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