hybrid.utils.bqm_induced_by¶
- bqm_induced_by(bqm, variables, sample)[source]¶
Induce a binary quadratic model by fixing values of boundary variables.
The function is optimized for
len(variables) << len(bqm)
, that is, for fixing the majority of variables.- Parameters
bqm (
dimod.BinaryQuadraticModel
) – Binary quadratic model (BQM).variables (list/set) – Subset of variables to keep in the reduced BQM, typically a subgraph.
sample (dict/list) – Mapping of variable labels to values or a list when labels are sequential integers. Values are required only for boundary variables, that is, for variables with interactions with variables (having edges with non-zero quadratic biases connected to the subgraph).
- Returns
A BQM induced by fixing values of those variables adjacent to its subset of variables and setting the energy offset to zero.
- Return type
dimod.BinaryQuadraticModel
Examples
This example induces a 2-variable BQM from a 6-variable path graph—the subset of nodes 2 and 3 of nodes 0 to 5—by fixing values of boundary variables 1 and 4.
>>> import dimod >>> import networkx as nx >>> bqm = dimod.BinaryQuadraticModel({}, ... {edge: edge[0] + 0.5 for edge in set(nx.path_graph(6).edges)}, 0, 'BINARY') >>> sample = {1: 3, 4: 3} >>> len(bqm_induced_by(bqm, [2, 3], sample)) 2