def initialize(self, current_time):
DistributedProblem.initialize(self, current_time)
self.start_time = current_time
self._objectives = []
self._stacked_x = [self.get_stacked_x_var_it()] # init value
self.x_var = []
for _ in range(self.options['init_iter']):
self.solve(self.start_time, 0.0)
def initialize(self, current_time):
DistributedProblem.initialize(self, current_time)
self.start_time = current_time
self._objectives = []
self._stacked_x = [self.get_stacked_x_var_it()] # init value
self.x_var = []
for _ in range(self.options['init_iter']):
self.solve(self.start_time, 0.0)
def set_default_options(self):
DistributedProblem.set_default_options(self)
self.options.update({
'max_iter': None,
'max_iter_per_update': 1,
'rho': 2.,
'init_iter': 5
})
def stop_criterium(self, current_time, update_time):
if self.options['max_iter']:
if self.iteration > self.options['max_iter']:
return True
else:
return DistributedProblem.stop_criterium(self, current_time,
update_time)
def __init__(self, problems, options):
DistributedProblem.__init__(self, problems, ADMM, options)
def final(self):
DistributedProblem.final(self)
def stop_criterium(self, current_time, update_time):
if self.options['max_iter']:
if self.iteration > self.options['max_iter']:
return True
else:
return DistributedProblem.stop_criterium(self, current_time, update_time)
def set_default_options(self):
DistributedProblem.set_default_options(self)
self.options.update({'max_iter': None, 'max_iter_per_update': 1,
'rho': 2., 'init_iter': 5})
def __init__(self, fleet, environment, problems, updater_type, options):
DistributedProblem.__init__(
self, fleet, environment, problems, updater_type, options)
def final(self):
DistributedProblem.final(self)
def __init__(self, fleet, environment, problems, updater_type, options):
DistributedProblem.__init__(self, fleet, environment, problems,
updater_type, options)