def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_common_option(options, "extension_precedence")
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_common_option(options, "input_name")
safe_declare_common_option(options, "load_stages")
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_common_option(options,
"verbose",
ap_group=_admm_group_label)
ScenarioTreeManagerSolverFactory.register_options(
options, options_prefix="subproblem_", setup_argparse=False)
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_common_option(options, "preprocess_fixed_variables")
safe_declare_common_option(options, "symbolic_solver_labels")
safe_declare_common_option(options, "output_times")
safe_declare_common_option(options, "verbose")
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_common_option(options,
"max_iterations",
ap_group=_admm_group_label)
safe_declare_unique_option(
options,
"primal_residual_relative_tolerance",
PySPConfigValue(
1.0e-4,
domain=_domain_positive,
description=(
"Relative primal-residual tolerance. Default is 1e-4."),
doc=None,
visibility=0),
ap_group=_admm_group_label)
safe_declare_unique_option(
options,
"dual_residual_relative_tolerance",
PySPConfigValue(
1.0e-4,
domain=_domain_positive,
description=(
"Relative dual-residual tolerance. Default is 1e-4."),
doc=None,
visibility=0),
ap_group=_admm_group_label)
ADMMAlgorithm._declare_options(options)
for rstype in RhoStrategyFactory.registered_types.values():
rstype._declare_options(options)
assert 'adaptive' in RhoStrategyFactory.registered_types
safe_declare_unique_option(
options,
"rho_strategy",
PySPConfigValue(
'adaptive',
domain=_domain_must_be_str,
description=
("Rho update strategy. Choices are: %s. Default is 'adaptive'."
% (str(sorted(RhoStrategyFactory.registered_types.keys())))),
doc=None,
visibility=0),
ap_group=_admm_group_label)
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
#
# scenario instance construction
#
safe_declare_common_option(options,
"objective_sense_stage_based")
safe_declare_common_option(options,
"output_instance_construction_time")
safe_declare_common_option(options,
"compile_scenario_instances")
#
# various
#
safe_declare_common_option(options,
"verbose")
safe_declare_common_option(options,
"profile_memory")
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_unique_option(
options,
"firststage_suffix",
PySPConfigValue(
"__DDSIP_FIRSTSTAGE",
domain=_domain_must_be_str,
description=("The suffix used to identity first-stage "
"variables to DDSIP. Default is "
"'__DDSIP_FIRSTSTAGE'"),
doc=None,
visibility=0),
ap_group=_ddsip_group_label)
safe_declare_unique_option(
options,
"config_file",
PySPConfigValue(
None,
domain=_domain_must_be_str,
description=(
"The name of a partial DDSIP configuration file "
"that contains option specifications unrelated to "
"the problem structure. If specified, the contents "
"of this file will be appended to the "
"configuration created by this solver interface. "
"Default is None."),
doc=None,
visibility=0),
ap_group=_ddsip_group_label)
safe_declare_common_option(options,
"verbose",
ap_group=_ddsip_group_label)
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
# options for controlling the solver manager
# (not the scenario tree manager)
safe_declare_common_option(options,
"solver_manager_pyro_host")
safe_declare_common_option(options,
"solver_manager_pyro_port")
safe_declare_common_option(options,
"solver_manager_pyro_shutdown")
ScenarioTreePreprocessor._declare_options(options)
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_unique_option(
options,
"cvar_weight",
PySPConfigValue(
1.0,
domain=_domain_nonnegative,
description=(
"The weight associated with the CVaR term in "
"the risk-weighted objective "
"formulation. If the weight is 0, then "
"*only* a non-weighted CVaR cost will appear "
"in the EF objective - the expected cost "
"component will be dropped. Default is 1.0."
),
doc=None,
visibility=0),
ap_group=_ef_group_label)
safe_declare_unique_option(
options,
"generate_weighted_cvar",
PySPConfigValue(
False,
domain=bool,
description=(
"Add a weighted CVaR term to the "
"primary objective. Default is False."
),
doc=None,
visibility=0),
ap_group=_ef_group_label)
safe_declare_unique_option(
options,
"risk_alpha",
PySPConfigValue(
0.95,
domain=_domain_unit_interval,
description=(
"The probability threshold associated with "
"CVaR (or any future) risk-oriented "
"performance metrics. Default is 0.95."
),
doc=None,
visibility=0),
ap_group=_ef_group_label)
safe_declare_unique_option(
options,
"cc_alpha",
PySPConfigValue(
0.0,
domain=_domain_unit_interval,
description=(
"The probability threshold associated with a "
"chance constraint. The RHS will be one "
"minus this value. Default is 0."
),
doc=None,
visibility=0),
ap_group=_ef_group_label)
safe_declare_unique_option(
options,
"cc_indicator_var",
PySPConfigValue(
None,
domain=_domain_must_be_str,
description=(
"The name of the binary variable to be used "
"to construct a chance constraint. Default "
"is None, which indicates no chance "
"constraint."
),
doc=None,
visibility=0),
ap_group=_ef_group_label)
safe_declare_unique_option(
options,
"mipgap",
PySPConfigValue(
None,
domain=_domain_unit_interval,
description=(
"Specifies the mipgap for the EF solve."
),
doc=None,
visibility=0),
ap_group=_ef_group_label)
safe_declare_common_option(options,
"solver")
safe_declare_common_option(options,
"solver_io")
safe_declare_common_option(options,
"solver_manager")
safe_declare_common_option(options,
"solver_options")
safe_declare_common_option(options,
"disable_warmstart")
safe_declare_common_option(options,
"pyro_host")
safe_declare_common_option(options,
"pyro_port")
safe_declare_common_option(options,
"pyro_shutdown")
safe_declare_common_option(options,
"verbose",
ap_group=_ef_group_label)
safe_declare_common_option(options,
"output_times",
ap_group=_ef_group_label)
safe_declare_common_option(options,
"output_solver_results",
ap_group=_ef_group_label)
safe_declare_common_option(options,
"symbolic_solver_labels",
ap_group=_ef_group_label)
safe_declare_common_option(options,
"output_solver_log",
ap_group=_ef_group_label)
safe_declare_common_option(options,
"verbose",
ap_group=_ef_group_label)
safe_declare_common_option(options,
"output_times",
ap_group=_ef_group_label)
safe_declare_common_option(options,
"keep_solver_files",
ap_group=_ef_group_label)
safe_declare_common_option(options,
"output_solver_results",
ap_group=_ef_group_label)
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_unique_option(
options,
"stopping_rule_tolerance",
PySPConfigValue(
"nominal",
domain=_domain_sd_tolerance,
description=("Stopping rule tolerance used by the SD solver. "
"Must be one of: %s. Default is 'nominal'." %
(str(_domain_sd_tolerance._values))),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"single_replication",
PySPConfigValue(
False,
domain=bool,
description=("Disables multiple replication procedure in "
"SD and uses a single replication."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"print_cycle",
PySPConfigValue(
100,
domain=_domain_positive_integer,
description=("Number of iterations between output of "
"solution data to screen and file."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"eval_run_flag",
PySPConfigValue(
False,
domain=bool,
description=(
"Set to evaluate on the run. This should be "
"only used for instances with relatively complete "
"recourse. This flag is not recommended because "
"accurate function evaluations are unnecessarily "
"time consuming. It is best to use a large print "
"cycle when this option is activated."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"eval_flag",
PySPConfigValue(
False,
domain=bool,
description=(
"Set to get an estimated objective function value "
"for the final incumbent of each replication."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"eval_seed1",
PySPConfigValue(
2668655841019641,
domain=int,
description=(
"Random number seed for re-sampling omegas during "
"optimality test. Default is None, meaning no "
"seed will be provided."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"eval_error",
PySPConfigValue(
0.01,
domain=_domain_positive,
description=(
"Objective evaluation is accurate to within "
"this much, with 95%% confidence. Default is 0.01."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"mean_dev",
PySPConfigValue(
0.05,
domain=_domain_positive,
description=("Solution tolerance for deciding the usage of "
"mean solution. Default is 0.05."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"min_iterations",
PySPConfigValue(
None,
domain=_domain_nonnegative_integer,
description=("Number of iterations which must pass before "
"optimality is checked. Default is None, meaning "
"no minimum is given."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_unique_option(
options,
"max_iterations",
PySPConfigValue(
5000,
domain=_domain_positive_integer,
description=("Maximum number of iterations for any given "
"problem. Default is 5000."),
doc=None,
visibility=0),
ap_group=_sd_group_label)
safe_declare_common_option(options,
"verbose",
ap_group=_sd_group_label)
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
safe_declare_common_option(options, "verbose")
return options
def _declare_options(cls, options=None):
if options is None:
options = PySPConfigBlock()
#
# solve and I/O related
#
safe_declare_common_option(options,
"symbolic_solver_labels")
safe_declare_common_option(options,
"solver_options")
safe_declare_common_option(options,
"solver")
safe_declare_common_option(options,
"solver_io")
safe_declare_common_option(options,
"solver_manager")
safe_declare_common_option(options,
"disable_warmstart")
safe_declare_common_option(options,
"disable_advanced_preprocessing")
safe_declare_common_option(options,
"output_solver_log")
safe_declare_common_option(options,
"keep_solver_files")
safe_declare_common_option(options,
"comparison_tolerance_for_fixed_variables")
return options
