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)[source]¶

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`	Return support level dictionary.
`gradient_approximation`	Construct gradient approximation from given sample.
`gradient_num_diff`	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`ls_optimize`	Run the line search optimization.
`optimize`	Perform optimization.
`print_options`	Print algorithm-specific options.
`sample_points`	Sample `num_points` points around `x` on the `n`-sphere of specified radius.
`sample_set`	Construct sample set of given size.
`set_max_evals_grouped`	Set max evals grouped
`set_options`	Sets or updates values in the options dictionary.
`wrap_function`	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

settings¶

Return type: Dict[str, Any]