qiskit.aqua.components.optimizers.GSLS

__init__(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, max_iter=None)

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.
• max_iter (Optional[int]) – Deprecated, use maxiter.

Returns bounds support level

get_support_level()

Return support level dictionary.

Return type

Dict[str, int]

Returns

A dictionary containing the support levels for different options.

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.

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)

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

Returns

the gradient computed

Return type

grad

Returns gradient support level

Returns initial point support level

Returns is bounds ignored

Returns is bounds required

Returns is bounds supported

Returns is gradient ignored

Returns is gradient required

Returns is gradient supported

Returns is initial point ignored

Returns is initial point required

Returns is initial point supported

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.

optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)

Perform optimization.

Parameters

• num_vars (int) – Number of parameters to be optimized.
• objective_function (callable) – A function that computes the objective function.
• gradient_function (callable) – A function that computes the gradient of the objective function, or None if not available.
• variable_bounds (list[(float, float)]) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
• initial_point (numpy.ndarray[float]) – Initial point.

Return type

Tuple[ndarray, float, int]

Returns

point, value, nfev

point: is a 1D numpy.ndarray[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None

Raises

ValueError – invalid input

print_options()

Print algorithm-specific options.

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(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(limit)

Set max evals grouped

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.

Return setting

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