Mixed Integer (Linear) Programming Generation¶

generate_bqm
(graph, table, decision, linear_energy_ranges=None, quadratic_energy_ranges=None, min_classical_gap=2, precision=7, max_decision=8, max_variables=10, return_auxiliary=False)[source]¶ Get a binary quadratic model with specific ground states.
Parameters:  graph (
Graph
) – Defines the structure of the generated binary quadratic model.  table (iterable) – Iterable of valid configurations (of spinvalues). Each configuration is a tuple of variable assignments ordered by decision.
 decision (list/tuple) – The variables in the binary quadratic model which have specified configurations.
 linear_energy_ranges (dict, optional) – Dict of the form {v: (min, max, …} where min and max are the range of values allowed to v. The default range is [2, 2].
 quadratic_energy_ranges (dict, optional) – Dict of the form {(u, v): (min, max), …} where min and max are the range of values allowed to (u, v). The default range is [1, 1].
 min_classical_gap (float) – The minimum energy gap between the highest feasible state and the lowest infeasible state.
 precision (int, optional, default=7) – Values returned by the optimization solver are rounded to precision digits of precision.
 max_decision (int, optional, default=4) – Maximum number of decision variables allowed. The algorithm is valid for arbitrary sizes of problem but can be extremely slow.
 max_variables (int, optional, default=4) – Maximum number of variables allowed. The algorithm is valid for arbitrary sizes of problem but can be extremely slow.
 return_auxiliary (bool, optional, False) – If True, the auxiliary configurations are returned for each configuration in table.
Returns: dimod.BinaryQuadraticModel
: The binary quadratic model.float: The classical gap.
If return_auxiliary is True:
dimod.BinaryQuadraticModel
: The binary quadratic model.float: The classical gap.
dict: The auxiliary configurations, keyed on the configurations in table.
Return type: If return_auxiliary is False
Raises: ImpossiblePenaltyModel
– If the penalty model cannot be built. Normally due to a nonzero infeasible gap. graph (