def test_recursive_min_eigen_optimizer(self):
"""Test the recursive minimum eigen optimizer."""
filename = "op_ip1.lp"
# get minimum eigen solver
min_eigen_solver = NumPyMinimumEigensolver()
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
min_eigen_optimizer, min_num_vars=4)
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(filename, "algorithms/resources")
problem.read_from_lp_file(lp_file)
# solve problem with cplex
cplex = CplexOptimizer()
cplex_result = cplex.solve(problem)
# solve problem
result = recursive_min_eigen_optimizer.solve(problem)
# analyze results
np.testing.assert_array_almost_equal(cplex_result.x, result.x, 4)
self.assertAlmostEqual(cplex_result.fval, result.fval)
def test_recursive_min_eigen_optimizer(self):
"""Test the recursive minimum eigen optimizer."""
try:
filename = 'op_ip1.lp'
# get minimum eigen solver
min_eigen_solver = NumPyMinimumEigensolver()
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
min_eigen_optimizer, min_num_vars=4)
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(path.join('resources', filename))
problem.read_from_lp_file(lp_file)
# solve problem with cplex
cplex = CplexOptimizer()
cplex_result = cplex.solve(problem)
# solve problem
result = recursive_min_eigen_optimizer.solve(problem)
# analyze results
self.assertAlmostEqual(cplex_result.fval, result.fval)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_recursive_warm_qaoa(self):
"""Test the recursive optimizer with warm start qaoa."""
algorithm_globals.random_seed = 12345
backend = BasicAer.get_backend("statevector_simulator")
qaoa = QAOA(quantum_instance=backend, reps=1)
warm_qaoa = WarmStartQAOAOptimizer(pre_solver=SlsqpOptimizer(),
relax_for_pre_solver=True,
qaoa=qaoa)
recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
warm_qaoa, min_num_vars=4)
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path("op_ip1.lp", "algorithms/resources")
problem.read_from_lp_file(lp_file)
# solve problem with cplex
cplex = CplexOptimizer(cplex_parameters={
"threads": 1,
"randomseed": 1
})
cplex_result = cplex.solve(problem)
# solve problem
result = recursive_min_eigen_optimizer.solve(problem)
# analyze results
np.testing.assert_array_almost_equal(cplex_result.x, result.x, 4)
self.assertAlmostEqual(cplex_result.fval, result.fval)
def test_min_eigen_optimizer(self, config):
"""Min Eigen Optimizer Test"""
try:
# unpack configuration
min_eigen_solver_name, backend, filename = config
# get minimum eigen solver
min_eigen_solver = self.min_eigen_solvers[min_eigen_solver_name]
if backend:
min_eigen_solver.quantum_instance = BasicAer.get_backend(backend)
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(filename, "algorithms/resources")
problem.read_from_lp_file(lp_file)
# solve problem with cplex
cplex = CplexOptimizer()
cplex_result = cplex.solve(problem)
# solve problem
result = min_eigen_optimizer.solve(problem)
self.assertIsNotNone(result)
# analyze results
self.assertAlmostEqual(cplex_result.fval, result.fval)
# check that eigensolver result is present
self.assertIsNotNone(result.min_eigen_solver_result)
except RuntimeError as ex:
self.fail(str(ex))
class TestCplexOptimizer(QiskitOptimizationTestCase):
"""Cplex Optimizer Tests."""
def setUp(self):
super().setUp()
try:
self.cplex_optimizer = CplexOptimizer(disp=False)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
@data(
('op_ip1.lp', [0, 2], 6),
('op_mip1.lp', [1, 1, 0], 6),
('op_lp1.lp', [0.25, 1.75], 5.8750)
)
def test_cplex_optimizer(self, config):
""" Cplex Optimizer Test """
# unpack configuration
filename, x, fval = config
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(path.join('resources', filename))
problem.read_from_lp_file(lp_file)
# solve problem with cplex
result = self.cplex_optimizer.solve(problem)
# analyze results
self.assertAlmostEqual(result.fval, fval)
for i in range(problem.get_num_vars()):
self.assertAlmostEqual(result.x[i], x[i])
def test_cplex_optimizer_no_solution(self, config):
"""Cplex Optimizer Test if no solution is found"""
cplex_optimizer = CplexOptimizer(disp=False, cplex_parameters={"dettimelimit": 0})
# unpack configuration
filename, _, _ = config
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(filename, "algorithms/resources")
problem.read_from_lp_file(lp_file)
# solve problem with cplex
result = cplex_optimizer.solve(problem)
np.testing.assert_array_almost_equal(result.x, np.zeros(problem.get_num_vars()))
self.assertEqual(result.status, OptimizationResultStatus.FAILURE)
self.assertEqual(result.raw_results, None)
def test_cplex_optimizer(self, config):
"""Cplex Optimizer Test"""
cplex_optimizer = CplexOptimizer(disp=False)
# unpack configuration
filename, x, fval = config
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(filename, "algorithms/resources")
problem.read_from_lp_file(lp_file)
# solve problem with cplex
result = cplex_optimizer.solve(problem)
# analyze results
self.assertAlmostEqual(result.fval, fval)
for i in range(problem.get_num_vars()):
self.assertAlmostEqual(result.x[i], x[i])