simopt.models.paramesti

Simulate MLE estimation for the parameters of a 2D gamma distribution.

Module Contents

class simopt.models.paramesti.ParameterEstimationConfig

Bases: pydantic.BaseModel

Configuration for the parameter estimation model.

xstar: Annotated[list[float], Field(default=[2, 5], description='x^*, the unknown parameter that maximizes g(x)')]

x: Annotated[list[float], Field(default=[1, 1], description='x, variable in pdf')]

class simopt.models.paramesti.ParamEstiMaxLogLikConfig

Bases: pydantic.BaseModel

Configuration model for Parameter Estimation Max Log Likelihood Problem.

Max Log Likelihood for Gamma Parameter Estimation simulation-optimization problem.

initial_solution: Annotated[tuple[float, Ellipsis], Field(default=1, 1, description='initial solution')]

budget: Annotated[int, Field(default=1000, description='max # of replications for a solver to take', gt=0, json_schema_extra={'isDatafarmable': False})]

class simopt.models.paramesti.ParameterEstimation(fixed_factors: dict | None = None)

Bases: simopt.base.Model

MLE estimation model for the parameters of a 2D gamma distribution.

Initialize the model.

Parameters:

fixed_factors (dict, optional) – Fixed factors of the simulation model. Defaults to None.

class_name_abbr: ClassVar[str] = 'PARAMESTI'

Short name of the model class.

class_name: ClassVar[str] = 'Gamma Parameter Estimation'

Long name of the model class.

config_class: ClassVar[type[pydantic.BaseModel]]

Configuration class for the model.

n_rngs: ClassVar[int] = 2

Number of RNGs used to run a simulation replication.

n_responses: ClassVar[int] = 1

Number of responses (performance measures).

y1_model

y2_model

before_replicate(rng_list: list[mrg32k3a.mrg32k3a.MRG32k3a]) → None

Prepare the model just before generating a replication.

Parameters:

rng_list (list[MRG32k3a]) – RNGs used to drive the simulation.

Raises:

NotImplementedError – If the subclass does not implement this hook.

replicate() → tuple[dict, dict]

Simulate a single replication for the current model factors.

Returns:

A tuple containing:

• responses (dict): Performance measures of interest, including:

  • ”loglik”: The corresponding log-likelihood.

• gradients (dict): A dictionary of gradient estimates for

  each response.

Return type:

tuple[dict, dict]

class simopt.models.paramesti.ParamEstiMaxLogLik(name: str = '', fixed_factors: dict | None = None, model_fixed_factors: dict | None = None)

Bases: simopt.base.Problem

Base class to implement simulation-optimization problems.

Initialize a problem object.

Parameters:

• name (str) – Name of the problem.

• fixed_factors (dict | None) – Dictionary of user-specified problem factors.

• model_fixed_factors (dict | None) – Subset of user-specified non-decision factors passed to the model.

class_name_abbr: ClassVar[str] = 'PARAMESTI-1'

Short name of the problem class.

class_name: ClassVar[str] = 'Max Log Likelihood for Gamma Parameter Estimation'

Long name of the problem class.

config_class: ClassVar[type[pydantic.BaseModel]]

Configuration class for problem.

model_class: ClassVar[type[simopt.base.Model]]

Simulation model class for problem.

n_objectives: ClassVar[int] = 1

Number of objectives.

n_stochastic_constraints: ClassVar[int] = 0

Number of stochastic constraints.

minmax: ClassVar[tuple[int, Ellipsis]] = (1,)

Indicators of maximization (+1) or minimization (-1) for each objective.

constraint_type: ClassVar[simopt.base.ConstraintType]

Description of constraints types.

variable_type: ClassVar[simopt.base.VariableType]

Description of variable types.

gradient_available: ClassVar[bool] = False

Indicates whether the solver provides direct gradient information.

model_default_factors: ClassVar[dict]

Default values for overriding model-level default factors.

model_decision_factors: ClassVar[set[str]]

Set of keys for factors that are decision variables.

property optimal_value: float | None

Optimal objective function value (if known).

property optimal_solution: tuple | None

Optimal solution if known; defaults to None.

property dim: int

Number of decision variables.

property lower_bounds: tuple

Lower bound for each decision variable.

property upper_bounds: tuple

Upper bound for each decision variable.

vector_to_factor_dict(vector: tuple) → dict

Convert a vector of variables to a dictionary with factor keys.

Parameters:

vector (tuple) – A vector of values associated with decision variables.

Returns:

Dictionary with factor keys and associated values.

Return type:

dict

factor_dict_to_vector(factor_dict: dict) → tuple

Convert a dictionary with factor keys to a vector of variables.

Parameters:

factor_dict (dict) – Dictionary with factor keys and associated values.

Returns:

Vector of values associated with decision variables.

Return type:

tuple

replicate(_x: tuple) → simopt.base.RepResult

Replicate the problem for a given solution.

Parameters:

x (tuple) – The solution to evaluate.

check_deterministic_constraints(_x: tuple) → bool

Check if a solution x satisfies the problem’s deterministic constraints.

Parameters:

x (tuple) – A vector of decision variables.

Returns:

True if the solution satisfies all deterministic constraints;

False otherwise.

Return type:

bool

get_random_solution(rand_sol_rng: mrg32k3a.mrg32k3a.MRG32k3a) → tuple

Generate a random solution for starting or restarting solvers.

Parameters:

rand_sol_rng (MRG32k3a) – Random number generator used to sample the solution.

Returns:

A tuple representing a randomly generated vector of decision

variables.

Return type:

tuple