Context Class

ropt.context

The validated configuration of a single optimization run.

EnOptContext is the frozen container holding every setting a run needs, normally built from a plain dictionary with EnOptContext.model_validate(config). See Configuration for the fields, broadcasting rules and defaults.

EnOptContext

Bases: BaseModel

The validated configuration of a single optimization run.

Holds the variables, objectives, constraints, realizations, gradient settings, samplers, filters, optimizer and backend that a run needs. It is built from plain dictionaries or configuration objects and validated on creation.

See Configuration.

Warning

Construction scales several settings in a way that cannot be undone. Treat a context as read-only afterwards, and do not rebuild one from serialized field values; persist the raw input dictionaries instead.

Attributes:

Name	Type	Description
`variables`	`VariablesConfig`	Variable settings.
`objectives`	`ObjectiveFunctionsConfig`	Objective function settings.
`linear_constraints`	`LinearConstraintsConfig \| None`	Optional linear constraint settings.
`nonlinear_constraints`	`NonlinearConstraintsConfig \| None`	Optional nonlinear constraint settings.
`realizations`	`RealizationsConfig`	Ensemble realization settings.
`optimizer`	`OptimizerConfig`	Optimizer settings.
`backend`	`BackendInstance`	Backend plugin instance used for function evaluations.
`gradient`	`GradientConfig`	Gradient estimation settings.
`realization_filters`	`RealizationFilterInstances`	Realization filter instances, by key.
`function_estimators`	`FunctionEstimatorInstances`	Function estimator instances, by key.
`samplers`	`SamplerInstances`	Sampler instances, by key.
`names`	`dict[str, tuple[str \| int, ...]]`	Optional mapping of axis names to label sequences.

get_objective_scales

get_objective_scales() -> NDArray[np.float64]

Return the scale applied to each objective.

Objectives are divided by their scale before they reach the optimizer, and multiplied by it again before they are reported. The scales are the configured scales, multiplied by an estimated factor if auto-scaling is enabled and has run.

Returns:

Type	Description
`NDArray[float64]`	The objective scales.

get_constraint_scales

get_constraint_scales() -> NDArray[np.float64] | None

Return the scale applied to each nonlinear constraint.

Returns:

Type	Description
`NDArray[float64] \| None`	The constraint scales, or `None` if there are no constraints.

get_objective_offsets

get_objective_offsets() -> NDArray[np.float64]

Return the offset applied to each objective.

Objectives have their offset subtracted before they are divided by their scale, and it is added back before they are reported.

Returns:

Type	Description
`NDArray[float64]`	The objective offsets.

get_nonlinear_constraint_bounds

get_nonlinear_constraint_bounds() -> (
    tuple[NDArray[np.float64], NDArray[np.float64]] | None
)

Return the scaled nonlinear constraint bounds.

The bounds are transformed together with the constraint values, so that the configured constraint is the constraint that is solved. Scales are positive, so the bounds keep their order.

Returns:

Type	Description
`tuple[NDArray[float64], NDArray[float64]] \| None`	The lower and upper bounds, or `None` if there are no constraints.

lock

lock() -> None

Lock the object to prevent sharing and re-use.

Raises:

Type	Description
`WorkflowError`	If the object is already locked.