# Introduction¶

Samplers are processes that sample from low energy states of a problem’s objective function. A binary quadratic model (BQM) sampler samples from low energy states in models such as those defined by an Ising equation or a Quadratic Unconstrained Binary Optimization (QUBO) problem and returns an iterable of samples, in order of increasing energy. A dimod sampler provides ‘sample_qubo’ and ‘sample_ising’ methods as well as the generic BQM sampler method.

The TabuSampler sampler implements the MST2 multistart tabu search algorithm for quadratic unconstrained binary optimization (QUBO) problems with a dimod Python wrapper.

For a description of the tabu search algorithm, see tabu search.

## Example¶

This example solves a two-variable Ising model.

>>> from tabu import TabuSampler
>>> response = TabuSampler().sample_ising({'a': -0.5, 'b': 1.0}, {('a', 'b'): -1})