simopt.models.example ===================== .. py:module:: simopt.models.example .. autoapi-nested-parse:: Example problem of deterministic function with noise. Simulate a synthetic problem with a deterministic objective function evaluated with noise. Module Contents --------------- .. py:class:: ExampleModelConfig Bases: :py:obj:`pydantic.BaseModel` Configuration model for Example simulation. A model that is a deterministic function evaluated with noise. .. py:attribute:: x :type: Annotated[tuple[float, Ellipsis], Field(default=(2.0, 2.0), description='point to evaluate')] .. py:class:: ExampleProblemConfig Bases: :py:obj:`pydantic.BaseModel` Configuration model for Example Problem. Base class to implement simulation-optimization problems. .. py:attribute:: initial_solution :type: Annotated[tuple[float, Ellipsis], Field(default=(2.0, 2.0), description='initial solution')] .. py:attribute:: budget :type: Annotated[int, Field(default=1000, description='max # of replications for a solver to take', gt=0, json_schema_extra={'isDatafarmable': False})] .. py:class:: ExampleModel(fixed_factors: dict | None = None) Bases: :py:obj:`simopt.base.Model` A model that is a deterministic function evaluated with noise. Initialize the model. :param fixed_factors: fixed factors of the model. If None, use default values. :type fixed_factors: dict | None .. py:attribute:: class_name_abbr :type: ClassVar[str] :value: 'EXAMPLE' Short name of the model class. .. py:attribute:: class_name :type: ClassVar[str] :value: 'Deterministic Function + Noise' Long name of the model class. .. py:attribute:: config_class :type: ClassVar[type[pydantic.BaseModel]] Configuration class for the model. .. py:attribute:: n_rngs :type: ClassVar[int] :value: 1 Number of RNGs used to run a simulation replication. .. py:attribute:: n_responses :type: ClassVar[int] :value: 1 Number of responses (performance measures). .. py:attribute:: noise_model .. py:method:: before_replicate(rng_list: list[mrg32k3a.mrg32k3a.MRG32k3a]) -> None Prepare the model just before generating a replication. :param rng_list: RNGs used to drive the simulation. :type rng_list: list[MRG32k3a] :raises NotImplementedError: If the subclass does not implement this hook. .. py:method:: replicate() -> tuple[dict, dict] Evaluate a deterministic function f(x) with stochastic noise. :returns: A tuple containing: - responses (dict): Performance measures of interest, including: - "est_f(x)": Estimate of f(x) with added stochastic noise. - gradients (dict): A dictionary of gradient estimates for each response. :rtype: tuple[dict, dict] .. py:class:: ExampleProblem(name: str = '', fixed_factors: dict | None = None, model_fixed_factors: dict | None = None) Bases: :py:obj:`simopt.base.Problem` Base class to implement simulation-optimization problems. Initialize a problem object. :param name: Name of the problem. :type name: str :param fixed_factors: Dictionary of user-specified problem factors. :type fixed_factors: dict | None :param model_fixed_factors: Subset of user-specified non-decision factors passed to the model. :type model_fixed_factors: dict | None .. py:attribute:: class_name_abbr :type: ClassVar[str] :value: 'EXAMPLE-1' Short name of the problem class. .. py:attribute:: class_name :type: ClassVar[str] :value: 'Min Deterministic Function + Noise' Long name of the problem class. .. py:attribute:: config_class :type: ClassVar[type[pydantic.BaseModel]] Configuration class for problem. .. py:attribute:: model_class :type: ClassVar[type[simopt.base.Model]] Simulation model class for problem. .. py:attribute:: n_objectives :type: ClassVar[int] :value: 1 Number of objectives. .. py:attribute:: n_stochastic_constraints :type: ClassVar[int] :value: 0 Number of stochastic constraints. .. py:attribute:: minmax :type: ClassVar[tuple[int, Ellipsis]] Indicators of maximization (+1) or minimization (-1) for each objective. .. py:attribute:: constraint_type :type: ClassVar[simopt.base.ConstraintType] Description of constraints types. .. py:attribute:: variable_type :type: ClassVar[simopt.base.VariableType] Description of variable types. .. py:attribute:: gradient_available :type: ClassVar[bool] :value: True Indicates whether the solver provides direct gradient information. .. py:attribute:: model_default_factors :type: ClassVar[dict] Default values for overriding model-level default factors. .. py:attribute:: model_decision_factors :type: ClassVar[set[str]] Set of keys for factors that are decision variables. .. py:property:: optimal_value :type: float | None Optimal objective function value (if known). .. py:property:: optimal_solution :type: tuple | None Optimal solution if known; defaults to None. .. py:property:: dim :type: int Number of decision variables. .. py:property:: lower_bounds :type: tuple Lower bound for each decision variable. .. py:property:: upper_bounds :type: tuple Upper bound for each decision variable. .. py:method:: vector_to_factor_dict(vector: tuple) -> dict Convert a vector of variables to a dictionary with factor keys. :param vector: A vector of values associated with decision variables. :type vector: tuple :returns: Dictionary with factor keys and associated values. :rtype: dict .. py:method:: factor_dict_to_vector(factor_dict: dict) -> tuple Convert a dictionary with factor keys to a vector of variables. :param factor_dict: Dictionary with factor keys and associated values. :type factor_dict: dict :returns: Vector of values associated with decision variables. :rtype: tuple .. py:method:: replicate(_x: tuple) -> simopt.base.RepResult Replicate the problem for a given solution. :param x: The solution to evaluate. :type x: tuple .. py:method:: get_random_solution(rand_sol_rng: mrg32k3a.mrg32k3a.MRG32k3a) -> tuple Generate a random solution for starting or restarting solvers. :param rand_sol_rng: Random number generator used to sample the solution. :type rand_sol_rng: MRG32k3a :returns: A tuple representing a randomly generated vector of decision variables. :rtype: tuple