SteppableOptimizer
class SteppableOptimizer(maxiter=100)
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 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.
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 the ask() and 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
Parameters
maxiter (int) – Number of steps in the optimization process before ending the loop.
Methods
ask
SteppableOptimizer.ask()
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.
Return type
Returns
An object containing the data needed to make the funciton evaluation to advance the optimization process.
continue_condition
SteppableOptimizer.continue_condition()
Condition that indicates the optimization process should continue.
Return type
bool
Returns
True if the optimization process should continue, False otherwise.
create_result
abstract SteppableOptimizer.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.
Return type
Returns
The result of the optimization process.
evaluate
abstract SteppableOptimizer.evaluate(ask_data)
Evaluates the function according to the instructions contained in ask_data.
If the user decides to use step() instead of ask() and tell() this function will contain the logic on how to evaluate the function.
Parameters
ask_data (AskData) – Contains the information on how to do the evaluation.
Return type
Returns
Data of all relevant information about the function evaluation.
get_support_level
abstract SteppableOptimizer.get_support_level()
Return support level dictionary
gradient_num_diff
static SteppableOptimizer.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.
Parameters
- x_center (ndarray) – point around which we compute the gradient
- f (func) – the function of which the gradient is to be computed.
- epsilon (float) – the epsilon used in the numeric differentiation.
- max_evals_grouped (int) – max evals grouped, defaults to 1 (i.e. no batching).
Returns
the gradient computed
Return type
grad
minimize
SteppableOptimizer.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 ask() and tell() and evaluating the function manually.
Parameters
- fun (
Callable[[Union[float,ndarray]],float]) – Function to minimize. - x0 (
Union[float,ndarray]) – Initial point. - jac (
Optional[Callable[[Union[float,ndarray]],Union[float,ndarray]]]) – Function to compute the gradient. - bounds (
Optional[List[Tuple[float,float]]]) – Bounds of the search space.
Return type
Returns
Object containing the result of the optimization.
print_options
SteppableOptimizer.print_options()
Print algorithm-specific options.
set_max_evals_grouped
SteppableOptimizer.set_max_evals_grouped(limit)
Set max evals grouped
set_options
SteppableOptimizer.set_options(**kwargs)
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.
Parameters
kwargs (dict) – options, given as name=value.
start
abstract SteppableOptimizer.start(fun, x0, jac=None, bounds=None)
Populates the state of the optimizer with the data provided and sets all the counters to 0.
Parameters
- fun (
Callable[[Union[float,ndarray]],float]) – Function to minimize. - x0 (
Union[float,ndarray]) – Initial point. - jac (
Optional[Callable[[Union[float,ndarray]],Union[float,ndarray]]]) – Function to compute the gradient. - bounds (
Optional[List[Tuple[float,float]]]) – Bounds of the search space.
Return type
None
step
SteppableOptimizer.step()
Performs one step in the optimization process.
This method composes ask(), evaluate(), and tell() to make a “step” in the optimization process.
Return type
None
tell
SteppableOptimizer.tell(ask_data, tell_data)
Updates the optimization state using the results of the function evaluation.
A canonical optimization example using ask() and tell() can be seen in step().
Parameters
- ask_data (
AskData) – Contains the information on how the evaluation was done. - tell_data (
TellData) – Contains all relevant information about the evaluation of the objective function.
Return type
None
wrap_function
static SteppableOptimizer.wrap_function(function, args)
Wrap the function to implicitly inject the args at the call of the function.
Parameters
- function (func) – the target function
- args (tuple) – the args to be injected
Returns
wrapper
Return type
function_wrapper
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]