Optimizer#

class qiskit.algorithms.optimizers.Optimizer[source]#

Bases: ABC

Base class for optimization algorithm.

Initialize the optimization algorithm, setting the support level for _gradient_support_level, _bound_support_level, _initial_point_support_level, and empty options.

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)

Methods

abstract get_support_level()[source]#: Return support level dictionary

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)[source]#

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

abstract minimize(fun, x0, jac=None, bounds=None)[source]#

Minimize the scalar function.

Parameters:

fun (Callable[[POINT], float]) – The scalar function to minimize.
x0 (POINT) – The initial point for the minimization.
jac (Callable[[POINT], POINT] | None) – The gradient of the scalar function fun.
bounds (list[tuple[float, float]] | None) – Bounds for the variables of fun. This argument might be ignored if the optimizer does not support bounds.

Returns:

The result of the optimization, containing e.g. the result as attribute x.

Return type:

OptimizerResult

print_options()[source]#: Print algorithm-specific options.

set_max_evals_grouped(limit)[source]#: Set max evals grouped

set_options(**kwargs)[source]#

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.

static wrap_function(function, args)[source]#

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