# 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 dimod
tool for testing your code locally, is the `ExactSolver()`

that calculates the energy of all
possible samples for a given problem. Such a sampler can solve a small three-variable
problem like the AND gate of the Formulate Your Problem for a Quantum Computer section,

```
>>> import dimod
>>> bqm = dimod.BinaryQuadraticModel({'x1': 0.0, 'x2': 0.0, 'y1': 6.0},
... {('x2', 'x1'): 2.0, ('y1', 'x1'): -4.0, ('y1', 'x2'): -4.0},
... 0, 'BINARY')
```

as follows:

```
>>> from dimod.reference.samplers import ExactSolver
>>> sampler = ExactSolver()
>>> response = sampler.sample(bqm)
>>> print(response) # doctest: +SKIP
x1 x2 y1 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 dwave_neal
simulated annealing sampler. For such a small problem, `num_reads=10`

most likely
finds the optimal solution.

```
>>> import neal
>>> solver = neal.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
```