def test_linear_equality_to_penalty_decode(self):
""" Test decode func of LinearEqualityToPenalty"""
qprog = QuadraticProgram()
qprog.binary_var('x')
qprog.binary_var('y')
qprog.binary_var('z')
qprog.maximize(linear={'x': 3, 'y': 1, 'z': 1})
qprog.linear_constraint(linear={
'x': 1,
'y': 1,
'z': 1
},
sense='EQ',
rhs=2,
name='xyz_eq')
lineq2penalty = LinearEqualityToPenalty()
qubo = lineq2penalty.convert(qprog)
exact_mes = NumPyMinimumEigensolver()
exact = MinimumEigenOptimizer(exact_mes)
result = exact.solve(qubo)
new_x = lineq2penalty.interpret(result.x)
np.testing.assert_array_almost_equal(new_x, [1, 1, 0])
infeasible_x = lineq2penalty.interpret([1, 1, 1])
np.testing.assert_array_almost_equal(infeasible_x, [1, 1, 1])
def test_linear_equality_to_penalty_decode(self):
""" Test decode func of LinearEqualityToPenalty"""
qprog = QuadraticProgram()
qprog.binary_var('x')
qprog.binary_var('y')
qprog.binary_var('z')
qprog.maximize(linear={'x': 3, 'y': 1, 'z': 1})
qprog.linear_constraint(linear={'x': 1, 'y': 1, 'z': 1}, sense='EQ', rhs=2, name='xyz_eq')
lineq2penalty = LinearEqualityToPenalty()
qubo = lineq2penalty.convert(qprog)
exact_mes = NumPyMinimumEigensolver()
exact = MinimumEigenOptimizer(exact_mes)
result = exact.solve(qubo)
decoded_result = lineq2penalty.interpret(result)
self.assertEqual(decoded_result.fval, 4)
np.testing.assert_array_almost_equal(decoded_result.x, [1, 1, 0])
self.assertEqual(decoded_result.status, OptimizationResultStatus.SUCCESS)
self.assertListEqual(decoded_result.variable_names, ['x', 'y', 'z'])
self.assertDictEqual(decoded_result.variables_dict, {'x': 1.0, 'y': 1.0, 'z': 0.0})
infeasible_result = OptimizationResult(x=[1, 1, 1], fval=0, variables=qprog.variables,
status=OptimizationResultStatus.SUCCESS)
decoded_infeasible_result = lineq2penalty.interpret(infeasible_result)
self.assertEqual(decoded_infeasible_result.fval, 5)
np.testing.assert_array_almost_equal(decoded_infeasible_result.x, [1, 1, 1])
self.assertEqual(decoded_infeasible_result.status, OptimizationResultStatus.INFEASIBLE)
self.assertListEqual(infeasible_result.variable_names, ['x', 'y', 'z'])
self.assertDictEqual(infeasible_result.variables_dict, {'x': 1.0, 'y': 1.0, 'z': 1.0})
def test_penalize_integer(self):
""" Test PenalizeLinearEqualityConstraints with integer variables """
op = QuadraticProgram()
for i in range(3):
op.integer_var(name='x{}'.format(i), lowerbound=-3, upperbound=3)
# Linear constraints
linear_constraint = {'x0': 1, 'x1': 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, 'x0x1')
linear_constraint = {'x1': 1, 'x2': -1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 2, 'x1x2')
linear_constraint = {'x0': 1, 'x2': -1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, 'x0x2')
op.minimize(constant=3, linear={'x0': 1}, quadratic={('x1', 'x2'): 2})
self.assertEqual(op.get_num_linear_constraints(), 3)
conv = LinearEqualityToPenalty()
op2 = conv.convert(op)
self.assertEqual(op2.get_num_linear_constraints(), 0)
result = OptimizationResult(x=[0, 1, -1],
fval=1,
variables=op2.variables)
new_result = conv.interpret(result)
self.assertAlmostEqual(new_result.fval, 1)
self.assertEqual(new_result.status, OptimizationResultStatus.SUCCESS)
np.testing.assert_array_almost_equal(new_result.x, [0, 1, -1])
self.assertListEqual(result.variable_names, ['x0', 'x1', 'x2'])
self.assertDictEqual(result.variables_dict, {
'x0': 0,
'x1': 1,
'x2': -1
})
def test_penalize_binary(self):
""" Test PenalizeLinearEqualityConstraints with binary variables """
op = QuadraticProgram()
for i in range(3):
op.binary_var(name='x{}'.format(i))
# Linear constraints
linear_constraint = {'x0': 1, 'x1': 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, 'x0x1')
linear_constraint = {'x1': 1, 'x2': -1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 2, 'x1x2')
linear_constraint = {'x0': 1, 'x2': 3}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 2, 'x0x2')
self.assertEqual(op.get_num_linear_constraints(), 3)
conv = LinearEqualityToPenalty()
op2 = conv.convert(op)
self.assertEqual(op2.get_num_linear_constraints(), 0)
result = OptimizationResult(x=np.arange(3),
fval=0,
variables=op2.variables)
new_result = conv.interpret(result)
self.assertEqual(new_result.status,
OptimizationResultStatus.INFEASIBLE)
np.testing.assert_array_almost_equal(new_result.x, np.arange(3))
self.assertListEqual(result.variable_names, ['x0', 'x1', 'x2'])
self.assertDictEqual(result.variables_dict, {
'x0': 0,
'x1': 1,
'x2': 2
})
def test_penalize_binary(self):
""" Test PenalizeLinearEqualityConstraints with binary variables """
op = QuadraticProgram()
for i in range(3):
op.binary_var(name='x{}'.format(i))
# Linear constraints
linear_constraint = {'x0': 1, 'x1': 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, 'x0x1')
linear_constraint = {'x1': 1, 'x2': -1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 2, 'x1x2')
linear_constraint = {'x0': 1, 'x2': 3}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 2, 'x0x2')
self.assertEqual(op.get_num_linear_constraints(), 3)
conv = LinearEqualityToPenalty()
op2 = conv.convert(op)
self.assertEqual(op2.get_num_linear_constraints(), 0)
new_x = conv.interpret(np.arange(3))
np.testing.assert_array_almost_equal(new_x, np.arange(3))
def test_penalize_integer(self):
""" Test PenalizeLinearEqualityConstraints with integer variables """
op = QuadraticProgram()
for i in range(3):
op.integer_var(name='x{}'.format(i), lowerbound=-3, upperbound=3)
# Linear constraints
linear_constraint = {'x0': 1, 'x1': 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, 'x0x1')
linear_constraint = {'x1': 1, 'x2': -1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 2, 'x1x2')
linear_constraint = {'x0': 1, 'x2': -1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, 'x0x2')
op.minimize(constant=3, linear={'x0': 1}, quadratic={('x1', 'x2'): 2})
self.assertEqual(op.get_num_linear_constraints(), 3)
conv = LinearEqualityToPenalty()
op2 = conv.convert(op)
self.assertEqual(op2.get_num_linear_constraints(), 0)
new_x = conv.interpret([0, 1, -1])
np.testing.assert_array_almost_equal(new_x, [0, 1, -1])
