smcopt

Stochastic Monte-Carlo optimization compatible with OptimizerBase.

SmcOpt

Bases: OptimizerBase

Sequential Monte-Carlo optimizer with resampling and backtracking.

obj_func_values

Legacy alias for fk.

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

Parameters:

Name	Type	Description	Default
x0	ndarray	Initial state	required
fun	callable	objective function	required
sens	callable	Ensemble sensitivity function	None
args	tuple	Initial covariance tuple where args[0] is the covariance matrix used for sampling.	()
bounds	list	(min, max) pairs for each element in x. None is used to specify no bound.	None
callback	callable	Callback invoked after successful updates.	None
options	dict	SmcOpt configuration, plus everything :class:OptimizerBase takes (transform is forced off: SmcOpt works in physical coordinates). tol: convergence tolerance for the objective function (default 1e-6). Also used as ftol when given. alpha: weight between previous and new step (default 0.1) alpha_maxiter: maximum number of backtracking trials (default 5) resample: number indicating how many times resampling is tried if no improvement is found cov_factor: factor used to shrink the covariance for each resampling trial (default 0.5) inflation_factor: term used to weight down prior influence (default 1.0) survival_factor: fraction of surviving samples (clipped to [0.1, 1.0]) best_func: best objective value seen before this run (default: the initial objective) savefolder/save_folder: folder used when saveit is true (default './')	{}

log_columns()

The row of the iteration log: iteration, backtracking attempts, objective, best objective seen, step size.

update_step()

Perform one SMC update step with backtracking and optional resampling.