GSLS
class qiskit.algorithms.optimizers.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)
Bases: 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.
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.
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
Methods
get_support_level
get_support_level()
Return support level dictionary.
Returns
A dictionary containing the support levels for different options.
Return type
gradient_approximation
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.
Returns
Gradient approximation at x, as a 1D array.
Return type
gradient_num_diff
static 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
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[[float |ndarray], float]) – 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.
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.
Return type
tuple[numpy.ndarray, float, int, float]
minimize
minimize(fun, x0, jac=None, bounds=None)
Minimize the scalar function.
Parameters
- fun (Callable[[POINT], float]) – The scalar function to minimize.
- x0 (POINT) – The initial point for the minimization.
- jac (Callable[[POINT], POINT] | None) – The gradient of the scalar function
fun. - bounds (list[tuple[float, float]] | None) – Bounds for the variables of
fun. This argument might be ignored if the optimizer does not support bounds.
Returns
The result of the optimization, containing e.g. the result as attribute x.
Return type
print_options
print_options()
Print algorithm-specific options.
sample_points
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.
Returns
A tuple containing the sampling points and the directions.
Return type
sample_set
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.
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.
Return type
set_max_evals_grouped
set_max_evals_grouped(limit)
Set max evals grouped
set_options
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 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