enopt

Ensemble optimization methods compatible with OptimizerBase.

EnOpt

Bases: OptimizerBase

Ensemble-based optimization (EnOpt).

obj_func_values

Legacy alias for fk.

__init__(x0, fun, jac=None, hess=None, args=(), bounds=None, callback=None, **options)

Initialize an EnOpt optimizer instance.

Parameters:

Name	Type	Description	Default
x0	ndarray	Initial control/state vector.	required
fun	callable	Objective function.	required
jac	callable	Ensemble gradient function.	None
hess	callable	Ensemble Hessian function.	None
args	tuple	The first tuple element is interpreted as the initial covariance.	()
bounds	sequence	Lower and upper bounds for each state variable.	None
callback	callable	Callback invoked after successful updates.	None
**options		EnOpt configuration, plus everything :class:OptimizerBase takes. - tol: Convergence tolerance for objective improvement (default: 1e-6). Also used as ftol when given. - step_size: Initial optimizer step size. Overrides alpha when provided. - alpha: Initial optimizer step size (default: 0.1). - alpha_cov: Covariance update scaling factor (default: 0.001). - beta: Momentum parameter used in the optimizer and optional Nesterov updates (default: 0.0). - nesterov: Whether to evaluate search quantities with Nesterov momentum (default: False). - alpha_maxiter: Maximum number of backtracking trials per iteration (default: 5). - resample: Number of covariance resampling attempts if no improvement is found (default: 0). - hessian: Whether to use the Hessian in the search direction computation (default: False). - normalize: Whether to normalize the gradient or Hessian-derived search quantities (default: True). - cov_factor: Covariance shrink factor applied during resampling (default: 0.5). - optimizer: Update rule name. Supported values are GD, Adam, AdaMax, and Steihaug (default: GD).	{}

log_columns()

The row of the iteration log: iteration, backtracking attempts, objective, step size, first covariance entry.

update_step()

Perform one EnOpt step with backtracking and optional resampling.