def test_hybrid(self):
import dimod
import hybrid
bqm = dimod.BinaryQuadraticModel({}, {'ab': 1, 'bc': -1, 'ca': 1}, 0, dimod.SPIN)
workflow = hybrid.Loop(hybrid.Race(
hybrid.InterruptableTabuSampler(),
hybrid.EnergyImpactDecomposer(size=2)
| hybrid.SimulatedAnnealingSubproblemSampler()
| hybrid.SplatComposer()
) | hybrid.ArgMin(), convergence=3)
result = workflow.run(hybrid.State.from_problem(bqm)).result()
self.assertEqual(result.samples.first.energy, -3.0)
import dimod
import hybrid
# load a problem
problem = sys.argv[1]
with open(problem) as fp:
bqm = dimod.BinaryQuadraticModel.from_coo(fp)
# construct a workflow that races Simulated Annealing against SA/Tabu on a subproblem
iteration = hybrid.Race(
hybrid.SimulatedAnnealingProblemSampler(),
hybrid.EnergyImpactDecomposer(size=50)
| hybrid.RacingBranches(
hybrid.SimulatedAnnealingSubproblemSampler(num_sweeps=1000),
hybrid.TabuSubproblemSampler(tenure=20, timeout=10))
| hybrid.ArgMin('subsamples.first.energy')
| hybrid.SplatComposer()
) | hybrid.ArgMin('samples.first.energy')
main = hybrid.Loop(iteration, max_iter=10, convergence=3)
# run the workflow
init_state = hybrid.State.from_sample(hybrid.utils.min_sample(bqm), bqm)
solution = main.run(init_state).result()
# show results
print("""
Solution:
energy={s.samples.first.energy}