.. _using_cpu:
=================
Classical Solvers
=================
You might use a classical solver while developing your code or on a small version of
your problem to verify your code.
To solve a problem classically on your local machine, you configure a classical solver,
either one of those included in the Ocean tools or your own.
Examples
~~~~~~~~
Among several samplers provided in the :doc:`dimod `
tool for testing your code locally, is the :class:`~dimod.reference.samplers.ExactSolver`
that calculates the energy of all
possible samples for a given problem. Such a sampler can solve a small three-variable
problem such as a BQM representing a Boolean AND gate (see also the
:ref:`formulating_bqm` section) as follows:
>>> from dimod.generators import and_gate
>>> from dimod import ExactSolver
>>> bqm = and_gate('in1', 'in2', 'out')
>>> sampler = ExactSolver()
>>> sampleset = sampler.sample(bqm)
>>> print(sampleset) # doctest: +SKIP
in1 in2 out energy num_oc.
0 0 0 0 0.0 1
1 1 0 0 0.0 1
3 0 1 0 0.0 1
5 1 1 1 0.0 1
2 1 1 0 2.0 1
4 0 1 1 2.0 1
6 1 0 1 2.0 1
7 0 0 1 6.0 1
['BINARY', 8 rows, 8 samples, 3 variables]
Note that the first four samples are the valid states of the AND gate and have
lower values than the second four, which represent invalid states.
If you use a classical solver running locally on your CPU, a single sample might provide
the optimal solution.
This example solves a two-variable problem using the :ref:`dwave-samplers `
simulated annealing sampler. For such a small problem, :code:`num_reads=10` most likely
finds the optimal solution.
>>> from dwave.samplers import SimulatedAnnealingSampler
>>> solver = SimulatedAnnealingSampler()
>>> sampleset = solver.sample_ising({'a': -0.5, 'b': 1.0}, {('a', 'b'): -1}, num_reads=10)
>>> sampleset.first.sample["a"] == sampleset.first.sample["b"] == -1
True