.. _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