piglot.optimisers.spsa.SPSA

class SPSA(objective: Objective, alpha=0.602, gamma=0.101, prob=0.5, seed=1, A=None, a=None, c=None)[source]

Bases: ScalarOptimiser

Simultaneous Perturbation Stochastic Approximation method for optimisation.

Reference: https://ieeexplore.ieee.org/document/705889

Methods

_optimise(self, func, n_dim, n_iter, bound, init_shot):: Solves the optimization problem

Methods

optimise

Optimiser for the outside world.

optimise(n_iter: int, parameters: ~piglot.parameter.ParameterSet, output_dir: str, stop_criteria: ~piglot.optimiser.StoppingCriteria = <piglot.optimiser.StoppingCriteria object>, verbose: bool = True) → Tuple[float, ndarray]

Optimiser for the outside world.

Parameters

objectiveObjective: Objective function to optimise.
n_iterint: Maximum number of iterations.
parametersParameterSet: Set of parameters to optimise.
output_dirstr: Whether to write output to the output directory, by default None.
stop_criteriaStoppingCriteria: List of stopping criteria, by default none attributed.
verbosebool: Whether to output progress status, by default True.

Returns

float: Best observed objective value.
np.ndarray: Observed optimum of the objective.