Source code for qutip.control.termcond

# -*- coding: utf-8 -*-
# @author: Alexander Pitchford
# @email1: agp1@aber.ac.uk
# @email2: alex.pitchford@gmail.com
# @organization: Aberystwyth University
# @supervisor: Daniel Burgarth

"""
Classes containing termination conditions for the control pulse optimisation
i.e. attributes that will be checked during the optimisation, that
will determine if the algorithm has completed its task / exceeded limits
"""


[docs]class TerminationConditions(object): """ Base class for all termination conditions Used to determine when to stop the optimisation algorithm Note different subclasses should be used to match the type of optimisation being used Attributes ---------- fid_err_targ : float Target fidelity error fid_goal : float goal fidelity, e.g. 1 - self.fid_err_targ It its typical to set this for unitary systems max_wall_time : float # maximum time for optimisation (seconds) min_gradient_norm : float Minimum normalised gradient after which optimisation will terminate max_iterations : integer Maximum iterations of the optimisation algorithm max_fid_func_calls : integer Maximum number of calls to the fidelity function during the optimisation algorithm accuracy_factor : float Determines the accuracy of the result. Typical values for accuracy_factor are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy scipy.optimize.fmin_l_bfgs_b factr argument. Only set for specific methods (fmin_l_bfgs_b) that uses this Otherwise the same thing is passed as method_option ftol (although the scale is different) Hence it is not defined here, but may be set by the user """ def __init__(self): self.reset() def reset(self): self.fid_err_targ = 1e-5 self.fid_goal = None self.max_wall_time = 60*60.0 self.min_gradient_norm = 1e-5 self.max_iterations = 1e10 self.max_fid_func_calls = 1e10