def min_time_limit(self, cqm: dimod.ConstrainedQuadraticModel) -> float:
"""Return the minimum `time_limit` accepted for the given problem."""
# todo: remove the hard-coded defaults
num_variables_multiplier = self.properties.get('num_variables_multiplier', 1.57e-04)
num_biases_multiplier = self.properties.get('num_biases_multiplier', 4.65e-06)
num_constraints_multiplier = self.properties.get('num_constraints_multiplier', 6.44e-09)
minimum_time_limit = self.properties['minimum_time_limit_s']
num_variables = len(cqm.variables)
num_constraints = len(cqm.constraints)
num_biases = cqm.num_biases()
return max(
num_variables_multiplier * num_variables +
num_biases_multiplier * num_biases +
num_constraints_multiplier * num_variables * num_constraints,
minimum_time_limit
)
def sample_cqm(self,
cqm: dimod.ConstrainedQuadraticModel,
time_limit: Optional[float] = None,
**kwargs):
"""Sample from the specified constrained quadratic model.
Args:
cqm (:obj:`dimod.ConstrainedQuadraticModel`):
Constrained quadratic model (CQM).
time_limit (int, optional):
Maximum run time, in seconds, to allow the solver to work on the
problem. Must be at least the minimum required for the problem,
which is calculated and set by default.
:meth:`~dwave.system.samplers.LeapHybridCQMSampler.min_time_limit`
calculates (and describes) the minimum time for your problem.
**kwargs:
Optional keyword arguments for the solver, specified in
:attr:`~dwave.system.samplers.LeapHybridCQMSampler.parameters`.
Returns:
:class:`~dimod.SampleSet`: Sample set constructed from a (non-blocking)
:class:`~concurrent.futures.Future`-like object.
Examples:
See the example in :class:`LeapHybridCQMSampler`.
"""
if not isinstance(cqm, dimod.ConstrainedQuadraticModel):
raise TypeError(
"first argument 'cqm' must be a ConstrainedQuadraticModel, "
f"recieved {type(cqm).__name__}")
if time_limit is None:
time_limit = self.min_time_limit(cqm)
elif time_limit < self.min_time_limit(cqm):
raise ValueError("the minimum time limit for this problem is "
f"{self.min_time_limit(cqm)} seconds "
f"({time_limit}s provided), "
"see .min_time_limit method")
contact_sales_str = "Contact D-Wave at [email protected] if your " + \
"application requires scale or performance that " + \
"exceeds the currently advertised capabilities of " + \
"this hybrid solver."
if len(cqm.constraints
) > self.properties['maximum_number_of_constraints']:
raise ValueError(
"constrained quadratic model must have "
f"{self.properties['maximum_number_of_constraints']} or fewer "
f"constraints; given model has {len(cqm.constraints)}. "
f"{contact_sales_str}")
if len(cqm.variables) > self.properties['maximum_number_of_variables']:
raise ValueError(
"constrained quadratic model must have "
f"{self.properties['maximum_number_of_variables']} or fewer "
f"variables; given model has {len(cqm.variables)}. "
f"{contact_sales_str}")
if cqm.num_biases() > self.properties['maximum_number_of_biases']:
raise ValueError(
"constrained quadratic model must have "
f"{self.properties['maximum_number_of_biases']} or fewer "
f"biases; given model has {cqm.num_biases()}. "
f"{contact_sales_str}")
if cqm.num_quadratic_variables(
) > self.properties['maximum_number_of_quadratic_variables']:
raise ValueError(
"constrained quadratic model must have "
f"{self.properties['maximum_number_of_quadratic_variables']} "
"or fewer variables with at least one quadratic bias across "
"all constraints; given model has "
f"{cqm.num_quadratic_variables()}. "
f"{contact_sales_str}")
return self.solver.sample_cqm(cqm, time_limit=time_limit,
**kwargs).sampleset