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)

GitHub

Bases: qiskit.algorithms.optimizers.optimizer.Optimizer

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.

Note

This component has some function that is normally random. If you want to reproduce behavior then you should set the random number generator seed in the algorithm_globals (qiskit.utils.algorithm_globals.random_seed = seed).

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.

Methods

get_support_level

GSLS.get_support_level()

Return support level dictionary.

Return type

Dict[str, int]

Returns

A dictionary containing the support levels for different options.

gradient_approximation

GSLS.gradient_approximation(n, x, x_value, directions, sample_set_x, sample_set_y)

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.

gradient_num_diff

static GSLS.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

ls_optimize

GSLS.ls_optimize(n, obj_fun, initial_point, var_lb, var_ub)

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.

minimize

GSLS.minimize(fun, x0, jac=None, bounds=None)

Minimize the scalar function.

Parameters

fun (Callable[[Union[float, ndarray]], float]) – The scalar function to minimize.
x0 (Union[float, ndarray]) – The initial point for the minimization.
jac (Optional[Callable[[Union[float, ndarray]], Union[float, ndarray]]]) – The gradient of the scalar function fun.
bounds (Optional[List[Tuple[float, float]]]) – Bounds for the variables of fun. This argument might be ignored if the optimizer does not support bounds.

Return type

OptimizerResult

Returns

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

print_options

GSLS.print_options()

Print algorithm-specific options.

sample_points

GSLS.sample_points(n, x, num_points)

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

GSLS.sample_set(n, x, var_lb, var_ub, num_points)

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

GSLS.set_max_evals_grouped(limit)

Set max evals grouped

set_options

GSLS.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.

wrap_function

static GSLS.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

Return type

Dict[str, Any]

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