qiskit.aqua.components.optimizers.GSLS¶

class GSLS(maxiter=10000, max_eval=10000, disp=False, sampling_radius=1e-06, sample_size_factor=1, initial_step_size=0.01, min_step_size=1e-10, step_size_multiplier=0.4, armijo_parameter=0.1, min_gradient_norm=1e-08, max_failed_rejection_sampling=50, max_iter=None)[source]¶

Gaussian-smoothed Line Search.

An implementation of the line search algorithm described in https://arxiv.org/pdf/1905.01332.pdf, using gradient approximation based on Gaussian-smoothed samples on a sphere.

Parameters

maxiter (int) – Maximum number of iterations.
max_eval (int) – Maximum number of evaluations.
disp (bool) – Set to True to display convergence messages.
sampling_radius (float) – Sampling radius to determine gradient estimate.
sample_size_factor (int) – The size of the sample set at each iteration is this number multiplied by the dimension of the problem, rounded to the nearest integer.
initial_step_size (float) – Initial step size for the descent algorithm.
min_step_size (float) – Minimum step size for the descent algorithm.
step_size_multiplier (float) – Step size reduction after unsuccessful steps, in the interval (0, 1).
armijo_parameter (float) – Armijo parameter for sufficient decrease criterion, in the interval (0, 1).
min_gradient_norm (float) – If the gradient norm is below this threshold, the algorithm stops.
max_failed_rejection_sampling (int) – Maximum number of attempts to sample points within bounds.
max_iter (Optional[int]) – Deprecated, use maxiter.

__init__(maxiter=10000, max_eval=10000, disp=False, sampling_radius=1e-06, sample_size_factor=1, initial_step_size=0.01, min_step_size=1e-10, step_size_multiplier=0.4, armijo_parameter=0.1, min_gradient_norm=1e-08, max_failed_rejection_sampling=50, max_iter=None)[source]¶

Parameters

maxiter (int) – Maximum number of iterations.
max_eval (int) – Maximum number of evaluations.
disp (bool) – Set to True to display convergence messages.
sampling_radius (float) – Sampling radius to determine gradient estimate.
sample_size_factor (int) – The size of the sample set at each iteration is this number multiplied by the dimension of the problem, rounded to the nearest integer.
initial_step_size (float) – Initial step size for the descent algorithm.
min_step_size (float) – Minimum step size for the descent algorithm.
step_size_multiplier (float) – Step size reduction after unsuccessful steps, in the interval (0, 1).
armijo_parameter (float) – Armijo parameter for sufficient decrease criterion, in the interval (0, 1).
min_gradient_norm (float) – If the gradient norm is below this threshold, the algorithm stops.
max_failed_rejection_sampling (int) – Maximum number of attempts to sample points within bounds.
max_iter (Optional[int]) – Deprecated, use maxiter.

Methods

`__init__`([maxiter, max_eval, disp, …])	type maxiter `int`
`get_support_level`()	Return support level dictionary.
`gradient_approximation`(n, x, x_value, …)	Construct gradient approximation from given sample.
`gradient_num_diff`(x_center, f, epsilon[, …])	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`ls_optimize`(n, obj_fun, initial_point, …)	Run the line search optimization.
`optimize`(num_vars, objective_function[, …])	Perform optimization.
`print_options`()	Print algorithm-specific options.
`sample_points`(n, x, num_points)	Sample `num_points` points around `x` on the `n`-sphere of specified radius.
`sample_set`(n, x, var_lb, var_ub, num_points)	Construct sample set of given size.
`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]¶

Return support level dictionary.

Return type: Dict[str, int]
Returns: A dictionary containing the support levels for different options.

gradient_approximation(n, x, x_value, directions, sample_set_x, sample_set_y)[source]¶

Construct gradient approximation from given sample.

Parameters

n (int) – Dimension of the problem.
x (ndarray) – Point around which the sample set was constructed.
x_value (float) – Objective function value at x.
directions (ndarray) – Directions of the sample points wrt the central point x, as a 2D array.
sample_set_x (ndarray) – x-coordinates of the sample set, one point per row, as a 2D array.
sample_set_y (ndarray) – Objective function values of the points in sample_set_x, as a 1D array.

Return type

ndarray

Returns

Gradient approximation at x, as a 1D array.

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

ls_optimize(n, obj_fun, initial_point, var_lb, var_ub)[source]¶

Run the line search optimization.

Parameters

n (int) – Dimension of the problem.
obj_fun (Callable) – Objective function.
initial_point (ndarray) – Initial point.
var_lb (ndarray) – Vector of lower bounds on the decision variables. Vector elements can be -np.inf if the corresponding variable is unbounded from below.
var_ub (ndarray) – Vector of upper bounds on the decision variables. Vector elements can be np.inf if the corresponding variable is unbounded from below.

Return type

Tuple[ndarray, float, int, float]

Returns

Final iterate as a vector, corresponding objective function value, number of evaluations, and norm of the gradient estimate.

Raises

ValueError – If the number of dimensions mismatches the size of the initial point or the length of the lower or upper bound.

optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)[source]¶

Perform optimization.

Parameters

num_vars (int) – Number of parameters to be optimized.
objective_function (callable) – A function that computes the objective function.
gradient_function (callable) – A function that computes the gradient of the objective function, or None if not available.
variable_bounds (list[(float, float)]) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
initial_point (numpy.ndarray[float]) – Initial point.

Return type

Tuple[ndarray, float, int]

Returns

point, value, nfev: point: is a 1D numpy.ndarray[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None

Raises

ValueError – invalid input

print_options()¶: Print algorithm-specific options.

sample_points(n, x, num_points)[source]¶

Sample num_points points around x on the n-sphere of specified radius.

The radius of the sphere is self._options['sampling_radius'].

Parameters

n (int) – Dimension of the problem.
x (ndarray) – Point around which the sample set is constructed.
num_points (int) – Number of points in the sample set.

Return type

Tuple[ndarray, ndarray]

Returns

A tuple containing the sampling points and the directions.

sample_set(n, x, var_lb, var_ub, num_points)[source]¶

Construct sample set of given size.

Parameters

n (int) – Dimension of the problem.
x (ndarray) – Point around which the sample set is constructed.
var_lb (ndarray) – Vector of lower bounds on the decision variables. Vector elements can be -np.inf if the corresponding variable is unbounded from below.
var_ub (ndarray) – Vector of lower bounds on the decision variables. Vector elements can be np.inf if the corresponding variable is unbounded from above.
num_points (int) – Number of points in the sample set.

Return type

Tuple[ndarray, ndarray]

Returns

Matrices of (unit-norm) sample directions and sample points, one per row. Both matrices are 2D arrays of floats.

Raises

RuntimeError – If not enough samples could be generated within the bounds.

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