def test_quadratic_program_element_when_loaded_from_source(self):
"""Test QuadraticProgramElement when QuadraticProgram is loaded from an external source"""
with self.subTest("from_ising"):
q_p = QuadraticProgram()
q_p.from_ising(PauliSumOp.from_list([("IZ", 1), ("ZZ", 2)]))
self.assertEqual(id(q_p.objective.quadratic_program), id(q_p))
for elem in q_p.variables:
self.assertEqual(id(elem.quadratic_program), id(q_p))
for elem in q_p.linear_constraints:
self.assertEqual(id(elem.quadratic_program), id(q_p))
for elem in q_p.quadratic_constraints:
self.assertEqual(id(elem.quadratic_program), id(q_p))
try:
lp_file = self.get_resource_path("test_quadratic_program.lp",
"problems/resources")
q_p = QuadraticProgram()
q_p.read_from_lp_file(lp_file)
self.assertEqual(id(q_p.objective.quadratic_program), id(q_p))
for elem in q_p.variables:
self.assertEqual(id(elem.quadratic_program), id(q_p))
for elem in q_p.linear_constraints:
self.assertEqual(id(elem.quadratic_program), id(q_p))
for elem in q_p.quadratic_constraints:
self.assertEqual(id(elem.quadratic_program), id(q_p))
except RuntimeError as ex:
self.fail(str(ex))
def test_maximize_to_minimize(self):
"""Test maximization to minimization conversion"""
op_max = QuadraticProgram()
op_min = QuadraticProgram()
for i in range(2):
op_max.binary_var(name="x{}".format(i))
op_min.binary_var(name="x{}".format(i))
op_max.integer_var(name="x{}".format(2), lowerbound=-3, upperbound=3)
op_min.integer_var(name="x{}".format(2), lowerbound=-3, upperbound=3)
op_max.maximize(constant=3,
linear={"x0": 1},
quadratic={("x1", "x2"): 2})
op_min.minimize(constant=3,
linear={"x0": 1},
quadratic={("x1", "x2"): 2})
# check conversion of maximization problem
conv = MaximizeToMinimize()
op_conv = conv.convert(op_max)
self.assertEqual(op_conv.objective.sense,
op_conv.objective.Sense.MINIMIZE)
x = [0, 1, 2]
fval_min = op_conv.objective.evaluate(conv.interpret(x))
self.assertAlmostEqual(fval_min, -7)
self.assertAlmostEqual(op_max.objective.evaluate(x), -fval_min)
# check conversion of minimization problem
op_conv = conv.convert(op_min)
self.assertEqual(op_conv.objective.sense, op_min.objective.sense)
fval_min = op_conv.objective.evaluate(conv.interpret(x))
self.assertAlmostEqual(op_min.objective.evaluate(x), fval_min)
def test_penalty_recalculation_when_reusing2(self):
"""Test the penalty retrieval and recalculation of LinearEqualityToPenalty 2"""
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.binary_var("z")
op.minimize(constant=3, linear={"x": 1}, quadratic={("x", "y"): 2})
op.linear_constraint(linear={
"x": 1,
"y": 1,
"z": 1
},
sense="EQ",
rhs=2,
name="xyz_eq")
# First, create a converter with no penalty
lineq2penalty = LinearEqualityToPenalty()
self.assertIsNone(lineq2penalty.penalty)
# Then converter must calculate the penalty for the problem (should be 4.0)
lineq2penalty.convert(op)
self.assertEqual(4, lineq2penalty.penalty)
# Re-use the converter for a new problem
op2 = QuadraticProgram()
op2.binary_var("x")
op2.minimize(linear={"x": 10})
op2.linear_constraint({"x": 1}, "==", 0)
lineq2penalty.convert(op2)
self.assertEqual(11, lineq2penalty.penalty)
def test_str_repr(self):
"""Test str and repr"""
with self.subTest("5 variables"):
n = 5
quadratic_program = QuadraticProgram()
quadratic_program.binary_var_list(n) # x0,...,x4
expr = LinearExpression(quadratic_program,
[float(e) for e in range(n)])
self.assertEqual(str(expr), "x1 + 2*x2 + 3*x3 + 4*x4")
self.assertEqual(repr(expr),
"<LinearExpression: x1 + 2*x2 + 3*x3 + 4*x4>")
with self.subTest("50 variables"):
# pylint: disable=cyclic-import
from qiskit_optimization.translators.prettyprint import DEFAULT_TRUNCATE
n = 50
quadratic_program = QuadraticProgram()
quadratic_program.binary_var_list(n) # x0,...,x49
expr = LinearExpression(quadratic_program,
[float(e) for e in range(n)])
expected = " ".join(["x1"] +
sorted([f"+ {i}*x{i}" for i in range(2, n)],
key=lambda e: e.split(" ")[1]))
self.assertEqual(str(expr), expected)
self.assertEqual(
repr(expr),
f"<LinearExpression: {expected[:DEFAULT_TRUNCATE]}...>")
def test_valid_variable_type(self):
"""Validate the types of the variables for QuadraticProgram.to_ising."""
# Integer variable
with self.assertRaises(QiskitOptimizationError):
op = QuadraticProgram()
op.integer_var(0, 10, "int_var")
_ = op.to_ising()
# Continuous variable
with self.assertRaises(QiskitOptimizationError):
op = QuadraticProgram()
op.continuous_var(0, 10, "continuous_var")
_ = op.to_ising()
def test_linear_inequality_to_penalty4(self):
"""Test special constraint to penalty x1+x2+x3+... <= 1 -> P(x1*x2+x1*x3+...)"""
op = QuadraticProgram()
lip = LinearInequalityToPenalty()
op.binary_var(name="x")
op.binary_var(name="y")
op.binary_var(name="z")
# Linear constraints
linear_constraint = {"x": 1, "y": 1, "z": 1}
op.linear_constraint(linear_constraint, Constraint.Sense.LE, 1,
"P(xy+yz+zx)")
# Test with no max/min
with self.subTest("No max/min"):
self.assertEqual(op.get_num_linear_constraints(), 1)
penalty = 1
quadratic = {
("x", "y"): penalty,
("x", "z"): penalty,
("y", "z"): penalty
}
op2 = lip.convert(op)
qdct = op2.objective.quadratic.to_dict(use_name=True)
self.assertEqual(qdct, quadratic)
self.assertEqual(op2.get_num_linear_constraints(), 0)
# Test maximize
op = QuadraticProgram()
op.binary_var_list(5)
linear2 = [1, 1, 0, 0, 0]
op.maximize(linear=linear2)
op.linear_constraint([1, 1, 1, 1, 1], Constraint.Sense.LE, 1, "")
with self.subTest("Maximum"):
self.assertEqual(op.get_num_linear_constraints(), 1)
lip.penalty = 5
quadratic2 = [
[0, 1, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 0, 1, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0],
]
op2 = lip.convert(op)
ldct2 = op2.objective.linear.to_array()
qdct2 = op2.objective.quadratic.to_array() / lip.penalty * -1
self.assertEqual(ldct2.tolist(), linear2)
self.assertEqual(qdct2.tolist(), quadratic2)
self.assertEqual(op2.get_num_linear_constraints(), 0)
def test_inequality_to_penalty_auto_define_penalty(self):
"""Test _auto_define_penalty() in InequalityToPenalty"""
op = QuadraticProgram()
op.integer_var(name="x", lowerbound=1, upperbound=3)
op.integer_var(name="y", lowerbound=-1, upperbound=4)
op.integer_var(name="z", lowerbound=-5, upperbound=-1)
op.maximize(linear={"x": 1, "y": 1, "z": 1})
lip = LinearInequalityToPenalty()
self.assertEqual(lip._auto_define_penalty(op), 12)
op = QuadraticProgram()
op.integer_var(name="x", lowerbound=1, upperbound=3)
op.integer_var(name="y", lowerbound=-1, upperbound=4)
op.integer_var(name="z", lowerbound=-5, upperbound=-1)
op.maximize(linear={"x": -1, "y": -1, "z": -1})
lip = LinearInequalityToPenalty()
self.assertEqual(lip._auto_define_penalty(op), 12)
op = QuadraticProgram()
op.integer_var(name="x", lowerbound=1, upperbound=3)
op.integer_var(name="y", lowerbound=-1, upperbound=4)
op.integer_var(name="z", lowerbound=-5, upperbound=-1)
op.maximize(quadratic={
(0, 0): 1,
(0, 1): 1,
(0, 2): 1,
(1, 1): 1,
(1, 2): 1,
(2, 2): 1
})
lip = LinearInequalityToPenalty()
self.assertEqual(lip._auto_define_penalty(op), 103)
op = QuadraticProgram()
op.integer_var(name="x", lowerbound=1, upperbound=3)
op.integer_var(name="y", lowerbound=-1, upperbound=4)
op.integer_var(name="z", lowerbound=-5, upperbound=-1)
op.maximize(quadratic={
(0, 0): -1,
(0, 1): -1,
(0, 2): -1,
(1, 1): -1,
(1, 2): -1,
(2, 2): -1
})
lip = LinearInequalityToPenalty()
self.assertEqual(lip._auto_define_penalty(op), 103)
op = QuadraticProgram()
op.integer_var(lowerbound=-2, upperbound=1, name="x")
op.minimize(quadratic={("x", "x"): 1})
lip = LinearInequalityToPenalty()
self.assertEqual(lip._auto_define_penalty(op), 5)
def test_name_setter(self):
""" test name setter """
q_p = QuadraticProgram()
self.assertEqual(q_p.name, '')
name = 'test name'
q_p.name = name
self.assertEqual(q_p.name, name)
def test_setters(self):
""" test setters. """
quadratic_program = QuadraticProgram()
for _ in range(5):
quadratic_program.continuous_var()
n = quadratic_program.get_num_vars()
zeros = np.zeros((n, n))
quadratic = QuadraticExpression(quadratic_program, zeros)
coefficients_list = [[0 for _ in range(5)] for _ in range(5)]
for i, v in enumerate(coefficients_list):
for j, _ in enumerate(v):
coefficients_list[min(i, j)][max(i, j)] += i * j
coefficients_array = np.array(coefficients_list)
coefficients_dok = dok_matrix(coefficients_list)
coefficients_dict_int = {(i, j): v for (i, j), v in coefficients_dok.items()}
coefficients_dict_str = {('x{}'.format(i), 'x{}'.format(j)): v for (i, j), v in
coefficients_dok.items()}
for coeffs in [coefficients_list,
coefficients_array,
coefficients_dok,
coefficients_dict_int,
coefficients_dict_str]:
quadratic.coefficients = coeffs
self.assertEqual((quadratic.coefficients != coefficients_dok).nnz, 0)
self.assertTrue((quadratic.to_array() == coefficients_list).all())
self.assertDictEqual(quadratic.to_dict(use_name=False), coefficients_dict_int)
self.assertDictEqual(quadratic.to_dict(use_name=True), coefficients_dict_str)
def build_qubo_unconstrained_from_edges_dict(G, all_edges_dict, variables):
qubo = QuadraticProgram()
linear, quadratic = get_linear_quadratic_coeffs(G, all_edges_dict)
for var in variables:
qubo.binary_var(var)
qubo.minimize(linear=linear, quadratic=quadratic)
return qubo, linear, quadratic
def test_feasibility(self):
"""Tests feasibility methods."""
q_p = QuadraticProgram("test")
_ = q_p.continuous_var(-1, 1, "x")
_ = q_p.continuous_var(-10, 10, "y")
q_p.minimize(linear={"x": 1, "y": 1})
q_p.linear_constraint({"x": 1, "y": 1}, "<=", 10, "c0")
q_p.linear_constraint({"x": 1, "y": 1}, ">=", -10, "c1")
q_p.linear_constraint({"x": 1, "y": 1}, "==", 5, "c2")
q_p.quadratic_constraint({"y": 1}, {("x", "x"): 1}, "<=", 10, "c3")
q_p.quadratic_constraint({"y": 1}, {("x", "x"): 1}, ">=", 5, "c4")
q_p.quadratic_constraint(None, {
("x", "x"): 1,
("y", "y"): 1
}, "==", 25, "c5")
self.assertTrue(q_p.is_feasible([0, 5]))
self.assertFalse(q_p.is_feasible([1, 10]))
self.assertFalse(q_p.is_feasible([1, -12]))
self.assertFalse(q_p.is_feasible([1, 5]))
self.assertFalse(q_p.is_feasible([5, 0]))
self.assertFalse(q_p.is_feasible([1, 1]))
self.assertFalse(q_p.is_feasible([0, 0]))
feasible, variables, constraints = q_p.get_feasibility_info([10, 0])
self.assertFalse(feasible)
self.assertIsNotNone(variables)
self.assertEqual(1, len(variables))
self.assertEqual("x", variables[0].name)
self.assertIsNotNone(constraints)
self.assertEqual(3, len(constraints))
self.assertEqual("c2", constraints[0].name)
self.assertEqual("c3", constraints[1].name)
self.assertEqual("c5", constraints[2].name)
def test_objective_handling(self):
"""test objective handling"""
q_p = QuadraticProgram()
q_p.binary_var("x")
q_p.binary_var("y")
q_p.binary_var("z")
q_p.minimize()
obj = q_p.objective
self.assertEqual(obj.sense, QuadraticObjective.Sense.MINIMIZE)
self.assertEqual(obj.constant, 0)
self.assertDictEqual(obj.linear.to_dict(), {})
self.assertDictEqual(obj.quadratic.to_dict(), {})
q_p.maximize(1, {"y": 1}, {("z", "x"): 1, ("y", "y"): 1})
obj = q_p.objective
self.assertEqual(obj.sense, QuadraticObjective.Sense.MAXIMIZE)
self.assertEqual(obj.constant, 1)
self.assertDictEqual(obj.linear.to_dict(), {1: 1})
self.assertDictEqual(obj.linear.to_dict(use_name=True), {"y": 1})
self.assertListEqual(obj.linear.to_array().tolist(), [0, 1, 0])
self.assertDictEqual(obj.quadratic.to_dict(), {(0, 2): 1, (1, 1): 1})
self.assertDictEqual(
obj.quadratic.to_dict(symmetric=True), {(0, 2): 0.5, (2, 0): 0.5, (1, 1): 1}
)
self.assertDictEqual(obj.quadratic.to_dict(use_name=True), {("x", "z"): 1, ("y", "y"): 1})
self.assertDictEqual(
obj.quadratic.to_dict(use_name=True, symmetric=True),
{("x", "z"): 0.5, ("z", "x"): 0.5, ("y", "y"): 1},
)
self.assertListEqual(obj.quadratic.to_array().tolist(), [[0, 0, 1], [0, 1, 0], [0, 0, 0]])
self.assertListEqual(
obj.quadratic.to_array(symmetric=True).tolist(),
[[0, 0, 0.5], [0, 1, 0], [0.5, 0, 0]],
)
def test_init(self):
"""test init."""
quadratic_program = QuadraticProgram()
for _ in range(5):
quadratic_program.continuous_var()
coefficients_list = [[0 for _ in range(5)] for _ in range(5)]
for i, v in enumerate(coefficients_list):
for j, _ in enumerate(v):
coefficients_list[min(i, j)][max(i, j)] += i * j
coefficients_array = np.array(coefficients_list)
coefficients_dok = dok_matrix(coefficients_list)
coefficients_dict_int = {(i, j): v
for (i, j), v in coefficients_dok.items()}
coefficients_dict_str = {(f"x{i}", f"x{j}"): v
for (i, j), v in coefficients_dok.items()}
for coeffs in [
coefficients_list,
coefficients_array,
coefficients_dok,
coefficients_dict_int,
coefficients_dict_str,
]:
quadratic = QuadraticExpression(quadratic_program, coeffs)
self.assertEqual((quadratic.coefficients != coefficients_dok).nnz,
0)
self.assertTrue((quadratic.to_array() == coefficients_list).all())
self.assertDictEqual(quadratic.to_dict(use_name=False),
coefficients_dict_int)
self.assertDictEqual(quadratic.to_dict(use_name=True),
coefficients_dict_str)
def test_setters(self):
"""test setters."""
quadratic_program = QuadraticProgram()
for _ in range(5):
quadratic_program.continuous_var()
zeros = np.zeros(quadratic_program.get_num_vars())
linear = LinearExpression(quadratic_program, zeros)
coefficients_list = list(range(5))
coefficients_array = np.array(coefficients_list)
coefficients_dok = dok_matrix([coefficients_list])
coefficients_dict_int = {i: i for i in range(1, 5)}
coefficients_dict_str = {f"x{i}": i for i in range(1, 5)}
for coeffs in [
coefficients_list,
coefficients_array,
coefficients_dok,
coefficients_dict_int,
coefficients_dict_str,
]:
linear.coefficients = coeffs
self.assertEqual((linear.coefficients != coefficients_dok).nnz, 0)
self.assertTrue((linear.to_array() == coefficients_list).all())
self.assertDictEqual(linear.to_dict(use_name=False), coefficients_dict_int)
self.assertDictEqual(linear.to_dict(use_name=True), coefficients_dict_str)
def setUp(self):
super().setUp()
random.seed(123)
low = 0
high = 100
pos = {
i: (random.randint(low, high), random.randint(low, high))
for i in range(4)
}
self.graph = nx.random_geometric_graph(
4, np.hypot(high - low, high - low) + 1, pos=pos)
for w, v in self.graph.edges:
delta = [
self.graph.nodes[w]["pos"][i] - self.graph.nodes[v]["pos"][i]
for i in range(2)
]
self.graph.edges[w, v]["weight"] = np.rint(
np.hypot(delta[0], delta[1]))
op = QuadraticProgram()
for i in range(16):
op.binary_var()
self.result = OptimizationResult(
x=[1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1],
fval=272,
variables=op.variables,
status=OptimizationResultStatus.SUCCESS,
)
def test_simple_qubo(self):
"""Test on a simple QUBO problem."""
model = Model()
# pylint:disable=invalid-name
u = model.binary_var(name="u")
v = model.binary_var(name="v")
model.minimize((u - v + 2)**2)
problem = QuadraticProgram()
problem.from_docplex(model)
backend = BasicAer.get_backend("statevector_simulator")
qaoa = QAOA(quantum_instance=backend, reps=1)
optimizer = WarmStartQAOAOptimizer(
pre_solver=SlsqpOptimizer(),
relax_for_pre_solver=True,
qaoa=qaoa,
epsilon=0.25,
)
result_warm = optimizer.solve(problem)
self.assertIsNotNone(result_warm)
self.assertIsNotNone(result_warm.x)
np.testing.assert_almost_equal([0, 1], result_warm.x, 3)
self.assertIsNotNone(result_warm.fval)
np.testing.assert_almost_equal(1, result_warm.fval, 3)
def test_quadratic_program_to_qubo_inequality_to_penalty(self):
"""Test QuadraticProgramToQubo, passing inequality pattern"""
op = QuadraticProgram()
conv = QuadraticProgramToQubo()
op.binary_var(name="x")
op.binary_var(name="y")
# Linear constraints
linear_constraint = {"x": 1, "y": 1}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 1,
"P(1-x-y+xy)")
conv.penalty = 1
constant = 1
linear = {"x": -conv.penalty, "y": -conv.penalty}
quadratic = {("x", "y"): conv.penalty}
op2 = conv.convert(op)
cnst = op2.objective.constant
ldct = op2.objective.linear.to_dict(use_name=True)
qdct = op2.objective.quadratic.to_dict(use_name=True)
self.assertEqual(cnst, constant)
self.assertEqual(ldct, linear)
self.assertEqual(qdct, quadratic)
self.assertEqual(op2.get_num_linear_constraints(), 0)
def test_linear_inequality_to_penalty3(self):
"""Test special constraint to penalty x1+x2+x3+... >= n-1 -> P(x1*x2+x1*x3+...)"""
op = QuadraticProgram()
lip = LinearInequalityToPenalty()
op.binary_var_list(5)
# Linear constraints
n = 5
op.linear_constraint([1, 1, 1, 1, 1], Constraint.Sense.GE, n - 1, "")
# Test with no max/min
with self.subTest("No max/min"):
self.assertEqual(op.get_num_linear_constraints(), 1)
penalty = 5
lip.penalty = penalty
constant = 10
linear = [n - 1, n - 1, n - 1, n - 1, n - 1]
quadratic = [
[0, 1, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 0, 1, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0],
]
op2 = lip.convert(op)
cnst2 = op2.objective.constant / penalty
ldct2 = op2.objective.linear.to_array() / penalty * -1
qdct2 = op2.objective.quadratic.to_array() / penalty
self.assertEqual(cnst2, constant)
self.assertEqual(ldct2.tolist(), linear)
self.assertEqual(qdct2.tolist(), quadratic)
self.assertEqual(op2.get_num_linear_constraints(), 0)
def qaoa(G):
n = G.numberOfNodes()
G = nw.nxadapter.nk2nx(G)
w = nx.adjacency_matrix(G)
problem = QuadraticProgram()
_ = [problem.binary_var(f"x{i}") for i in range(n)]
linear = w.dot(np.ones(n))
quadratic = -w
problem.maximize(linear=linear, quadratic=quadratic)
c = [1]
for _ in range(n - 1):
c.append(0)
problem.linear_constraint(c, '==', 1)
cobyla = COBYLA()
backend = BasicAer.get_backend('qasm_simulator')
qaoa = QAOA(optimizer=cobyla, reps=3, quantum_instance=backend)
algorithm = MinimumEigenOptimizer(qaoa)
result = algorithm.solve(problem)
L = result.x
i = 0
res = {}
for x in L:
res[i] = x
i += 1
return res
def test_constrained_binary(self):
"""Constrained binary optimization problem."""
model = Model()
v = model.binary_var(name="v")
w = model.binary_var(name="w")
# pylint:disable=invalid-name
t = model.binary_var(name="t")
model.minimize(v + w + t)
model.add_constraint(2 * v + 10 * w + t <= 3, "cons1")
model.add_constraint(v + w + t >= 2, "cons2")
problem = QuadraticProgram()
problem.from_docplex(model)
backend = BasicAer.get_backend("statevector_simulator")
qaoa = QAOA(quantum_instance=backend, reps=1)
aggregator = MeanAggregator()
optimizer = WarmStartQAOAOptimizer(
pre_solver=SlsqpOptimizer(),
relax_for_pre_solver=True,
qaoa=qaoa,
epsilon=0.25,
aggregator=aggregator,
)
result_warm = optimizer.solve(problem)
self.assertIsNotNone(result_warm)
self.assertIsNotNone(result_warm.x)
np.testing.assert_almost_equal([1, 0, 1], result_warm.x, 3)
self.assertIsNotNone(result_warm.fval)
np.testing.assert_almost_equal(2, result_warm.fval, 3)
def test_auto_penalty(self):
"""Test auto penalty function"""
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.binary_var("z")
op.minimize(constant=3, linear={"x": 1}, quadratic={("x", "y"): 2})
op.linear_constraint(linear={
"x": 1,
"y": 1,
"z": 1
},
sense="EQ",
rhs=2,
name="xyz_eq")
lineq2penalty = LinearEqualityToPenalty(penalty=1e5)
lineq2penalty_auto = LinearEqualityToPenalty()
qubo = lineq2penalty.convert(op)
qubo_auto = lineq2penalty_auto.convert(op)
exact_mes = NumPyMinimumEigensolver()
exact = MinimumEigenOptimizer(exact_mes)
result = exact.solve(qubo)
result_auto = exact.solve(qubo_auto)
self.assertEqual(result.fval, result_auto.fval)
np.testing.assert_array_almost_equal(result.x, result_auto.x)
def test_integer_to_binary2(self):
"""Test integer to binary variables 2"""
mod = QuadraticProgram()
mod.integer_var(name="x", lowerbound=0, upperbound=1)
mod.integer_var(name="y", lowerbound=0, upperbound=1)
mod.minimize(1, {"x": 1}, {("x", "y"): 2})
mod.linear_constraint({"x": 1}, "==", 1)
mod.quadratic_constraint({"x": 1}, {("x", "y"): 2}, "==", 1)
mod2 = IntegerToBinary().convert(mod)
self.assertListEqual([e.name + "@0" for e in mod.variables],
[e.name for e in mod2.variables])
self.assertDictEqual(mod.objective.linear.to_dict(),
mod2.objective.linear.to_dict())
self.assertDictEqual(mod.objective.quadratic.to_dict(),
mod2.objective.quadratic.to_dict())
self.assertEqual(mod.get_num_linear_constraints(),
mod2.get_num_linear_constraints())
for cst, cst2 in zip(mod.linear_constraints, mod2.linear_constraints):
self.assertDictEqual(cst.linear.to_dict(), cst2.linear.to_dict())
self.assertEqual(mod.get_num_quadratic_constraints(),
mod2.get_num_quadratic_constraints())
for cst, cst2 in zip(mod.quadratic_constraints,
mod2.quadratic_constraints):
self.assertDictEqual(cst.linear.to_dict(), cst2.linear.to_dict())
self.assertDictEqual(cst.quadratic.to_dict(),
cst2.quadratic.to_dict())
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_ising_to_quadraticprogram_quadratic(self):
"""Test optimization problem to operators with linear=False"""
op = QUBIT_OP_MAXIMIZE_SAMPLE
offset = OFFSET_MAXIMIZE_SAMPLE
quadratic = QuadraticProgram()
quadratic.from_ising(op, offset, linear=False)
self.assertEqual(quadratic.get_num_vars(), 4)
self.assertEqual(quadratic.get_num_linear_constraints(), 0)
self.assertEqual(quadratic.get_num_quadratic_constraints(), 0)
self.assertEqual(quadratic.objective.sense,
quadratic.objective.Sense.MINIMIZE)
self.assertAlmostEqual(quadratic.objective.constant, 900000)
quadratic_matrix = np.zeros((4, 4))
quadratic_matrix[0, 0] = -500001
quadratic_matrix[0, 1] = 400000
quadratic_matrix[0, 2] = 600000
quadratic_matrix[0, 3] = 800000
quadratic_matrix[1, 1] = -800001
quadratic_matrix[1, 2] = 1200000
quadratic_matrix[1, 3] = 1600000
quadratic_matrix[2, 2] = -900001
quadratic_matrix[2, 3] = 2400000
quadratic_matrix[3, 3] = -800001
np.testing.assert_array_almost_equal(
quadratic.objective.quadratic.coefficients.toarray(),
quadratic_matrix)
def test_init(self):
"""test init."""
quadratic_program = QuadraticProgram()
for _ in range(5):
quadratic_program.continuous_var()
coefficients_list = list(range(5))
coefficients_array = np.array(coefficients_list)
coefficients_dok = dok_matrix([coefficients_list])
coefficients_dict_int = {i: i for i in range(1, 5)}
coefficients_dict_str = {"x{}".format(i): i for i in range(1, 5)}
for coeffs in [
coefficients_list,
coefficients_array,
coefficients_dok,
coefficients_dict_int,
coefficients_dict_str,
]:
linear = LinearExpression(quadratic_program, coeffs)
self.assertEqual((linear.coefficients != coefficients_dok).nnz, 0)
self.assertTrue((linear.to_array() == coefficients_list).all())
self.assertDictEqual(linear.to_dict(use_name=False),
coefficients_dict_int)
self.assertDictEqual(linear.to_dict(use_name=True),
coefficients_dict_str)
def test_write_to_lp_file(self):
"""test write problem"""
q_p = QuadraticProgram("my problem")
q_p.binary_var("x")
q_p.integer_var(-1, 5, "y")
q_p.continuous_var(-1, 5, "z")
q_p.minimize(1, {"x": 1, "y": -1, "z": 10}, {("x", "x"): 0.5, ("y", "z"): -1})
q_p.linear_constraint({"x": 1, "y": 2}, "==", 1, "lin_eq")
q_p.linear_constraint({"x": 1, "y": 2}, "<=", 1, "lin_leq")
q_p.linear_constraint({"x": 1, "y": 2}, ">=", 1, "lin_geq")
q_p.quadratic_constraint(
{"x": 1, "y": 1},
{("x", "x"): 1, ("y", "z"): -1, ("z", "z"): 2},
"==",
1,
"quad_eq",
)
q_p.quadratic_constraint(
{"x": 1, "y": 1},
{("x", "x"): 1, ("y", "z"): -1, ("z", "z"): 2},
"<=",
1,
"quad_leq",
)
q_p.quadratic_constraint(
{"x": 1, "y": 1},
{("x", "x"): 1, ("y", "z"): -1, ("z", "z"): 2},
">=",
1,
"quad_geq",
)
reference_file_name = self.get_resource_path(
"test_quadratic_program.lp", "problems/resources"
)
with tempfile.TemporaryDirectory() as tmp:
temp_output_path = path.join(tmp, "temp.lp")
q_p.write_to_lp_file(temp_output_path)
with open(reference_file_name, encoding="utf8") as reference, open(
temp_output_path, encoding="utf8"
) as temp_output_file:
lines1 = temp_output_file.readlines()
lines2 = reference.readlines()
self.assertListEqual(lines1, lines2)
with tempfile.TemporaryDirectory() as temp_problem_dir:
q_p.write_to_lp_file(temp_problem_dir)
with open(path.join(temp_problem_dir, "my_problem.lp"), encoding="utf8") as file1, open(
reference_file_name, encoding="utf8"
) as file2:
lines1 = file1.readlines()
lines2 = file2.readlines()
self.assertListEqual(lines1, lines2)
with self.assertRaises(OSError):
q_p.write_to_lp_file("/cannot/write/this/file.lp")
with self.assertRaises(DOcplexException):
q_p.write_to_lp_file("")
def build_qubo_unconstrained_from_nodes(G, routes_dict):
all_edges_dict = get_edges_dict(routes_dict)
qubo = QuadraticProgram()
linear, quadratic = get_linear_quadratic_coeffs(G, all_edges_dict)
for var in routes_dict.keys():
qubo.binary_var(var)
qubo.minimize(linear=linear, quadratic=quadratic)
return qubo, linear, quadratic
def test_bounds(self):
"""test lowerbound and upperbound"""
with self.subTest("bounded"):
quadratic_program = QuadraticProgram()
quadratic_program.continuous_var_list(3, lowerbound=-1, upperbound=2)
coefficients_list = list(range(3))
bounds = LinearExpression(quadratic_program, coefficients_list).bounds
self.assertAlmostEqual(bounds.lowerbound, -3)
self.assertAlmostEqual(bounds.upperbound, 6)
with self.subTest("bounded2"):
quadratic_program = QuadraticProgram()
quadratic_program.integer_var(lowerbound=-2, upperbound=-1, name="x")
quadratic_program.integer_var(lowerbound=2, upperbound=4, name="y")
bounds = LinearExpression(quadratic_program, {"x": 1, "y": 10}).bounds
self.assertAlmostEqual(bounds.lowerbound, 18)
self.assertAlmostEqual(bounds.upperbound, 39)
bounds = LinearExpression(quadratic_program, {"x": -1, "y": 10}).bounds
self.assertAlmostEqual(bounds.lowerbound, 21)
self.assertAlmostEqual(bounds.upperbound, 42)
bounds = LinearExpression(quadratic_program, {"x": 1, "y": -10}).bounds
self.assertAlmostEqual(bounds.lowerbound, -42)
self.assertAlmostEqual(bounds.upperbound, -21)
bounds = LinearExpression(quadratic_program, {"x": -1, "y": -10}).bounds
self.assertAlmostEqual(bounds.lowerbound, -39)
self.assertAlmostEqual(bounds.upperbound, -18)
bounds = LinearExpression(quadratic_program, {"x": 0, "y": 0}).bounds
self.assertAlmostEqual(bounds.lowerbound, 0)
self.assertAlmostEqual(bounds.upperbound, 0)
with self.assertRaises(QiskitOptimizationError):
quadratic_program = QuadraticProgram()
quadratic_program.continuous_var_list(3, lowerbound=0, upperbound=INFINITY)
coefficients_list = list(range(3))
_ = LinearExpression(quadratic_program, coefficients_list).bounds
with self.assertRaises(QiskitOptimizationError):
quadratic_program = QuadraticProgram()
quadratic_program.continuous_var_list(3, lowerbound=-INFINITY, upperbound=0)
coefficients_list = list(range(3))
_ = LinearExpression(quadratic_program, coefficients_list).bounds
def test_symmetric_set(self):
""" test symmetric set """
q_p = QuadraticProgram()
q_p.binary_var('x')
q_p.binary_var('y')
q_p.binary_var('z')
quad = QuadraticExpression(q_p, {('x', 'y'): -1, ('y', 'x'): 2, ('z', 'x'): 3})
self.assertDictEqual(quad.to_dict(use_name=True), {('x', 'y'): 1, ('x', 'z'): 3})
self.assertDictEqual(quad.to_dict(symmetric=True, use_name=True),
{('x', 'y'): 0.5, ('y', 'x'): 0.5, ('x', 'z'): 1.5, ('z', 'x'): 1.5})
def setUp(self):
"""Set up for the test"""
super().setUp()
self.num_set = [8, 7, 6, 5, 4]
op = QuadraticProgram()
for _ in range(5):
op.binary_var()
self.result = OptimizationResult(
x=[1, 1, 0, 0, 0], fval=0, variables=op.variables,
status=OptimizationResultStatus.SUCCESS)
