GSLS

class GSLS(max_iter=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)

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

• max_iter (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.

Attributes

GSLS.bounds_support_level	Returns bounds support level
GSLS.gradient_support_level	Returns gradient support level
GSLS.initial_point_support_level	Returns initial point support level
GSLS.is_bounds_ignored	Returns is bounds ignored
GSLS.is_bounds_required	Returns is bounds required
GSLS.is_bounds_supported	Returns is bounds supported
GSLS.is_gradient_ignored	Returns is gradient ignored
GSLS.is_gradient_required	Returns is gradient required
GSLS.is_gradient_supported	Returns is gradient supported
GSLS.is_initial_point_ignored	Returns is initial point ignored
GSLS.is_initial_point_required	Returns is initial point required
GSLS.is_initial_point_supported	Returns is initial point supported
GSLS.setting	Return setting

Methods

GSLS.get_support_level()	Return support level dictionary.
GSLS.gradient_approximation(n, x, x_value, …)	Construct gradient approximation from given sample.
GSLS.gradient_num_diff(x_center, f, epsilon)	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
GSLS.ls_optimize(n, obj_fun, initial_point, …)	Run the line search optimization.
GSLS.optimize(num_vars, objective_function)	Perform optimization.
GSLS.print_options()	Print algorithm-specific options.
GSLS.sample_points(n, x, num_points)	Sample num_points points around x on the n-sphere of specified radius.
GSLS.sample_set(n, x, var_lb, var_ub, num_points)	Construct sample set of given size.
GSLS.set_max_evals_grouped(limit)	Set max evals grouped
GSLS.set_options(**kwargs)	Sets or updates values in the options dictionary.
GSLS.wrap_function(function, args)	Wrap the function to implicitly inject the args at the call of the function.