D-Wave Tabu Sampler

A dimod sampler_ that uses the MST2 multistart tabu search algorithm.

class TabuSampler[source]

A tabu-search sampler.

Examples

This example solves a two-variable Ising model.

>>> from tabu import TabuSampler
>>> samples = TabuSampler().sample_ising({'a': -0.5, 'b': 1.0}, {'ab': -1})
>>> list(samples.data()) # doctest: +SKIP
[Sample(sample={'a': -1, 'b': -1}, energy=-1.5, num_occurrences=1)]
>>> samples.first.energy
-1.5
sample(bqm, initial_states=None, initial_states_generator='random', num_reads=None, tenure=None, timeout=20, scale_factor=1, **kwargs)[source]

Run Tabu search on a given binary quadratic model.

Parameters:
  • bqm (BinaryQuadraticModel) – The binary quadratic model (BQM) to be sampled.
  • initial_states (SampleSet, optional, default=None) – One or more samples, each defining an initial state for all the problem variables. Initial states are given one per read, but if fewer than num_reads initial states are defined, additional values are generated as specified by initial_states_generator.
  • initial_states_generator (str, 'none'/'tile'/'random', optional, default='random') –

    Defines the expansion of initial_states if fewer than num_reads are specified:

    • ”none”:
      If the number of initial states specified is smaller than num_reads, raises ValueError.
    • ”tile”:
      Reuses the specified initial states if fewer than num_reads or truncates if greater.
    • ”random”:
      Expands the specified initial states with randomly generated states if fewer than num_reads or truncates if greater.
  • num_reads (int, optional, default=len(initial_states) or 1) – Number of reads. Each read is generated by one run of the tabu algorithm. If num_reads is not explicitly given, it is selected to match the number of initial states given. If initial states are not provided, only one read is performed.
  • tenure (int, optional) – Tabu tenure, which is the length of the tabu list, or number of recently explored solutions kept in memory. Default is a quarter of the number of problem variables up to a maximum value of 20.
  • timeout (int, optional) – Total running time in milliseconds.
  • scale_factor (number, optional) – Scaling factor for linear and quadratic biases in the BQM. Internally, the BQM is converted to a QUBO matrix, and elements are stored as long ints using internal_q = long int (q * scale_factor).
  • init_solution (SampleSet, optional) – Deprecated. Alias for initial_states.
Returns:

A dimod SampleSet object.
Return type:

SampleSet

Examples

This example samples a simple two-variable Ising model.

>>> import dimod
>>> bqm = dimod.BQM.from_ising({}, {'ab': 1})

>>> import tabu
>>> sampler = tabu.TabuSampler()

>>> samples = sampler.sample(bqm)
>>> samples.record[0].energy
-1.0