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_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])