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 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_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 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_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 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_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 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_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 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)
def setUp(self): """set up the test class""" super().setUp() self.graph = nx.gnm_random_graph(5, 4, 3) op = QuadraticProgram() for _ in range(5): op.binary_var() self.result = OptimizationResult( x=[0, 0, 0, 0, 1], fval=1, variables=op.variables, status=OptimizationResultStatus.SUCCESS)
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): super().setUp() self.graph = nx.gnm_random_graph(5, 4, 3) op = QuadraticProgram() for _ in range(5): op.binary_var() self.result = OptimizationResult( x=[1, 1, 1, 1, 0], fval=4, variables=op.variables, status=OptimizationResultStatus.SUCCESS)
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_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): super().setUp() self.total_set = [1, 2, 3, 4, 5] self.list_of_subsets = [[1, 2, 3], [2, 3, 4], [4, 5], [1, 3], [2]] op = QuadraticProgram() for _ in range(5): op.binary_var() self.result = OptimizationResult( x=[0, 0, 1, 1, 1], fval=3, variables=op.variables, status=OptimizationResultStatus.SUCCESS)
def setUp(self): """Set up for the tests""" super().setUp() self.values = [10, 40, 30, 50] self.weights = [5, 4, 6, 3] self.max_weight = 10 op = QuadraticProgram() for _ in range(4): op.binary_var() self.result = OptimizationResult( x=[0, 1, 0, 1], fval=90, variables=op.variables, status=OptimizationResultStatus.SUCCESS)
def setUp(self): """Set up for the tests""" super().setUp() self.graph = nx.gnm_random_graph(4, 4, 123) op = QuadraticProgram() for _ in range(4): op.binary_var() self.result = OptimizationResult( x=[0, 1, 1, 0], fval=2, variables=op.variables, status=OptimizationResultStatus.SUCCESS)
def setUp(self): """Set up for the tests""" super().setUp() self.total_set = [1, 2, 3, 4, 5] self.list_of_subsets = [[1, 4], [2, 3, 4], [1, 5], [2, 3]] op = QuadraticProgram() for _ in range(4): op.binary_var() self.result = OptimizationResult( x=[0, 1, 1, 0], fval=2, variables=op.variables, status=OptimizationResultStatus.SUCCESS)
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 setUp(self): """Set up for the tests""" super().setUp() self.weights = [16, 9, 23] self.max_weight = 40 self.max_number_of_bins = 2 op = QuadraticProgram() for _ in range(12): op.binary_var() self.result = OptimizationResult( x=[1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1], fval=2.0, variables=op.variables, status=OptimizationResultStatus.SUCCESS, )
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_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_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_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_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_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_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])