def test_equality_constraints_with_continuous_variables(self):
"""Simple example to test equality constraints with continuous variables."""
mdl = Model("eq-constraints-cts-vars")
# pylint:disable=invalid-name
v = mdl.binary_var(name="v")
w = mdl.continuous_var(name="w", lb=0.0)
t = mdl.continuous_var(name="t", lb=0.0)
mdl.minimize(v + w + t)
mdl.add_constraint(2 * v + w >= 2, "cons1")
mdl.add_constraint(w + t == 1, "cons2")
op = from_docplex_mp(mdl)
admm_params = ADMMParameters(
rho_initial=1001,
beta=1000,
factor_c=900,
maxiter=100,
three_block=True,
)
solver = ADMMOptimizer(params=admm_params)
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([0.0, 1.0, 0.0], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(1.0, solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
def test_quad_constraints(self):
"""Simple example to test quadratic constraints."""
mdl = Model("quad-constraints")
v = mdl.binary_var(name="v")
w = mdl.continuous_var(name="w", lb=0.0)
mdl.minimize(v + w)
mdl.add_constraint(v + w >= 1, "cons2")
mdl.add_constraint(v**2 + w**2 <= 1, "cons2")
op = from_docplex_mp(mdl)
admm_params = ADMMParameters(
rho_initial=1001,
beta=1000,
factor_c=900,
maxiter=100,
three_block=True,
)
solver = ADMMOptimizer(params=admm_params)
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([0.0, 1.0], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(1.0, solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
def test_admm_ex6_max(self):
"""Example 6 as maximization"""
mdl = Model('ex6-max')
# pylint:disable=invalid-name
v = mdl.binary_var(name='v')
w = mdl.binary_var(name='w')
t = mdl.binary_var(name='t')
u = mdl.continuous_var(name='u')
# mdl.minimize(v + w + t + 5 * (u - 2) ** 2)
mdl.maximize(- v - w - t - 5 * (u - 2) ** 2)
mdl.add_constraint(v + 2 * w + t + u <= 3, "cons1")
mdl.add_constraint(v + w + t >= 1, "cons2")
mdl.add_constraint(v + w == 1, "cons3")
op = QuadraticProgram()
op.from_docplex(mdl)
admm_params = ADMMParameters(
rho_initial=1001, beta=1000, factor_c=900,
maxiter=100, three_block=True, tol=1.e-6
)
solver = ADMMOptimizer(params=admm_params)
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([1., 0., 0., 2.], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(-1., solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
def test_admm_maximization(self):
"""Tests a simple maximization problem using ADMM optimizer"""
mdl = Model("simple-max")
c = mdl.continuous_var(lb=0, ub=10, name="c")
x = mdl.binary_var(name="x")
mdl.maximize(c + x * x)
op = from_docplex_mp(mdl)
admm_params = ADMMParameters()
solver = ADMMOptimizer(params=admm_params,
continuous_optimizer=CobylaOptimizer())
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([10, 0], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(10, solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
self.assertEqual(len(solution.samples), 1)
self.assertAlmostEqual(solution.fval, solution.samples[0].fval)
np.testing.assert_almost_equal(solution.x, solution.samples[0].x)
self.assertEqual(solution.status, solution.samples[0].status)
self.assertAlmostEqual(solution.samples[0].probability, 1.0)
def test_admm_ex5_warm_start(self):
"""Example 5 but with a warm start"""
mdl = Model('ex5')
# pylint:disable=invalid-name
v = mdl.binary_var(name='v')
w = mdl.binary_var(name='w')
t = mdl.binary_var(name='t')
mdl.minimize(v + w + t)
mdl.add_constraint(2 * v + 2 * w + t <= 3, "cons1")
mdl.add_constraint(v + w + t >= 1, "cons2")
mdl.add_constraint(v + w == 1, "cons3")
op = QuadraticProgram()
op.from_docplex(mdl)
admm_params = ADMMParameters(
rho_initial=1001, beta=1000, factor_c=900,
maxiter=100, three_block=False, warm_start=True
)
solver = ADMMOptimizer(params=admm_params)
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([0., 1., 0.], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(1., solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
def test_admm_ex4_no_bin_var_in_objective(self):
"""Modified Example 4 as a unit test. See the description of the previous test.
This test differs from the previous in the objective: one binary variable
is omitted in objective to test a problem when a binary variable defined but is used only
in constraints.
"""
mdl = Model("ex4")
v = mdl.binary_var(name="v")
w = mdl.binary_var(name="w")
# pylint:disable=invalid-name
t = mdl.binary_var(name="t")
b = 2
mdl.minimize(v + t)
mdl.add_constraint(2 * v + 10 * w + t <= 3, "cons1")
mdl.add_constraint(v + w + t >= b, "cons2")
op = from_docplex_mp(mdl)
admm_params = ADMMParameters(
rho_initial=1001, beta=1000, factor_c=900, maxiter=100, three_block=False
)
solver = ADMMOptimizer(params=admm_params)
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([1.0, 0.0, 1.0], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(2.0, solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
def test_admm_ex4(self):
"""Example 4 as a unit test. Example 4 is reported in:
Gambella, C., & Simonetto, A. (2020).
Multi-block ADMM Heuristics for Mixed-Binary Optimization on Classical
and Quantum Computers.
arXiv preprint arXiv:2001.02069."""
mdl = Model("ex4")
v = mdl.binary_var(name="v")
w = mdl.binary_var(name="w")
# pylint:disable=invalid-name
t = mdl.binary_var(name="t")
b = 2
mdl.minimize(v + w + t)
mdl.add_constraint(2 * v + 10 * w + t <= 3, "cons1")
mdl.add_constraint(v + w + t >= b, "cons2")
op = from_docplex_mp(mdl)
admm_params = ADMMParameters(
rho_initial=1001, beta=1000, factor_c=900, maxiter=100, three_block=False
)
solver = ADMMOptimizer(params=admm_params)
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([1.0, 0.0, 1.0], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(2.0, solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
def test_admm_setters_getters(self):
"""Tests get/set properties of ADMMOptimizer"""
optimizer = ADMMOptimizer()
self.assertEqual(optimizer.parameters.maxiter, 10)
optimizer.parameters.maxiter = 11
self.assertEqual(optimizer.parameters.maxiter, 11)
params = ADMMParameters(maxiter=12)
optimizer.parameters = params
self.assertEqual(optimizer.parameters.maxiter, 12)
def test_admm_ex6(self):
"""Example 6 as a unit test. Example 6 is reported in:
Gambella, C., & Simonetto, A. (2020).
Multi-block ADMM Heuristics for Mixed-Binary Optimization on Classical
and Quantum Computers.
arXiv preprint arXiv:2001.02069."""
mdl = Model("ex6")
# pylint:disable=invalid-name
v = mdl.binary_var(name="v")
w = mdl.binary_var(name="w")
t = mdl.binary_var(name="t")
u = mdl.continuous_var(name="u")
mdl.minimize(v + w + t + 5 * (u - 2)**2)
mdl.add_constraint(v + 2 * w + t + u <= 3, "cons1")
mdl.add_constraint(v + w + t >= 1, "cons2")
mdl.add_constraint(v + w == 1, "cons3")
op = QuadraticProgram()
op.from_docplex(mdl)
admm_params = ADMMParameters(
rho_initial=1001,
beta=1000,
factor_c=900,
maxiter=100,
three_block=True,
tol=1.0e-6,
)
solver = ADMMOptimizer(params=admm_params)
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([1.0, 0.0, 0.0, 2.0], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(1.0, solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
def test_integer_variables(self):
"""Tests ADMM with integer variables."""
mdl = Model("integer-variables")
v = mdl.integer_var(lb=5, ub=20, name="v")
w = mdl.continuous_var(name="w", lb=0.0)
mdl.minimize(v + w)
op = from_docplex_mp(mdl)
solver = ADMMOptimizer()
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([5.0, 0.0], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(5.0, solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
def test_integer_variables(self):
"""Tests ADMM with integer variables."""
mdl = Model('integer-variables')
v = mdl.integer_var(lb=5, ub=20, name='v')
w = mdl.continuous_var(name='w', lb=0.)
mdl.minimize(v + w)
op = QuadraticProgram()
op.from_docplex(mdl)
solver = ADMMOptimizer()
solution = solver.solve(op)
self.assertIsNotNone(solution)
self.assertIsInstance(solution, ADMMOptimizationResult)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([5., 0.], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(5., solution.fval, 3)
self.assertIsNotNone(solution.state)
self.assertIsInstance(solution.state, ADMMState)
