qiskit.algorithms.optimizers.GSLS.ls_optimize

GSLS.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.