def _execute_single_test(problem, baseline, solver=None, comm=None, **kwds):
if solver is None:
solver = Solver(comm=comm)
else:
assert comm is None
if solver.comm is not None:
if solver.comm.rank == 0:
pass
elif solver.comm.rank == 1:
pass
elif solver.comm.rank == 3:
pass
orig = pybnb.node.Node()
problem.save_state(orig)
results = solver.solve(problem, **kwds)
current = pybnb.node.Node()
problem.save_state(current)
assert len(current.state) == len(orig.state)
for i in range(len(current.state)):
assert current.state[i] == orig.state[i]
assert len(vars(results)) > 0
assert len(vars(results)) == len(vars(baseline))
for name in sorted(sorted(list(vars(results).keys()))):
if getattr(baseline, name) is _ignore_value_:
continue
if (name == 'nodes') and \
(solver.comm is not None) and \
(solver.comm.size > 2):
pass
else:
assert getattr(results, name) == getattr(baseline, name), \
("value for '"+str(name)+"' ("+
str(getattr(results, name))+") does "
"not match baseline ("+
str(getattr(baseline, name))+")")
if solver.is_dispatcher:
q = solver.save_dispatcher_queue()
assert len(q) == 2
assert q.next_tree_id >= 0
assert len(q.nodes) == solver._disp.queue.size()
class NestedSolver(_ProblemWithSolveInfoCollection):
"""A class for creating a nested branch-and-bound solve
strategy. An instance of this class (wrapped around a
standard problem) can be passed to the solver as the
problem argument.
Parameters
----------
problem : :class:`pybnb.Problem <pybnb.problem.Problem>`
An object defining a branch-and-bound problem.
node_limit : int, optional
The same as the standard solver option, but applied
to the nested solver to limit the number of nodes to
explore when processing a work item. (default: None)
time_limit : float, optional
The same as the standard solver option, but applied
to the nested solver to limit the amount of time
spent processing a work item. (default: 5)
queue_limit : int, optional
The same as the standard solver option, but applied
to the nested solver to limit the size of the queue.
(default: None)
track_bound : bool, optional
The same as the standard solver option, but applied
to the nested solver control bound tracking.
(default: True)
queue_strategy : :class:`QueueStrategy <pybnb.common.QueueStrategy>` or tuple
The same as the standard solver option, but applied
to the nested solver to control the queue strategy
used when processing a work item. (default: 'depth')
"""
def __init__(self,
problem,
node_limit=None,
time_limit=5,
queue_limit=None,
track_bound=True,
queue_strategy="depth"):
# we import from pybnb.solver here to avoid a
# circular import reference
from pybnb.solver import Solver
self._problem = problem
self._node_limit = node_limit
self._time_limit = time_limit
self._queue_limit = queue_limit
self._track_bound = track_bound
self._queue_strategy = queue_strategy
self._solver = Solver(comm=None)
self._log = logging.Logger(None, level=logging.WARNING)
self._convergence_checker = None
self._best_objective = None
self._best_node = None
self._current_node = None
self._results = None
self._queue = None
super(NestedSolver, self).__init__()
def _initialize(self, dispatcher, best_objective, disable_objective_call):
assert best_objective is not None
self._best_objective = best_objective
self._disable_objective_call = disable_objective_call
self._log.addHandler(_RedirectHandler(dispatcher))
def _solve(self):
bound_stop = None
if not math.isinf(self._best_objective):
bound_stop = self._best_objective
# shallow copy
root = copy.copy(self._current_node)
init_queue = DispatcherQueueData(nodes=[root],
worst_terminal_bound=None,
sense=self._convergence_checker.sense)
self._results = self._solver.solve(
self._problem,
best_objective=self._best_objective,
best_node=self._best_node,
bound_stop=bound_stop,
initialize_queue=init_queue,
log=self._log,
absolute_gap=\
self._convergence_checker.absolute_gap,
relative_gap=\
self._convergence_checker.relative_gap,
scale_function=\
self._convergence_checker.scale_function,
queue_tolerance=\
self._convergence_checker.queue_tolerance,
branch_tolerance=\
self._convergence_checker.branch_tolerance,
comparison_tolerance=\
self._convergence_checker.comparison_tolerance,
objective_stop=\
self._convergence_checker.objective_stop,
disable_objective_call=self._disable_objective_call,
node_limit=self._node_limit,
time_limit=self._time_limit,
queue_limit=self._queue_limit,
track_bound=self._track_bound,
queue_strategy=self._queue_strategy,
disable_signal_handlers=True)
self._queue = self._solver.save_dispatcher_queue()
self._solve_info.add_from(self._solver._global_solve_info)
#
# Ducktype a partial Problem interface
#
def objective(self): #pragma:nocover
"""The solver does not call this method when it sees the
problem implements a nested solve."""
raise NotImplementedError()
def bound(self): #pragma:nocover
"""The solver does not call this method when it sees the
problem implements a nested solve."""
raise NotImplementedError()
def branch(self): #pragma:nocover
"""The solver does not call this method when it sees the
problem implements a nested solve."""
raise NotImplementedError()
def sense(self):
"""Calls the sense() method on the user-provided
problem."""
return self._problem.sense()
def save_state(self, node):
"""Calls the save_state() method on the user-provided
problem."""
self._problem.save_state(node)
def load_state(self, node):
"""Calls the load_state() method on the user-provided
problem and prepares for a nested solve."""
self._problem.load_state(node)
self._results = None
self._children = None
self._current_node = node
self._solve_found_best_node = False
def notify_solve_begins(self, comm, worker_comm, convergence_checker):
"""Calls the notify_solve_begins() method on the
user-provided problem and prepares for a solve."""
self._best_objective = None
self._best_node = None
self._convergence_checker = convergence_checker
self._current_node = None
self._results = None
self._queue = None
self._problem.notify_solve_begins(comm, worker_comm,
convergence_checker)
def notify_new_best_node(self, node, current):
"""Calls the notify_new_best_node() method on the
user-provided problem and stores the best node for
use in the next nested solve."""
self._best_objective = self._convergence_checker.\
best_objective(self._best_objective,
node.objective)
self._best_node = node
self._problem.notify_new_best_node(node, current)
def notify_solve_finished(self, comm, worker_comm, results):
"""Calls the notify_solve_finished() method on the
user-provided problem and does some final
cleanup."""
while len(self._log.handlers) > 0:
self._log.removeHandler(self._log.handlers[0])
self._problem.notify_solve_finished(comm, worker_comm, results)
def _test_initialize_queue(comm):
solver = Solver(comm=comm)
# no initial queue
for sense in (minimize, maximize):
problem = DummyProblem(sense)
results = solver.solve(problem, queue_limit=0)
assert results.solution_status == "unknown"
assert results.termination_condition == "queue_limit"
assert results.objective == (inf if (sense == minimize) else -inf)
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap is None
assert results.relative_gap is None
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
assert solver._local_solve_info.explored_nodes_count == 0
problem = DummyProblem(sense)
results = solver.solve(problem)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node is not None
assert results.best_node.objective == results.objective
assert results.best_node.tree_depth == 0
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
problem = DummyProblem(sense)
results = solver.solve(
problem, best_objective=(1 if (sense == minimize) else -1)
)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node is not None
assert results.best_node.objective == results.objective
assert results.best_node.tree_depth == 0
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
problem = DummyProblem(sense)
results = solver.solve(
problem,
best_objective=(1 if (sense == minimize) else -1),
disable_objective_call=True,
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == (1 if (sense == minimize) else -1)
assert results.bound == 0
assert results.absolute_gap == 1
assert results.relative_gap == 1
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
best_node_ = Node()
best_node_.objective = 1 if (sense == minimize) else -1
best_node_._uuid = "abcd"
problem = DummyProblem(sense)
results = solver.solve(
problem, best_node=best_node_, disable_objective_call=True
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == best_node_.objective
assert results.bound == 0
assert results.absolute_gap == 1
assert results.relative_gap == 1
assert results.nodes == 1
assert results.best_node._uuid == best_node_._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is best_node_
assert results.best_node.objective == results.objective
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert not problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
best_node_ = Node()
best_node_.objective = 1 if (sense == minimize) else -1
best_node_._uuid = "abcd"
problem = DummyProblem(sense)
results = solver.solve(problem, best_node=best_node_)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node._uuid != best_node_._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is not best_node_
assert results.best_node.objective == results.objective
assert results.best_node.tree_depth == 0
if solver.is_worker:
assert problem._notify_new_best_node_call_count >= 1
assert problem._notify_new_best_node_args[0] is results.best_node
if problem._notify_new_best_node_call_count == 2:
assert problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_args[0]
else:
assert problem._notify_new_best_node_call_count == 0
# empty initial queue
queue = DispatcherQueueData(nodes=[], worst_terminal_bound=None, sense=minimize)
for sense in (minimize, maximize):
queue.sense = sense
problem = DummyProblem(sense)
results = solver.solve(problem, initialize_queue=queue)
assert results.solution_status == "unknown"
assert results.termination_condition == "queue_empty"
assert results.objective == (inf if (sense == minimize) else -inf)
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap is None
assert results.relative_gap is None
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
problem = DummyProblem(sense)
results = solver.solve(problem, initialize_queue=queue, best_objective=0)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == 0
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap == inf
assert results.relative_gap == inf
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=0,
disable_objective_call=True,
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == 0
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap == inf
assert results.relative_gap == inf
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
best_node_ = Node()
best_node_.objective = 1 if (sense == minimize) else -1
best_node_._uuid = "abcd"
problem = DummyProblem(sense)
results = solver.solve(
problem, initialize_queue=queue, best_objective=0, best_node=best_node_
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == 0
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap == inf
assert results.relative_gap == inf
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node._uuid == best_node_._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is best_node_
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
best_node_ = Node()
best_node_.objective = 1 if (sense == minimize) else -1
best_node_._uuid = "abcd"
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=(2 if (sense == minimize) else -2),
best_node=best_node_,
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == (1 if (sense == minimize) else -1)
assert results.bound == (-inf if (sense == minimize) else inf)
assert results.absolute_gap == inf
assert results.relative_gap == inf
assert results.nodes == 0
assert results.wall_time is not None
assert results.best_node._uuid == best_node_._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is best_node_
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
# non-empty initial queue
root = Node()
root._uuid = "abcd"
root.tree_depth = 0
root.objective = 0
root.bound = 0
queue = DispatcherQueueData(nodes=[root], worst_terminal_bound=None, sense=minimize)
orig_objective = queue.nodes[0].objective
for sense in (minimize, maximize):
queue.sense = sense
queue.nodes[0].objective = orig_objective
problem = DummyProblem(sense)
results = solver.solve(problem, initialize_queue=queue)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node._uuid == root._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is root
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
queue.nodes[0].objective = orig_objective
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=(1 if (sense == minimize) else -1),
)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node._uuid == root._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is root
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
queue.nodes[0].objective = inf if (sense == minimize) else -inf
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=(1 if (sense == minimize) else -1),
)
assert results.solution_status == "optimal"
assert results.termination_condition == "optimality"
assert results.objective == 0
assert results.bound == 0
assert results.absolute_gap == 0
assert results.relative_gap == 0
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node._uuid == root._uuid
if (comm is None) or (comm.size == 1):
assert results.best_node is root
if solver.is_worker:
assert problem._notify_new_best_node_call_count == 1
assert problem._notify_new_best_node_args[0] is results.best_node
if solver._local_solve_info.explored_nodes_count == 1:
assert problem._notify_new_best_node_args[1]
else:
assert solver._local_solve_info.explored_nodes_count == 0
assert not problem._notify_new_best_node_args[1]
else:
assert problem._notify_new_best_node_call_count == 0
queue.nodes[0].objective = inf if (sense == minimize) else -inf
problem = DummyProblem(sense)
results = solver.solve(
problem,
initialize_queue=queue,
best_objective=(1 if (sense == minimize) else -1),
disable_objective_call=True,
)
assert results.solution_status == "feasible"
assert results.termination_condition == "queue_empty"
assert results.objective == (1 if (sense == minimize) else -1)
assert results.bound == 0
assert results.absolute_gap == 1
assert results.relative_gap == 1
assert results.nodes == 1
assert results.wall_time is not None
assert results.best_node is None
assert problem._notify_new_best_node_call_count == 0
queue.nodes[0].objective = orig_objective