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_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_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 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_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_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 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_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 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_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 _sample_code(self):
def print(*args):
""" overloads print to log values """
if args:
self.log.debug(args[0], *args[1:])
# --- Exact copy of sample code ----------------------------------------
import networkx as nx
import numpy as np
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit import BasicAer
from qiskit.algorithms import QAOA
from qiskit.algorithms.optimizers import SPSA
# Generate a graph of 4 nodes
n = 4
graph = nx.Graph()
graph.add_nodes_from(np.arange(0, n, 1))
elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0),
(2, 3, 1.0)]
graph.add_weighted_edges_from(elist)
# Compute the weight matrix from the graph
w = nx.adjacency_matrix(graph)
# Formulate the problem as quadratic program
problem = QuadraticProgram()
_ = [problem.binary_var('x{}'.format(i))
for i in range(n)] # create n binary variables
linear = w.dot(np.ones(n))
quadratic = -w
problem.maximize(linear=linear, quadratic=quadratic)
# Fix node 0 to be 1 to break the symmetry of the max-cut solution
problem.linear_constraint([1, 0, 0, 0], '==', 1)
# Run quantum algorithm QAOA on qasm simulator
spsa = SPSA(maxiter=250)
backend = BasicAer.get_backend('qasm_simulator')
qaoa = QAOA(optimizer=spsa, p=5, quantum_instance=backend)
algorithm = MinimumEigenOptimizer(qaoa)
result = algorithm.solve(problem)
print(result) # prints solution, x=[1, 0, 1, 0], the cost, fval=4
# ----------------------------------------------------------------------
return result
def test_binary_to_integer(self):
"""Test binary to integer"""
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name="x{}".format(i))
op.integer_var(name="x2", lowerbound=0, upperbound=5)
linear = {"x0": 1, "x1": 2, "x2": 1}
op.maximize(0, linear, {})
linear = {}
for x in op.variables:
linear[x.name] = 1
op.linear_constraint(linear, Constraint.Sense.EQ, 6, "x0x1x2")
conv = IntegerToBinary()
_ = conv.convert(op)
new_x = conv.interpret([0, 1, 1, 1, 1])
np.testing.assert_array_almost_equal(new_x, [0, 1, 5])
def test_0var_range_inequality(self):
""" Test InequalityToEquality converter when the var_rang of the slack variable is 0"""
op = QuadraticProgram()
op.binary_var('x')
op.binary_var('y')
op.linear_constraint(linear={'x': 1, 'y': 1}, sense='LE', rhs=0, name='xy_leq1')
op.linear_constraint(linear={'x': 1, 'y': 1}, sense='GE', rhs=2, name='xy_geq1')
op.quadratic_constraint(quadratic={('x', 'x'): 1}, sense='LE', rhs=0, name='xy_leq2')
op.quadratic_constraint(quadratic={('x', 'y'): 1}, sense='GE', rhs=1, name='xy_geq2')
ineq2eq = InequalityToEquality()
new_op = ineq2eq.convert(op)
self.assertEqual(new_op.get_num_vars(), 2)
self.assertTrue(all(l_const.sense == Constraint.Sense.EQ
for l_const in new_op.linear_constraints))
self.assertTrue(all(q_const.sense == Constraint.Sense.EQ
for q_const in new_op.quadratic_constraints))
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_empty_name(self):
"""Test empty names"""
with self.subTest("problem name"):
q_p = QuadraticProgram("")
self.assertEqual(q_p.name, "")
with self.subTest("variable name"):
q_p = QuadraticProgram()
x = q_p.binary_var(name="")
y = q_p.integer_var(name="")
z = q_p.continuous_var(name="")
self.assertEqual(x.name, "x0")
self.assertEqual(y.name, "x1")
self.assertEqual(z.name, "x2")
with self.subTest("variable name 2"):
q_p = QuadraticProgram()
w = q_p.binary_var(name="w")
x = q_p.binary_var(name="")
y = q_p.integer_var(name="")
z = q_p.continuous_var(name="")
self.assertEqual(w.name, "w")
self.assertEqual(x.name, "x1")
self.assertEqual(y.name, "x2")
self.assertEqual(z.name, "x3")
with self.subTest("variable name list"):
q_p = QuadraticProgram()
x = q_p.binary_var_list(2, name="")
y = q_p.integer_var_list(2, name="")
z = q_p.continuous_var_list(2, name="")
self.assertListEqual([v.name for v in x], ["x0", "x1"])
self.assertListEqual([v.name for v in y], ["x2", "x3"])
self.assertListEqual([v.name for v in z], ["x4", "x5"])
with self.subTest("variable name dict"):
q_p = QuadraticProgram()
x = q_p.binary_var_dict(2, name="")
y = q_p.integer_var_dict(2, name="")
z = q_p.continuous_var_dict(2, name="")
self.assertDictEqual({k: v.name for k, v in x.items()}, {"x0": "x0", "x1": "x1"})
self.assertDictEqual({k: v.name for k, v in y.items()}, {"x2": "x2", "x3": "x3"})
self.assertDictEqual({k: v.name for k, v in z.items()}, {"x4": "x4", "x5": "x5"})
with self.subTest("linear constraint name"):
q_p = QuadraticProgram()
x = q_p.linear_constraint(name="")
y = q_p.linear_constraint(name="")
self.assertEqual(x.name, "c0")
self.assertEqual(y.name, "c1")
with self.subTest("quadratic constraint name"):
q_p = QuadraticProgram()
x = q_p.quadratic_constraint(name="")
y = q_p.quadratic_constraint(name="")
self.assertEqual(x.name, "q0")
self.assertEqual(y.name, "q1")
def test_optimizationproblem_to_ising(self):
"""Test optimization problem to operators"""
op = QuadraticProgram()
for i in range(4):
op.binary_var(name="x{}".format(i))
linear = {}
for x in op.variables:
linear[x.name] = 1
op.maximize(0, linear, {})
linear = {}
for i, x in enumerate(op.variables):
linear[x.name] = i + 1
op.linear_constraint(linear, Constraint.Sense.EQ, 3, "sum1")
penalize = LinearEqualityToPenalty(penalty=1e5)
op2 = penalize.convert(op)
qubitop, offset = op2.to_ising()
self.assertEqual(qubitop, QUBIT_OP_MAXIMIZE_SAMPLE)
self.assertEqual(offset, OFFSET_MAXIMIZE_SAMPLE)
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_printable_name(self):
"""Test non-printable names"""
name = "\n"
with self.assertWarns(UserWarning):
_ = QuadraticProgram(name)
q_p = QuadraticProgram()
with self.assertWarns(UserWarning):
q_p.binary_var(name + "bin")
with self.assertWarns(UserWarning):
q_p.binary_var_list(10, name)
with self.assertWarns(UserWarning):
q_p.binary_var_dict(10, name)
with self.assertWarns(UserWarning):
q_p.integer_var(0, 1, name + "int")
with self.assertWarns(UserWarning):
q_p.integer_var_list(10, 0, 1, name)
with self.assertWarns(UserWarning):
q_p.integer_var_dict(10, 0, 1, name)
with self.assertWarns(UserWarning):
q_p.continuous_var(0, 1, name + "cont")
with self.assertWarns(UserWarning):
q_p.continuous_var_list(10, 0, 1, name)
with self.assertWarns(UserWarning):
q_p.continuous_var_dict(10, 0, 1, name)
with self.assertWarns(UserWarning):
q_p.linear_constraint(name=name)
with self.assertWarns(UserWarning):
q_p.quadratic_constraint(name=name)
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_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_0var_range_inequality(self):
"""Test InequalityToEquality converter when the var_rang of the slack variable is 0"""
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.linear_constraint(linear={
"x": 1,
"y": 1
},
sense="LE",
rhs=0,
name="xy_leq1")
op.linear_constraint(linear={
"x": 1,
"y": 1
},
sense="GE",
rhs=2,
name="xy_geq1")
op.quadratic_constraint(quadratic={("x", "x"): 1},
sense="LE",
rhs=0,
name="xy_leq2")
op.quadratic_constraint(quadratic={("x", "y"): 1},
sense="GE",
rhs=1,
name="xy_geq2")
ineq2eq = InequalityToEquality()
new_op = ineq2eq.convert(op)
self.assertEqual(new_op.get_num_vars(), 2)
self.assertTrue(
all(l_const.sense == Constraint.Sense.EQ
for l_const in new_op.linear_constraints))
self.assertTrue(
all(q_const.sense == Constraint.Sense.EQ
for q_const in new_op.quadratic_constraints))
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_sense(self):
"""Test PenalizeLinearEqualityConstraints with senses"""
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.LE, 2, "x1x2")
linear_constraint = {"x0": 1, "x2": 3}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 2, "x0x2")
self.assertEqual(op.get_num_linear_constraints(), 3)
conv = LinearEqualityToPenalty()
with self.assertRaises(QiskitOptimizationError):
conv.convert(op)
def test_inequality_mode_auto(self):
""" Test auto mode of InequalityToEqualityConverter() """
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.LE, 2, 'x1x2')
linear_constraint = {'x0': 1.1, 'x2': 2.2}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 3.3, 'x0x2')
conv = InequalityToEquality(mode='auto')
op2 = conv.convert(op)
lst = [op2.variables[3].vartype, op2.variables[4].vartype]
self.assertListEqual(lst, [Variable.Type.INTEGER, Variable.Type.CONTINUOUS])
def test_inequality_mode_continuous(self):
"""Test continuous mode of InequalityToEqualityConverter()"""
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.LE, 2, "x1x2")
linear_constraint = {"x0": 1, "x2": 3}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 2, "x0x2")
conv = InequalityToEquality(mode="continuous")
op2 = conv.convert(op)
lst = [op2.variables[3].vartype, op2.variables[4].vartype]
self.assertListEqual(
lst, [Variable.Type.CONTINUOUS, Variable.Type.CONTINUOUS])
def test_inequality_mode_integer(self):
"""Test integer mode of InequalityToEqualityConverter()"""
op = QuadraticProgram()
for i in range(3):
op.binary_var(name=f"x{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.LE, 2, "x1x2")
linear_constraint = {"x0": 1, "x2": 3}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 2, "x0x2")
conv = InequalityToEquality(mode="integer")
op2 = conv.convert(op)
lst = [op2.variables[3].vartype, op2.variables[4].vartype]
self.assertListEqual(lst,
[Variable.Type.INTEGER, Variable.Type.INTEGER])
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])
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
},
)
