedData):
objective = None
else:
objective += bundle.probability * \
status.objective[bundle.name]
solver_status.add(status.solver_status[bundle.name])
solver_message.add(status.solver_message[bundle.name])
termination_condition.add(status.termination_condition[bundle.name])
if isinstance(status.solution_status[bundle.name], UndefinedData):
solution_status.add(None)
else:
solution_status.add(status.solution_status[bundle.name])
else:
assert status.solve_type == "scenarios"
assert len(status.objective) == \
len(sp.scenario_tree.scenarios)
for scenario in sp.scenario_tree.scenarios:
if objective is not None:
if isinstance(status.objective[scenario.name], UndefinedData):
objective = None
else:
objective += scenario.probability * \
status.objective[scenario.name]
solver_status.add(status.solver_status[scenario.name])
solver_message.add(status.solver_message[scenario.name])
termination_condition.add(status.termination_condition[scenario.name])
if isinstance(status.solution_status[scenario.name], UndefinedData):
solution_status.add(None)
else:
solution_status.add(status.solution_status[scenario.name])
assert len(solver_status) == 1
assert len(solver_message) == 1
assert len(termination_condition) == 1
assert len(solution_status) == 1
results = SPSolverResults()
results.objective = None
results.bound = None
results.status = solution_status.pop()
results.solver.status = solver_status.pop()
results.solver.termination_condition = termination_condition.pop()
results.solver.message = solver_message.pop()
results.solver.time = max(status.solve_time.values())
results.solver.pyomo_time = \
max(status.pyomo_solve_time.values())
results.xhat = None
if str(results.solver.status) == "ok" and \
str(results.solver.termination_condition) == "optimal":
results.objective = objective
xhat = results.xhat = {}
for stage in sp.scenario_tree.stages[:-1]:
for node in stage.nodes:
worker_name = sp.get_worker_for_scenario(
node.scenarios[0].name)
node_solution = sp.invoke_function_on_worker(
worker_name,
"EXTERNAL_collect_solution",
thisfile,
invocation_type=InvocationType.Single,
function_args=(node.name,))
node_xhat = xhat[node.name] = {}
for id_ in node_solution:
node_xhat[repr(id_)] = node_solution[id_][0]
return results
