qiskit.aqua.components.optimizers.SNOBFIT¶

class SNOBFIT(maxiter=1000, maxfail=10, maxmp=None, verbose=False)[source]¶

Stable Noisy Optimization by Branch and FIT algorithm.

SnobFit is used for the optimization of derivative-free, noisy objective functions providing robust and fast solutions of problems with continuous variables varying within bound.

Uses skquant.opt installed with pip install scikit-quant. For further detail, please refer to https://github.com/scikit-quant/scikit-quant and https://qat4chem.lbl.gov/software.

Parameters

maxiter (int) – Maximum number of function evaluations.
maxmp (Optional[int]) – Maximum number of model points requested for the local fit. Default = 2 * number of parameters + 6 set to this value when None.
maxfail (int) – Maximum number of failures to improve the solution. Stops the algorithm after maxfail is reached.
verbose (bool) – Provide verbose (debugging) output.

Raises

MissingOptionalLibraryError – scikit-quant or SQSnobFit not installed

__init__(maxiter=1000, maxfail=10, maxmp=None, verbose=False)[source]¶

Parameters

maxiter (int) – Maximum number of function evaluations.
maxmp (Optional[int]) – Maximum number of model points requested for the local fit. Default = 2 * number of parameters + 6 set to this value when None.
maxfail (int) – Maximum number of failures to improve the solution. Stops the algorithm after maxfail is reached.
verbose (bool) – Provide verbose (debugging) output.

Raises

MissingOptionalLibraryError – scikit-quant or SQSnobFit not installed

Methods

`__init__`([maxiter, maxfail, maxmp, verbose])	type maxiter `int`
`get_support_level`()	Returns support level dictionary.
`gradient_num_diff`(x_center, f, epsilon[, …])	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`optimize`(num_vars, objective_function[, …])	Runs the optimization.
`print_options`()	Print algorithm-specific options.
`set_max_evals_grouped`(limit)	Set max evals grouped
`set_options`(**kwargs)	Sets or updates values in the options dictionary.
`wrap_function`(function, args)	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

property bounds_support_level¶: Returns bounds support level

get_support_level()[source]¶: Returns support level dictionary.

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)¶

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

Returns

the gradient computed

Return type

grad

property gradient_support_level¶: Returns gradient support level

property initial_point_support_level¶: Returns initial point support level

property is_bounds_ignored¶: Returns is bounds ignored

property is_bounds_required¶: Returns is bounds required

property is_bounds_supported¶: Returns is bounds supported

property is_gradient_ignored¶: Returns is gradient ignored

property is_gradient_required¶: Returns is gradient required

property is_gradient_supported¶: Returns is gradient supported

property is_initial_point_ignored¶: Returns is initial point ignored

property is_initial_point_required¶: Returns is initial point required

property is_initial_point_supported¶: Returns is initial point supported

optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)[source]¶: Runs the optimization.

print_options()¶: Print algorithm-specific options.

set_max_evals_grouped(limit)¶: Set max evals grouped

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.

property setting¶: Return setting

static 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