SteppableOptimizer¶
- class SteppableOptimizer(maxiter=100)[source]¶
Bases:
qiskit.algorithms.optimizers.optimizer.Optimizer
Base class for a steppable optimizer.
This family of optimizers uses the ask and tell interface. When using this interface the user has to call
ask()
to get information about how to evaluate the fucntion (we are asking the optimizer about how to do the evaluation). This information is typically the next points at which the function is evaluated, but depending on the optimizer it can also determine whether to evaluate the function or its gradient. Once the function has been evaluated, the user calls the methodtell()
to tell the optimizer what the result of the function evaluation(s) is. The optimizer then updates its state accordingly and the user can decide whether to stop the optimization process or to repeat a step.This interface is more customizable, and allows the user to have full control over the evaluation of the function.
Examples
An example where the evaluation of the function has a chance of failing. The user, with specific knowledge about his function can catch this errors and handle them before passing the result to the optimizer.
import random import numpy as np from qiskit.algorithms.optimizers import GradientDescent def objective(x): if random.choice([True, False]): return None else: return (np.linalg.norm(x) - 1) ** 2 def grad(x): if random.choice([True, False]): return None else: return 2 * (np.linalg.norm(x) - 1) * x / np.linalg.norm(x) initial_point = np.random.normal(0, 1, size=(100,)) optimizer = GradientDescent(maxiter=20) optimizer.start(x0=initial_point, fun=objective, jac=grad) while optimizer.continue_condition(): ask_data = optimizer.ask() evaluated_gradient = None while evaluated_gradient is None: evaluated_gradient = grad(ask_data.x_center) optimizer.state.njev += 1 optmizer.state.nit += 1 cf = TellData(eval_jac=evaluated_gradient) optimizer.tell(ask_data=ask_data, tell_data=tell_data) result = optimizer.create_result()
Users that aren’t dealing with complicated functions and who are more familiar with step by step optimization algorithms can use the
step()
method which wraps theask()
andtell()
methods. In the same spirit the methodminimize()
will optimize the function and return the result.To see other libraries that use this interface one can visit: https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/009_ask_and_tell.html
- Parameters
maxiter (
int
) – Number of steps in the optimization process before ending the loop.
Methods
Ask the optimizer for a set of points to evaluate.
Condition that indicates the optimization process should continue.
Returns the result of the optimization.
Evaluates the function according to the instructions contained in
ask_data
.Return support level dictionary
We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
Minimizes the function.
Print algorithm-specific options.
Set max evals grouped
Sets or updates values in the options dictionary.
Populates the state of the optimizer with the data provided and sets all the counters to 0.
Performs one step in the optimization process.
Updates the optimization state using the results of the function evaluation.
Wrap the function to implicitly inject the args at the call of the function.
Attributes
- bounds_support_level¶
Returns bounds support level
- gradient_support_level¶
Returns gradient support level
- initial_point_support_level¶
Returns initial point support level
- is_bounds_ignored¶
Returns is bounds ignored
- is_bounds_required¶
Returns is bounds required
- is_bounds_supported¶
Returns is bounds supported
- is_gradient_ignored¶
Returns is gradient ignored
- is_gradient_required¶
Returns is gradient required
- is_gradient_supported¶
Returns is gradient supported
- is_initial_point_ignored¶
Returns is initial point ignored
- is_initial_point_required¶
Returns is initial point required
- is_initial_point_supported¶
Returns is initial point supported
- setting¶
Return setting
- settings¶
The optimizer settings in a dictionary format.
The settings can for instance be used for JSON-serialization (if all settings are serializable, which e.g. doesn’t hold per default for callables), such that the optimizer object can be reconstructed as
settings = optimizer.settings # JSON serialize and send to another server optimizer = OptimizerClass(**settings)
- Return type
Dict
[str
,Any
]
- state¶
Return the current state of the optimizer.
- Return type