squlearn.optimizers.SGLBO

class squlearn.optimizers.SGLBO(options: dict = None, callback=<function default_callback>)

sQUlearn’s implementation of the SGLBO optimizer

Possible options that can be set in the options dictionary are:

  • tol (float): Tolerance for the termination of the optimization (default: 1e-6)

  • maxiter (int): Maximum number of iterations per fit run (default: 100)

  • maxiter_total (int): Maximum number of iterations in total (default: maxiter)

  • eps (float): Step size for finite differences (default: 0.01)

  • num_average (int): Number of gradients to average (default: 1)

  • bo_n_calls (int): Number of iterations for the Bayesian Optimization (default: 20)

  • bo_bounds (List): Lower and upper bound for the search space for the Bayesian Optimization for each dimension. Each bound should be provided as a tupel (default: (0.0, 0.3))

  • bo_n_initial_points (int): Number of initial points for the Bayesian Optimization (default: 10)

  • bo_acq_func (str): Acquisition function for the Bayesian Optimization (default: “ei”). Valid values are: “ucb”, “ei”, “poi”

  • bo_xi (float): Exploration-exploitation trade-off parameter for the Bayesian Optimization (default: 0.01)

  • log_file (str): File to log the optimization (default: None)

Parameters:

options (dict) – Options for the SGLBO optimizer

minimize(fun: callable, x0: ndarray, grad: callable = None, bounds=None) → OptimizerResult

Function to minimize a given function using the SGLBO optimizer.

Parameters:

  • fun (callable) – Function to minimize.

  • x0 (np.ndarray) – Initial guess.

  • grad (callable) – Gradient of the function to minimize.

  • bounds (sequence) – Bounds for the parameters.

Returns:

OptimizerResult format.

Return type:

Result of the optimization in class

reset()

Resets the optimizer to its initial state.

set_callback(callback)

Set the callback function.

step(**kwargs)

Perform one update step.

Parameters:

  • x – Current value

  • grad – Precomputed gradient

Returns:

Updated x