def run(self):
model = mp_model.build(self._args.file)
decomposer = decomposers.get_instance_of(self._args.decomposer_id, model)
try:
decomposer.decompose()
except Exception, e:
logging.exception('Cannot decompose the model.')
return
def __init__(self, model, optimization_parameters, pipeline):
super(LocalEliminationDriver, self).__init__()
if not model.is_binary():
raise TypeError("Optimization can be applied only to binary integer "
"linear programming problems.")
self._pipeline = pipeline
self._optimization_params = optimization_parameters
self._driver_params = optimization_parameters.driver.local_elimination_driver_parameters
self._decomposer = decomposers.get_instance_of(self._driver_params.decomposer, model)
logging.info("Decomposer: %s" % self._decomposer.__class__.__name__)
self._solution_table = solution_tables.get_instance_of(self._driver_params.solution_table)
if not self._solution_table:
logging.info("Cannot create solution table: %d", self._driver_params.solution_table)
return
logging.info("Relaxation backend solvers are %s", self._driver_params.relaxation_backend_solvers)
self._solver_id_stack = list(self._driver_params.relaxation_backend_solvers)
self._solver_id_stack.append(optimization_parameters.driver.default_backend_solver)
self._active_contexts = collections.OrderedDict()
self._frozen_contexts = {}
