# -*- 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