Compute Steps

ropt.plugins.compute_step.default

This module provides the default plugin implementations for compute steps.

Supported Components:

• ensemble_evaluator: Performs ensemble evaluations (DefaultEnsembleEvaluatorComputeStep).
• optimizer: Runs an optimization algorithm using a configured optimizer plugin (DefaultOptimizerComputeStep).

ropt.plugins.compute_step.default.DefaultComputeStepPlugin

Bases: ComputeStepPlugin

The default plugin for creating compute_steps.

This plugin acts as a factory for the standard ComputeStep implementations provided by ropt.

Supported Compute Steps:

• ensemble_evaluator: Creates a DefaultEnsembleEvaluatorComputeStep instance, which performs ensemble evaluations.
• optimizer: Creates a DefaultOptimizerComputeStep instance, which runs an optimization algorithm using a configured optimizer plugin.

ropt.plugins.compute_step.ensemble_evaluator.DefaultEnsembleEvaluatorComputeStep

Bases: ComputeStep

The default ensemble evaluator compute step for optimization workflows.

This compute step performs one or more ensemble evaluations based on the provided variables. It yields a tuple of FunctionResults objects, one for each input variable vector evaluated.

The compute step emits the following events:

• START_ENSEMBLE_EVALUATOR: Emitted before the evaluation process begins.
• START_EVALUATION: Emitted just before the underlying ensemble evaluation is called.
• FINISHED_EVALUATION: Emitted after the evaluation completes, carrying the generated FunctionResults in its data dictionary under the key "results". Event handlers typically listen for this event.
• FINISHED_ENSEMBLE_EVALUATOR: Emitted after the entire compute step, including result emission, is finished.

__init__

__init__(*, evaluator: Evaluator) -> None

Initialize a default evaluator.

Parameters:

Name	Type	Description	Default
evaluator	Evaluator	The evaluator object to run function evaluations.	required

run

run(
    config: EnOptConfig,
    variables: ArrayLike,
    *,
    transforms: OptModelTransforms | None = None,
    metadata: dict[str, Any] | None = None,
) -> ExitCode

Run the ensemble evaluator.

This method executes the core logic of the ensemble evaluator. It requires an optimizer configuration (EnOptConfig) and optionally accepts specific variable vectors to evaluate.

If metadata is provided, it is attached to the Results objects emitted via the FINISHED_EVALUATION event.

Parameters:

Name	Type	Description	Default
config	EnOptConfig	Optimizer configuration.	required
variables	ArrayLike	Variable vector(s) to evaluate.	required
transforms	OptModelTransforms | None	Optional transforms to apply to the variables, objectives, and constraints.	None
metadata	dict[str, Any] | None	Optional dictionary to attach to emitted FunctionResults.	None

Returns:

Type	Description
ExitCode	An ExitCode indicating the outcome.

ropt.plugins.compute_step.optimizer.DefaultOptimizerComputeStep

Bases: ComputeStep

The default optimizer compute step.

This compute step executes an optimization algorithm based on a provided configuration (EnOptConfig or a compatible dictionary). It iteratively performs function and potentially gradient evaluations, yielding a sequence of FunctionResults and GradientResults objects.

While initial variable values are typically specified in the configuration, they can be overridden by passing them directly to the run method.

The following events are emitted during execution:

• START_OPTIMIZER: Emitted just before the optimization process begins.
• START_EVALUATION: Emitted immediately before an ensemble evaluation (for functions or gradients) is requested from the underlying optimizer.
• FINISHED_EVALUATION: Emitted after an evaluation completes. This event carries the generated Results object(s) in its data dictionary under the key "results". Event handlers typically listen for this event to process or track optimization progress.
• FINISHED_OPTIMIZER: Emitted after the entire optimization process concludes (successfully, or due to termination conditions or errors).

This compute step also supports nested optimization. If a nested_optimization function is provided to the run method, the optimizer will execute a nested optimization at as part of each function evaluation. The nested_optimization function is expected to return a single FunctionResults object and a flag that indicates whether the optimization was aborted by the user.

__init__

__init__(*, evaluator: Evaluator) -> None

Initialize a default optimizer.

Parameters:

Name	Type	Description	Default
evaluator	Evaluator	The evaluator object to run function evaluations.	required

run

run(
    config: EnOptConfig,
    variables: ArrayLike,
    *,
    transforms: OptModelTransforms | None = None,
    nested_optimization: NestedOptimizationCallable
    | None = None,
    metadata: dict[str, Any] | None = None,
) -> ExitCode

Run the compute step to perform an optimization.

This method executes the core logic of the optimizer compute step. It requires an optimizer configuration (EnOptConfig) and optionally accepts specific initial variable vectors, and/or a nested optimization workflow, and metadata.

If variables are not provided, the initial values specified in the config are used. If variables are provided, they override the config's initial values.

If metadata is provided, it is attached to the Results objects emitted via the FINISHED_EVALUATION event.

If a nested_optimization callable is provided, a callable will be called passing the initial variables to use. The callable should return a a single FunctionResults object that should contain the results of the nested optimization, and a flag indicating whether the optimization was aborted by the user.

Parameters:

Name	Type	Description	Default
config	EnOptConfig	Optimizer configuration.	required
transforms	OptModelTransforms | None	Optional transforms to apply to the variables, objectives, and constraints.	None
variables	ArrayLike	Optional initial variable vector(s) to start from.	required
nested_optimization	NestedOptimizationCallable | None	Optional callable to run a nested optimization.	None
metadata	dict[str, Any] | None	Optional dictionary to attach to emitted Results.	None

Returns:

Type	Description
ExitCode	An exit code indicating the outcome of the optimization.