GSLS
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 (opens in a new tab), 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 (opens in a new tab)) – Maximum number of iterations.
- max_eval (int (opens in a new tab)) – Maximum number of evaluations.
- disp (bool (opens in a new tab)) – Set to True to display convergence messages.
- sampling_radius (float (opens in a new tab)) – Sampling radius to determine gradient estimate.
- sample_size_factor (int (opens in a new tab)) – 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 (opens in a new tab)) – Initial step size for the descent algorithm.
- min_step_size (float (opens in a new tab)) – Minimum step size for the descent algorithm.
- step_size_multiplier (float (opens in a new tab)) – Step size reduction after unsuccessful steps, in the interval (0, 1).
- armijo_parameter (float (opens in a new tab)) – Armijo parameter for sufficient decrease criterion, in the interval (0, 1).
- min_gradient_norm (float (opens in a new tab)) – If the gradient norm is below this threshold, the algorithm stops.
- max_failed_rejection_sampling (int (opens in a new tab)) – 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
dict (opens in a new tab)[str (opens in a new tab), int (opens in a new tab)]
gradient_approximation
gradient_approximation(n, x, x_value, directions, sample_set_x, sample_set_y)
Construct gradient approximation from given sample.
Parameters
- n (int (opens in a new tab)) – Dimension of the problem.
- x (ndarray (opens in a new tab)) – Point around which the sample set was constructed.
- x_value (float (opens in a new tab)) – Objective function value at x.
- directions (ndarray (opens in a new tab)) – Directions of the sample points wrt the central point x, as a 2D array.
- sample_set_x (ndarray (opens in a new tab)) – x-coordinates of the sample set, one point per row, as a 2D array.
- sample_set_y (ndarray (opens in a new tab)) – 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 (opens in a new tab)) – the epsilon used in the numeric differentiation.
- max_evals_grouped (int (opens in a new tab)) – 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 (opens in a new tab)) – Dimension of the problem.
- obj_fun (Callable (opens in a new tab)[[float (opens in a new tab) |ndarray (opens in a new tab)], float (opens in a new tab)]) – Objective function.
- initial_point (ndarray (opens in a new tab)) – Initial point.
- var_lb (ndarray (opens in a new tab)) – 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 (opens in a new tab)) – 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 (opens in a new tab) – If the number of dimensions mismatches the size of the initial point or the length of the lower or upper bound.
Return type
tuple (opens in a new tab)[numpy.ndarray (opens in a new tab), float (opens in a new tab), int (opens in a new tab), float (opens in a new tab)]
minimize
minimize(fun, x0, jac=None, bounds=None)
Minimize the scalar function.
Parameters
- fun (Callable[[POINT], float (opens in a new tab)]) – 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 (opens in a new tab)[tuple (opens in a new tab)[float (opens in a new tab), float (opens in a new tab)]] | 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 (opens in a new tab)) – Dimension of the problem.
- x (ndarray (opens in a new tab)) – Point around which the sample set is constructed.
- num_points (int (opens in a new tab)) – Number of points in the sample set.
Returns
A tuple containing the sampling points and the directions.
Return type
tuple (opens in a new tab)[numpy.ndarray (opens in a new tab), numpy.ndarray (opens in a new tab)]
sample_set
sample_set(n, x, var_lb, var_ub, num_points)
Construct sample set of given size.
Parameters
- n (int (opens in a new tab)) – Dimension of the problem.
- x (ndarray (opens in a new tab)) – Point around which the sample set is constructed.
- var_lb (ndarray (opens in a new tab)) – 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 (opens in a new tab)) – 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 (opens in a new tab)) – 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 (opens in a new tab) – If not enough samples could be generated within the bounds.
Return type
tuple (opens in a new tab)[numpy.ndarray (opens in a new tab), numpy.ndarray (opens in a new tab)]
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 (opens in a new tab)) – 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 (opens in a new tab)) – the args to be injected
Returns
wrapper
Return type
function_wrapper