- class qiskit.algorithms.optimizers.SteppableOptimizer(maxiter=100)#
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 method
tell()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.
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 the
tell()methods. In the same spirit the method
minimize()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
maxiter (int) – Number of steps in the optimization process before ending the loop.
Returns bounds support level
Returns gradient support level
Returns initial point support level
Returns is bounds ignored
Returns is bounds required
Returns is bounds supported
Returns is gradient ignored
Returns is gradient required
Returns is gradient supported
Returns is initial point ignored
Returns is initial point required
Returns is initial point supported
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 the current state of the optimizer.
Ask the optimizer for a set of points to evaluate.
This method asks the optimizer which are the next points to evaluate. These points can, e.g., correspond to function values and/or its derivative. It may also correspond to variables that let the user infer which points to evaluate. It is the first method inside of a
step()in the optimization process.
An object containing the data needed to make the funciton evaluation to advance the optimization process.
- Return type:
Condition that indicates the optimization process should continue.
Trueif the optimization process should continue,
- Return type:
- abstract create_result()#
Returns the result of the optimization.
All the information needed to create such a result should be stored in the optimizer state and will typically contain the best point found, the function value and gradient at that point, the number of function and gradient evaluation and the number of iterations in the optimization.
The result of the optimization process.
- Return type:
- abstract evaluate(ask_data)#
Evaluates the function according to the instructions contained in
- abstract get_support_level()#
Return support level dictionary
- static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)#
We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
the gradient computed
- Return type:
- minimize(fun, x0, jac=None, bounds=None)#
Minimizes the function.
For well behaved functions the user can call this method to minimize a function. If the user wants more control on how to evaluate the function a custom loop can be created using
tell()and evaluating the function manually.
Object containing the result of the optimization.
- Return type:
Print algorithm-specific options.
Set max evals grouped
Sets or updates values in the options dictionary.
The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.
kwargs (dict) – options, given as name=value.
- abstract start(fun, x0, jac=None, bounds=None)#
Populates the state of the optimizer with the data provided and sets all the counters to 0.
Performs one step in the optimization process.
- tell(ask_data, tell_data)#
Updates the optimization state using the results of the function evaluation.