smcopt

Stochastic Monte-Carlo optimisation.

SmcOpt

Bases: Optimize

TODO: Write docstring ala EnOpt

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

Parameters:

Name	Type	Description	Default
fun	callable	objective function	required
x	ndarray	Initial state	required
sens	callable	Ensemble sensitivity	required
bounds	list	(min, max) pairs for each element in x. None is used to specify no bound.	None
options	dict	Optimization options maxiter: maximum number of iterations (default 10) restart: restart optimization from a restart file (default false) restartsave: save a restart file after each successful iteration (defalut false) tol: convergence tolerance for the objective function (default 1e-6) alpha: weight between previous and new step (default 0.1) alpha_maxiter: maximum number of backtracing 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 (defalut 0.5) inflation_factor: term used to weight down prior influence (defalult 1) survival_factor: fraction of surviving samples savedata: specify which class variables to save to the result files (state, objective function value, iteration number, number of function evaluations, and number of gradient evaluations, are always saved)	{}

calc_update()

Update using sequential monte carlo method