Basic Optimization Workflow

BasicOptimizer is the high-level driver for running a single optimization. See Basic Optimization for a walkthrough.

ropt.workflow._basic_optimizer

This module defines a basic optimization object.

ropt.workflow.BasicOptimizer

A simple interface for single optimization runs.

Wraps the workflow framework into a run-once interface with built-in result tracking.

See Basic Optimization for a walkthrough and full example.

__init__

__init__(
    config: dict[str, Any],
    evaluator: EvaluationBatchCallback | Evaluator,
    *,
    constraint_tolerance: float = 1e-10,
) -> None

Initialize a BasicOptimizer object.

This constructor sets up the necessary components for a single optimization run. It requires an optimization configuration, an evaluator, and optional domain transform, which together define the optimization problem.

The constraint_tolerance is used to check any constraints, if a constraint value is within this tolerance, it is considered satisfied.

Parameters:

Name	Type	Description	Default
config	dict[str, Any]	The configuration for the optimization.	required
evaluator	EvaluationBatchCallback | Evaluator	The evaluator object.	required
constraint_tolerance	float	The constraint violation tolerance.	1e-10

results property

results: FunctionResults | None

The optimal result found during the optimization.

This property provides access to the best FunctionResults object discovered during the optimization process. It encapsulates the objective function value, constraint values, and other relevant information about the optimal solution.

Returns:

Type	Description
FunctionResults | None	The optimal result.

run

run(initial_values: ArrayLike) -> ExitCode

Run the optimization process.

This method initiates and executes the optimization workflow defined by the BasicOptimizer object. It manages the optimization, result handling, and event processing. After the optimization is complete, the optimal results, variables, and exit code can be accessed via the corresponding properties.

Returns:

Type	Description
ExitCode	The exit code returned by the optimization workflow.

set_abort_callback

set_abort_callback(callback: Callable[[], bool]) -> None

Set a callback to check for abort conditions.

The provided callback function will be invoked repeatedly during the optimization process. If the callback returns True, the optimization will be aborted, and the BasicOptimizer will exit with an ExitCode.USER_ABORT.

The callback function should have no arguments and return a boolean value.

Parameters:

Name	Type	Description	Default
callback	Callable[[], bool]	The callable to check for abort conditions.	required

set_results_callback

set_results_callback(callback: Callable[..., None]) -> None

Set a callback to report new results.

The provided callback function will be invoked whenever new results become available during the optimization process. This allows for real-time monitoring and analysis of the optimization's progress.

The required signature of the callback function should be:

def callback(results: tuple[FunctionResults, ...]) -> None:
    ...

Parameters:

Name	Type	Description	Default
callback	Callable[..., None]	The callable that will be invoked to report new results.	required