dimod.binary.BinaryQuadraticModel.reduce_neighborhood#

BinaryQuadraticModel.reduce_neighborhood(v: Hashable, function: Callable, initializer: float | floating | integer | None = None) → Any[source]#

Apply function of two arguments cumulatively to the quadratic biases associated with a single variable.

See functools.reduce() for information on reducing an iterable to a single value.

Parameters:

  • v – Variable in the binary quadratic model.

  • function – Function of two arguments to apply cumulatively to quadratic biases.

  • initializer – A value to precede the linear biases.

Examples

>>> from operator import add
>>> bqm = dimod.BinaryQuadraticModel({0: 10},
...                                  {(0, 1): 1, (0, 2): 2, (1, 2): 5},
...                                  0, "BINARY")
>>> bqm.reduce_neighborhood(0, add, 0.25)
3.25