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_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_reusing(self):
"""Test the penalty retrieval and recalculation of LinearEqualityToPenalty"""
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 with a newly defined penalty
lineq2penalty.penalty = 3
lineq2penalty.convert(op)
self.assertEqual(3, lineq2penalty.penalty)
# Re-use the converter letting the penalty be calculated again
lineq2penalty.penalty = None
lineq2penalty.convert(op)
self.assertEqual(4, lineq2penalty.penalty)
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_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_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_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_linear_inequality_to_penalty7(self):
"""Test special constraint to penalty 6 x-y >= 0 -> P(y-x*y)"""
op = QuadraticProgram()
lip = LinearInequalityToPenalty()
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, 0,
"P(y-xy)")
# Test with no max/min
with self.subTest("No max/min"):
self.assertEqual(op.get_num_linear_constraints(), 1)
penalty = 1
linear = {"y": penalty}
quadratic = {("x", "y"): -1 * penalty}
op2 = lip.convert(op)
ldct = op2.objective.linear.to_dict(use_name=True)
qdct = op2.objective.quadratic.to_dict(use_name=True)
self.assertEqual(ldct, linear)
self.assertEqual(qdct, quadratic)
self.assertEqual(op2.get_num_linear_constraints(), 0)
# Test maximize
with self.subTest("Maximize"):
linear = {"x": 2, "y": 1}
op.maximize(linear=linear)
penalty = 4
linear["y"] = linear["y"] - penalty
quadratic = {("x", "y"): penalty}
op2 = lip.convert(op)
ldct = op2.objective.linear.to_dict(use_name=True)
qdct = op2.objective.quadratic.to_dict(use_name=True)
self.assertEqual(ldct, linear)
self.assertEqual(qdct, quadratic)
self.assertEqual(op2.get_num_linear_constraints(), 0)
# Test minimize
with self.subTest("Minimize"):
linear = {"x": 2, "y": 1}
op.minimize(linear={"x": 2, "y": 1})
penalty = 4
linear["y"] = linear["y"] + penalty
quadratic = {("x", "y"): -1 * penalty}
op2 = lip.convert(op)
ldct = op2.objective.linear.to_dict(use_name=True)
qdct = op2.objective.quadratic.to_dict(use_name=True)
self.assertEqual(ldct, linear)
self.assertEqual(qdct, quadratic)
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_clear(self):
"""test clear"""
q_p = QuadraticProgram("test")
q_p.binary_var("x")
q_p.binary_var("y")
q_p.minimize(constant=1, linear={"x": 1, "y": 2}, quadratic={("x", "x"): 1})
q_p.linear_constraint({"x": 1}, "==", 1)
q_p.quadratic_constraint({"x": 1}, {("y", "y"): 2}, "<=", 1)
q_p.clear()
self.assertEqual(q_p.name, "")
self.assertEqual(q_p.status, QuadraticProgram.Status.VALID)
self.assertEqual(q_p.get_num_vars(), 0)
self.assertEqual(q_p.get_num_linear_constraints(), 0)
self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
self.assertEqual(q_p.objective.constant, 0)
self.assertDictEqual(q_p.objective.linear.to_dict(), {})
self.assertDictEqual(q_p.objective.quadratic.to_dict(), {})
def test_auto_penalty_warning(self):
""" Test warnings of auto penalty function"""
op = QuadraticProgram()
op.binary_var('x')
op.binary_var('y')
op.binary_var('z')
op.minimize(linear={'x': 1, 'y': 2})
op.linear_constraint(linear={'x': 0.5, 'y': 0.5, 'z': 0.5}, sense='EQ', rhs=1, name='xyz')
with self.assertLogs('qiskit_optimization', level='WARNING') as log:
lineq2penalty = LinearEqualityToPenalty()
_ = lineq2penalty.convert(op)
warning = (
'WARNING:qiskit_optimization.converters.linear_equality_to_penalty:'
'Warning: Using 100000.000000 for the penalty coefficient because a float '
'coefficient exists in constraints. \nThe value could be too small. If so, '
'set the penalty coefficient manually.'
)
self.assertIn(warning, log.output)
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])
def test_str_repr(self):
"""Test str and repr"""
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",
)
self.assertEqual(
str(q_p),
"minimize 0.5*x^2 - y*z + x - y + 10*z + 1 (3 variables, 6 constraints, 'my problem')",
)
self.assertEqual(
repr(q_p),
"<QuadraticProgram: minimize 0.5*x^2 - y*z + x - y + 10*z + 1, "
"3 variables, 6 constraints, 'my problem'>",
)
def test_clear(self):
""" test clear """
q_p = QuadraticProgram('test')
q_p.binary_var('x')
q_p.binary_var('y')
q_p.minimize(constant=1,
linear={
'x': 1,
'y': 2
},
quadratic={('x', 'x'): 1})
q_p.linear_constraint({'x': 1}, '==', 1)
q_p.quadratic_constraint({'x': 1}, {('y', 'y'): 2}, '<=', 1)
q_p.clear()
self.assertEqual(q_p.name, '')
self.assertEqual(q_p.status, QuadraticProgram.Status.VALID)
self.assertEqual(q_p.get_num_vars(), 0)
self.assertEqual(q_p.get_num_linear_constraints(), 0)
self.assertEqual(q_p.get_num_quadratic_constraints(), 0)
self.assertEqual(q_p.objective.constant, 0)
self.assertDictEqual(q_p.objective.linear.to_dict(), {})
self.assertDictEqual(q_p.objective.quadratic.to_dict(), {})
def test_integer_to_binary_quadratic(self):
"""Test integer to binary variables with quadratic expressions"""
mod = QuadraticProgram()
mod.integer_var(name="x", lowerbound=10, upperbound=13)
mod.minimize(quadratic={("x", "x"): 1})
mod2 = IntegerToBinary().convert(mod)
self.assertListEqual([e.name for e in mod2.variables], ["x@0", "x@1"])
self.assertEqual(mod.get_num_linear_constraints(), 0)
self.assertEqual(mod.get_num_quadratic_constraints(), 0)
self.assertAlmostEqual(mod2.objective.constant, 100)
self.assertDictEqual(mod2.objective.linear.to_dict(use_name=True), {
"x@0": 20,
"x@1": 40
})
self.assertDictEqual(
mod2.objective.quadratic.to_dict(use_name=True),
{
("x@0", "x@0"): 1,
("x@1", "x@1"): 4,
("x@0", "x@1"): 4
},
)
def test_auto_penalty_warning(self):
"""Test warnings of auto penalty function"""
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.binary_var("z")
op.minimize(linear={"x": 1, "y": 2})
op.linear_constraint(linear={
"x": 0.5,
"y": 0.5,
"z": 0.5
},
sense="EQ",
rhs=1,
name="xyz")
with self.assertLogs("qiskit_optimization", level="WARNING") as log:
lineq2penalty = LinearEqualityToPenalty()
_ = lineq2penalty.convert(op)
warning = (
"WARNING:qiskit_optimization.converters.linear_equality_to_penalty:"
"Warning: Using 100000.000000 for the penalty coefficient because a float "
"coefficient exists in constraints. \nThe value could be too small. If so, "
"set the penalty coefficient manually.")
self.assertIn(warning, log.output)
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(
path.join('resources', 'test_quadratic_program.lp'))
temp_output_file = tempfile.NamedTemporaryFile(mode='w+t',
suffix='.lp')
q_p.write_to_lp_file(temp_output_file.name)
with open(reference_file_name) as reference:
lines1 = temp_output_file.readlines()
lines2 = reference.readlines()
self.assertListEqual(lines1, lines2)
temp_output_file.close() # automatically deleted
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')) as file1, open(
reference_file_name) 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 test_linear_inequality_to_penalty1(self):
"""Test special constraint to penalty x+y <= 1 -> P(x*y)"""
op = QuadraticProgram()
lip = LinearInequalityToPenalty()
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.LE, 1,
"P(xy)")
# Test with no max/min
with self.subTest("No max/min"):
self.assertEqual(op.get_num_linear_constraints(), 1)
lip.penalty = 1
quadratic = {("x", "y"): lip.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
with self.subTest("Maximize"):
linear = {"x": 2, "y": 1}
op.maximize(linear=linear)
lip.penalty = 5
quadratic = {("x", "y"): -1 * lip.penalty}
op2 = lip.convert(op)
ldct = op2.objective.linear.to_dict(use_name=True)
qdct = op2.objective.quadratic.to_dict(use_name=True)
self.assertEqual(ldct, linear)
self.assertEqual(qdct, quadratic)
self.assertEqual(op2.get_num_linear_constraints(), 0)
# Test minimize
with self.subTest("Minimize"):
linear = {"x": 2, "y": 1}
op.minimize(linear=linear)
lip.penalty = 5
quadratic = {("x", "y"): lip.penalty}
op2 = lip.convert(op)
ldct = op2.objective.linear.to_dict(use_name=True)
qdct = op2.objective.quadratic.to_dict(use_name=True)
self.assertEqual(ldct, linear)
self.assertEqual(qdct, quadratic)
self.assertEqual(op2.get_num_linear_constraints(), 0)
# Test combination
with self.subTest("Combination"):
op = QuadraticProgram()
lip = LinearInequalityToPenalty()
op.binary_var(name="x")
op.binary_var(name="y")
op.binary_var(name="z")
op.binary_var(name="w")
linear = {"x": 2, "y": 1, "z": -1, "w": 1}
quadratic = {("y", "z"): -2, ("w", "w"): 1}
op.minimize(linear=linear, quadratic=quadratic)
linear_constraint = {"x": 1, "w": 1}
op.linear_constraint(linear_constraint, Constraint.Sense.LE, 1,
"P(xw)")
linear_constraint = {"y": 1, "z": 1}
op.linear_constraint(linear_constraint, Constraint.Sense.LE, 1,
"P(yz)")
linear_constraint = {"y": 2, "z": 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1,
"None 1")
quadratic_constraint = {("x", "x"): -2, ("y", "w"): 1}
op.quadratic_constraint(linear_constraint, quadratic_constraint,
Constraint.Sense.LE, 1, "None 2")
lip.penalty = 5
op2 = lip.convert(op)
quadratic[("x", "w")] = lip.penalty
quadratic[("y", "z")] = quadratic[("y", "z")] + lip.penalty
ldct = op2.objective.linear.to_dict(use_name=True)
qdct = op2.objective.quadratic.to_dict(use_name=True)
self.assertEqual(ldct, linear)
self.assertEqual(qdct, quadratic)
self.assertEqual(op2.get_num_linear_constraints(), 1)
self.assertEqual(op2.get_num_quadratic_constraints(), 1)
def test_integer_to_binary_zero_range_variable(self):
"""Test integer to binary variables with zero range variables"""
with self.subTest(
"zero range variable in a linear expression of the objective"):
mod = QuadraticProgram()
mod.integer_var(name="x", lowerbound=10, upperbound=10)
mod.minimize(linear={"x": 1})
mod2 = IntegerToBinary().convert(mod)
self.assertListEqual([e.name for e in mod2.variables], ["x@0"])
self.assertEqual(mod.get_num_linear_constraints(), 0)
self.assertEqual(mod.get_num_quadratic_constraints(), 0)
self.assertAlmostEqual(mod2.objective.constant, 10)
self.assertDictEqual(mod2.objective.linear.to_dict(), {})
self.assertDictEqual(mod2.objective.quadratic.to_dict(), {})
with self.subTest(
"zero range variable in a quadratic expression of the objective"
):
mod = QuadraticProgram()
mod.integer_var(name="x", lowerbound=10, upperbound=10)
mod.minimize(quadratic={("x", "x"): 1})
mod2 = IntegerToBinary().convert(mod)
self.assertListEqual([e.name for e in mod2.variables], ["x@0"])
self.assertEqual(mod.get_num_linear_constraints(), 0)
self.assertEqual(mod.get_num_quadratic_constraints(), 0)
self.assertAlmostEqual(mod2.objective.constant, 100)
self.assertDictEqual(mod2.objective.linear.to_dict(), {})
self.assertDictEqual(mod2.objective.quadratic.to_dict(), {})
with self.subTest("zero range variable in a linear constraint"):
mod = QuadraticProgram()
mod.integer_var(name="x", lowerbound=10, upperbound=10)
mod.binary_var(name="y")
mod.linear_constraint({"x": 1, "y": 1}, "<=", 100)
mod2 = IntegerToBinary().convert(mod)
self.assertListEqual([e.name for e in mod2.variables],
["x@0", "y"])
self.assertEqual(mod.get_num_linear_constraints(), 1)
self.assertEqual(mod.get_num_quadratic_constraints(), 0)
self.assertAlmostEqual(mod2.objective.constant, 0)
self.assertDictEqual(mod2.objective.linear.to_dict(), {})
self.assertDictEqual(mod2.objective.quadratic.to_dict(), {})
cst = mod2.get_linear_constraint(0)
self.assertDictEqual(cst.linear.to_dict(use_name=True), {"y": 1})
self.assertEqual(cst.sense, Constraint.Sense.LE)
self.assertAlmostEqual(cst.rhs, 90)
self.assertEqual(cst.name, "c0")
with self.subTest("zero range variable in a quadratic constraint"):
mod = QuadraticProgram()
mod.integer_var(name="x", lowerbound=10, upperbound=10)
mod.binary_var(name="y")
mod.quadratic_constraint({"x": 1}, {
("x", "x"): 2,
("x", "y"): 3
}, ">=", 100)
mod2 = IntegerToBinary().convert(mod)
self.assertListEqual([e.name for e in mod2.variables],
["x@0", "y"])
self.assertEqual(mod.get_num_linear_constraints(), 0)
self.assertEqual(mod.get_num_quadratic_constraints(), 1)
self.assertAlmostEqual(mod2.objective.constant, 0)
self.assertDictEqual(mod2.objective.linear.to_dict(), {})
self.assertDictEqual(mod2.objective.quadratic.to_dict(), {})
cst = mod2.get_quadratic_constraint(0)
self.assertDictEqual(cst.linear.to_dict(use_name=True), {"y": 30})
self.assertEqual(cst.sense, Constraint.Sense.GE)
self.assertAlmostEqual(cst.rhs, -110)
self.assertEqual(cst.name, "q0")
def test_docplex(self):
"""test from_docplex and to_docplex"""
q_p = QuadraticProgram("test")
q_p.binary_var(name="x")
q_p.integer_var(name="y", lowerbound=-2, upperbound=4)
q_p.continuous_var(name="z", lowerbound=-1.5, upperbound=3.2)
q_p.minimize(
constant=1,
linear={
"x": 1,
"y": 2
},
quadratic={
("x", "y"): -1,
("z", "z"): 2
},
)
q_p.linear_constraint({"x": 2, "z": -1}, "==", 1)
q_p.quadratic_constraint({"x": 2, "z": -1}, {("y", "z"): 3}, "==", 1)
q_p2 = QuadraticProgram()
q_p2.from_docplex(q_p.to_docplex())
self.assertEqual(q_p.export_as_lp_string(), q_p2.export_as_lp_string())
mod = Model("test")
x = mod.binary_var("x")
y = mod.integer_var(-2, 4, "y")
z = mod.continuous_var(-1.5, 3.2, "z")
mod.minimize(1 + x + 2 * y - x * y + 2 * z * z)
mod.add(2 * x - z == 1, "c0")
mod.add(2 * x - z + 3 * y * z == 1, "q0")
self.assertEqual(q_p.export_as_lp_string(), mod.export_as_lp_string())
with self.assertRaises(QiskitOptimizationError):
mod = Model()
mod.semiinteger_var(lb=1, name="x")
q_p.from_docplex(mod)
with self.assertRaises(QiskitOptimizationError):
mod = Model()
x = mod.binary_var("x")
mod.add_range(0, 2 * x, 1)
q_p.from_docplex(mod)
with self.assertRaises(QiskitOptimizationError):
mod = Model()
x = mod.binary_var("x")
y = mod.binary_var("y")
mod.add_indicator(x, x + y <= 1, 1)
q_p.from_docplex(mod)
with self.assertRaises(QiskitOptimizationError):
mod = Model()
x = mod.binary_var("x")
y = mod.binary_var("y")
mod.add_equivalence(x, x + y <= 1, 1)
q_p.from_docplex(mod)
with self.assertRaises(QiskitOptimizationError):
mod = Model()
x = mod.binary_var("x")
y = mod.binary_var("y")
mod.add(mod.not_equal_constraint(x, y + 1))
q_p.from_docplex(mod)
# test from_docplex without explicit variable names
mod = Model()
x = mod.binary_var()
y = mod.continuous_var()
z = mod.integer_var()
mod.minimize(x + y + z + x * y + y * z + x * z)
mod.add_constraint(x + y == z) # linear EQ
mod.add_constraint(x + y >= z) # linear GE
mod.add_constraint(x + y <= z) # linear LE
mod.add_constraint(x * y == z) # quadratic EQ
mod.add_constraint(x * y >= z) # quadratic GE
mod.add_constraint(x * y <= z) # quadratic LE
q_p = QuadraticProgram()
q_p.from_docplex(mod)
var_names = [v.name for v in q_p.variables]
self.assertListEqual(var_names, ["x0", "x1", "x2"])
senses = [
Constraint.Sense.EQ, Constraint.Sense.GE, Constraint.Sense.LE
]
for i, c in enumerate(q_p.linear_constraints):
self.assertDictEqual(c.linear.to_dict(use_name=True), {
"x0": 1,
"x1": 1,
"x2": -1
})
self.assertEqual(c.rhs, 0)
self.assertEqual(c.sense, senses[i])
for i, c in enumerate(q_p.quadratic_constraints):
self.assertEqual(c.rhs, 0)
self.assertDictEqual(c.linear.to_dict(use_name=True), {"x2": -1})
self.assertDictEqual(c.quadratic.to_dict(use_name=True),
{("x0", "x1"): 1})
self.assertEqual(c.sense, senses[i])
def test_substitute_variables(self):
"""test substitute variables"""
q_p = QuadraticProgram("test")
q_p.binary_var(name="x")
q_p.integer_var(name="y", lowerbound=-2, upperbound=4)
q_p.continuous_var(name="z", lowerbound=-1.5, upperbound=3.2)
q_p.minimize(
constant=1,
linear={
"x": 1,
"y": 2
},
quadratic={
("x", "y"): -1,
("z", "z"): 2
},
)
q_p.linear_constraint({"x": 2, "z": -1}, "==", 1)
q_p.quadratic_constraint({"x": 2, "z": -1}, {("y", "z"): 3}, "<=", -1)
with self.subTest("x <- -1"):
q_p2 = substitute_variables(q_p, constants={"x": -1})
self.assertEqual(q_p2.status, QuadraticProgram.Status.INFEASIBLE)
q_p2 = substitute_variables(q_p, constants={"y": -3})
self.assertEqual(q_p2.status, QuadraticProgram.Status.INFEASIBLE)
q_p2 = substitute_variables(q_p, constants={"x": 1, "z": 2})
self.assertEqual(q_p2.status, QuadraticProgram.Status.INFEASIBLE)
q_p2.clear()
self.assertEqual(q_p2.status, QuadraticProgram.Status.VALID)
with self.subTest("x <- 0"):
q_p2 = substitute_variables(q_p, constants={"x": 0})
self.assertEqual(q_p2.status, QuadraticProgram.Status.VALID)
self.assertDictEqual(q_p2.objective.linear.to_dict(use_name=True),
{"y": 2})
self.assertDictEqual(
q_p2.objective.quadratic.to_dict(use_name=True),
{("z", "z"): 2})
self.assertEqual(q_p2.objective.constant, 1)
self.assertEqual(len(q_p2.linear_constraints), 1)
self.assertEqual(len(q_p2.quadratic_constraints), 1)
cst = q_p2.linear_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {"z": -1})
self.assertEqual(cst.sense.name, "EQ")
self.assertEqual(cst.rhs, 1)
cst = q_p2.quadratic_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {"z": -1})
self.assertDictEqual(cst.quadratic.to_dict(use_name=True),
{("y", "z"): 3})
self.assertEqual(cst.sense.name, "LE")
self.assertEqual(cst.rhs, -1)
with self.subTest("z <- -1"):
q_p2 = substitute_variables(q_p, constants={"z": -1})
self.assertEqual(q_p2.status, QuadraticProgram.Status.VALID)
self.assertDictEqual(q_p2.objective.linear.to_dict(use_name=True),
{
"x": 1,
"y": 2
})
self.assertDictEqual(
q_p2.objective.quadratic.to_dict(use_name=True),
{("x", "y"): -1})
self.assertEqual(q_p2.objective.constant, 3)
self.assertEqual(len(q_p2.linear_constraints), 2)
self.assertEqual(len(q_p2.quadratic_constraints), 0)
cst = q_p2.linear_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {"x": 2})
self.assertEqual(cst.sense.name, "EQ")
self.assertEqual(cst.rhs, 0)
cst = q_p2.linear_constraints[1]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {
"x": 2,
"y": -3
})
self.assertEqual(cst.sense.name, "LE")
self.assertEqual(cst.rhs, -2)
with self.subTest("y <- -0.5 * x"):
q_p2 = substitute_variables(q_p, variables={"y": ("x", -0.5)})
self.assertEqual(q_p2.status, QuadraticProgram.Status.VALID)
self.assertDictEqual(q_p2.objective.linear.to_dict(use_name=True),
{})
self.assertDictEqual(
q_p2.objective.quadratic.to_dict(use_name=True),
{
("x", "x"): 0.5,
("z", "z"): 2
},
)
self.assertEqual(q_p2.objective.constant, 1)
self.assertEqual(len(q_p2.linear_constraints), 1)
self.assertEqual(len(q_p2.quadratic_constraints), 1)
cst = q_p2.linear_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {
"x": 2,
"z": -1
})
self.assertEqual(cst.sense.name, "EQ")
self.assertEqual(cst.rhs, 1)
cst = q_p2.quadratic_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {
"x": 2,
"z": -1
})
self.assertDictEqual(cst.quadratic.to_dict(use_name=True),
{("x", "z"): -1.5})
self.assertEqual(cst.sense.name, "LE")
self.assertEqual(cst.rhs, -1)
def test_substitute_variables(self):
"""test substitute variables"""
q_p = QuadraticProgram('test')
q_p.binary_var(name='x')
q_p.integer_var(name='y', lowerbound=-2, upperbound=4)
q_p.continuous_var(name='z', lowerbound=-1.5, upperbound=3.2)
q_p.minimize(constant=1,
linear={
'x': 1,
'y': 2
},
quadratic={
('x', 'y'): -1,
('z', 'z'): 2
})
q_p.linear_constraint({'x': 2, 'z': -1}, '==', 1)
q_p.quadratic_constraint({'x': 2, 'z': -1}, {('y', 'z'): 3}, '<=', -1)
q_p2 = q_p.substitute_variables(constants={'x': -1})
self.assertEqual(q_p2.status, QuadraticProgram.Status.INFEASIBLE)
q_p2 = q_p.substitute_variables(constants={'y': -3})
self.assertEqual(q_p2.status, QuadraticProgram.Status.INFEASIBLE)
q_p2 = q_p.substitute_variables(constants={'x': 1, 'z': 2})
self.assertEqual(q_p2.status, QuadraticProgram.Status.INFEASIBLE)
q_p2.clear()
self.assertEqual(q_p2.status, QuadraticProgram.Status.VALID)
q_p2 = q_p.substitute_variables(constants={'x': 0})
self.assertEqual(q_p2.status, QuadraticProgram.Status.VALID)
self.assertDictEqual(q_p2.objective.linear.to_dict(use_name=True),
{'y': 2})
self.assertDictEqual(q_p2.objective.quadratic.to_dict(use_name=True),
{('z', 'z'): 2})
self.assertEqual(q_p2.objective.constant, 1)
self.assertEqual(len(q_p2.linear_constraints), 1)
self.assertEqual(len(q_p2.quadratic_constraints), 1)
cst = q_p2.linear_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {'z': -1})
self.assertEqual(cst.sense.name, 'EQ')
self.assertEqual(cst.rhs, 1)
cst = q_p2.quadratic_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {'z': -1})
self.assertDictEqual(cst.quadratic.to_dict(use_name=True),
{('y', 'z'): 3})
self.assertEqual(cst.sense.name, 'LE')
self.assertEqual(cst.rhs, -1)
q_p2 = q_p.substitute_variables(constants={'z': -1})
self.assertEqual(q_p2.status, QuadraticProgram.Status.VALID)
self.assertDictEqual(q_p2.objective.linear.to_dict(use_name=True), {
'x': 1,
'y': 2
})
self.assertDictEqual(q_p2.objective.quadratic.to_dict(use_name=True),
{('x', 'y'): -1})
self.assertEqual(q_p2.objective.constant, 3)
self.assertEqual(len(q_p2.linear_constraints), 2)
self.assertEqual(len(q_p2.quadratic_constraints), 0)
cst = q_p2.linear_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {'x': 2})
self.assertEqual(cst.sense.name, 'EQ')
self.assertEqual(cst.rhs, 0)
cst = q_p2.linear_constraints[1]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {
'x': 2,
'y': -3
})
self.assertEqual(cst.sense.name, 'LE')
self.assertEqual(cst.rhs, -2)
q_p2 = q_p.substitute_variables(variables={'y': ('x', -0.5)})
self.assertEqual(q_p2.status, QuadraticProgram.Status.VALID)
self.assertDictEqual(q_p2.objective.linear.to_dict(use_name=True), {})
self.assertDictEqual(q_p2.objective.quadratic.to_dict(use_name=True), {
('x', 'x'): 0.5,
('z', 'z'): 2
})
self.assertEqual(q_p2.objective.constant, 1)
self.assertEqual(len(q_p2.linear_constraints), 1)
self.assertEqual(len(q_p2.quadratic_constraints), 1)
cst = q_p2.linear_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {
'x': 2,
'z': -1
})
self.assertEqual(cst.sense.name, 'EQ')
self.assertEqual(cst.rhs, 1)
cst = q_p2.quadratic_constraints[0]
self.assertDictEqual(cst.linear.to_dict(use_name=True), {
'x': 2,
'z': -1
})
self.assertDictEqual(cst.quadratic.to_dict(use_name=True),
{('x', 'z'): -1.5})
self.assertEqual(cst.sense.name, 'LE')
self.assertEqual(cst.rhs, -1)
def test_linear_inequality_to_penalty8(self):
"""Test combinations of inequality constraints"""
with self.subTest("minimize 1"):
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.binary_var("z")
op.integer_var(-1, 4, "q")
op.minimize(linear={
"x": 1,
"y": 1,
"z": 1
},
quadratic={("q", "q"): -1})
op.linear_constraint({"x": 1, "y": -1}, "<=", 0)
op.linear_constraint({"x": 1, "y": 1, "z": 1}, "<=", 1)
op2 = LinearInequalityToPenalty().convert(op)
self.assertEqual(op2.get_num_vars(), 4)
self.assertEqual(op2.get_num_binary_vars(), 3)
self.assertEqual(op2.get_num_integer_vars(), 1)
self.assertEqual(op2.get_num_continuous_vars(), 0)
self.assertEqual(op2.get_num_linear_constraints(), 0)
self.assertEqual(op2.get_num_quadratic_constraints(), 0)
obj = op2.objective
self.assertEqual(obj.constant, 0)
self.assertDictEqual(obj.linear.to_dict(use_name=True), {
"x": 21,
"y": 1,
"z": 1
})
self.assertDictEqual(
obj.quadratic.to_dict(use_name=True),
{
("x", "z"): 20,
("y", "z"): 20,
("q", "q"): -1
},
)
with self.subTest("maximize 1"):
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.binary_var("z")
op.integer_var(-1, 4, "q")
op.maximize(linear={
"x": 1,
"y": 1,
"z": 1
},
quadratic={("q", "q"): -1})
op.linear_constraint({"x": 1, "y": -1}, "<=", 0)
op.linear_constraint({"x": 1, "y": 1, "z": 1}, "<=", 1)
op2 = LinearInequalityToPenalty().convert(op)
self.assertEqual(op2.get_num_vars(), 4)
self.assertEqual(op2.get_num_binary_vars(), 3)
self.assertEqual(op2.get_num_integer_vars(), 1)
self.assertEqual(op2.get_num_continuous_vars(), 0)
self.assertEqual(op2.get_num_linear_constraints(), 0)
self.assertEqual(op2.get_num_quadratic_constraints(), 0)
obj = op2.objective
self.assertEqual(obj.constant, 0)
self.assertDictEqual(obj.linear.to_dict(use_name=True), {
"x": -19,
"y": 1,
"z": 1
})
self.assertDictEqual(
obj.quadratic.to_dict(use_name=True),
{
("x", "z"): -20,
("y", "z"): -20,
("q", "q"): -1
},
)
with self.subTest("minimize 2"):
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.binary_var("z")
op.integer_var(-1, 4, "q")
op.minimize(linear={
"x": 1,
"y": 1,
"z": 1
},
quadratic={("q", "q"): -1})
op.linear_constraint({"x": 1, "y": -1}, ">=", 0)
op.linear_constraint({"x": 1, "y": 1, "z": 1}, ">=", 2)
op2 = LinearInequalityToPenalty().convert(op)
self.assertEqual(op2.get_num_vars(), 4)
self.assertEqual(op2.get_num_binary_vars(), 3)
self.assertEqual(op2.get_num_integer_vars(), 1)
self.assertEqual(op2.get_num_continuous_vars(), 0)
self.assertEqual(op2.get_num_linear_constraints(), 0)
self.assertEqual(op2.get_num_quadratic_constraints(), 0)
obj = op2.objective
self.assertEqual(obj.constant, 60)
self.assertDictEqual(obj.linear.to_dict(use_name=True), {
"x": -39,
"y": -19,
"z": -39
})
self.assertDictEqual(
obj.quadratic.to_dict(use_name=True),
{
("x", "z"): 20,
("y", "z"): 20,
("q", "q"): -1
},
)
with self.subTest("maximize 2"):
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.binary_var("z")
op.integer_var(-1, 4, "q")
op.maximize(linear={
"x": 1,
"y": 1,
"z": 1
},
quadratic={("q", "q"): -1})
op.linear_constraint({"x": 1, "y": -1}, ">=", 0)
op.linear_constraint({"x": 1, "y": 1, "z": 1}, ">=", 2)
op2 = LinearInequalityToPenalty().convert(op)
self.assertEqual(op2.get_num_vars(), 4)
self.assertEqual(op2.get_num_binary_vars(), 3)
self.assertEqual(op2.get_num_integer_vars(), 1)
self.assertEqual(op2.get_num_continuous_vars(), 0)
self.assertEqual(op2.get_num_linear_constraints(), 0)
self.assertEqual(op2.get_num_quadratic_constraints(), 0)
obj = op2.objective
self.assertEqual(obj.constant, -60)
self.assertDictEqual(obj.linear.to_dict(use_name=True), {
"x": 41,
"y": 21,
"z": 41
})
self.assertDictEqual(
obj.quadratic.to_dict(use_name=True),
{
("x", "z"): -20,
("y", "z"): -20,
("q", "q"): -1
},
)
def test_docplex(self):
"""test from_docplex and to_docplex"""
q_p = QuadraticProgram('test')
q_p.binary_var(name='x')
q_p.integer_var(name='y', lowerbound=-2, upperbound=4)
q_p.continuous_var(name='z', lowerbound=-1.5, upperbound=3.2)
q_p.minimize(constant=1,
linear={
'x': 1,
'y': 2
},
quadratic={
('x', 'y'): -1,
('z', 'z'): 2
})
q_p.linear_constraint({'x': 2, 'z': -1}, '==', 1)
q_p.quadratic_constraint({'x': 2, 'z': -1}, {('y', 'z'): 3}, '==', 1)
q_p2 = QuadraticProgram()
q_p2.from_docplex(q_p.to_docplex())
self.assertEqual(q_p.export_as_lp_string(), q_p2.export_as_lp_string())
mod = Model('test')
x = mod.binary_var('x')
y = mod.integer_var(-2, 4, 'y')
z = mod.continuous_var(-1.5, 3.2, 'z')
mod.minimize(1 + x + 2 * y - x * y + 2 * z * z)
mod.add(2 * x - z == 1, 'c0')
mod.add(2 * x - z + 3 * y * z == 1, 'q0')
self.assertEqual(q_p.export_as_lp_string(), mod.export_as_lp_string())
with self.assertRaises(QiskitOptimizationError):
mod = Model()
mod.semiinteger_var(lb=1, name='x')
q_p.from_docplex(mod)
with self.assertRaises(QiskitOptimizationError):
mod = Model()
x = mod.binary_var('x')
mod.add_range(0, 2 * x, 1)
q_p.from_docplex(mod)
with self.assertRaises(QiskitOptimizationError):
mod = Model()
x = mod.binary_var('x')
y = mod.binary_var('y')
mod.add_indicator(x, x + y <= 1, 1)
q_p.from_docplex(mod)
with self.assertRaises(QiskitOptimizationError):
mod = Model()
x = mod.binary_var('x')
y = mod.binary_var('y')
mod.add_equivalence(x, x + y <= 1, 1)
q_p.from_docplex(mod)
with self.assertRaises(QiskitOptimizationError):
mod = Model()
x = mod.binary_var('x')
y = mod.binary_var('y')
mod.add(mod.not_equal_constraint(x, y + 1))
q_p.from_docplex(mod)
# test from_docplex without explicit variable names
mod = Model()
x = mod.binary_var()
y = mod.continuous_var()
z = mod.integer_var()
mod.minimize(x + y + z + x * y + y * z + x * z)
mod.add_constraint(x + y == z) # linear EQ
mod.add_constraint(x + y >= z) # linear GE
mod.add_constraint(x + y <= z) # linear LE
mod.add_constraint(x * y == z) # quadratic EQ
mod.add_constraint(x * y >= z) # quadratic GE
mod.add_constraint(x * y <= z) # quadratic LE
q_p = QuadraticProgram()
q_p.from_docplex(mod)
var_names = [v.name for v in q_p.variables]
self.assertListEqual(var_names, ['x0', 'x1', 'x2'])
senses = [
Constraint.Sense.EQ, Constraint.Sense.GE, Constraint.Sense.LE
]
for i, c in enumerate(q_p.linear_constraints):
self.assertDictEqual(c.linear.to_dict(use_name=True), {
'x0': 1,
'x1': 1,
'x2': -1
})
self.assertEqual(c.rhs, 0)
self.assertEqual(c.sense, senses[i])
for i, c in enumerate(q_p.quadratic_constraints):
self.assertEqual(c.rhs, 0)
self.assertDictEqual(c.linear.to_dict(use_name=True), {'x2': -1})
self.assertDictEqual(c.quadratic.to_dict(use_name=True),
{('x0', 'x1'): 1})
self.assertEqual(c.sense, senses[i])
