def test_cobyla_optimizer_with_variable_bounds(self):
"""Cobyla Optimizer Test With Variable Bounds."""
# initialize optimizer
cobyla = CobylaOptimizer()
# initialize problem
problem = QuadraticProgram()
# set variables and bounds
problem.continuous_var(lowerbound=-1, upperbound=1)
problem.continuous_var(lowerbound=-2, upperbound=2)
# set objective and minimize
problem.minimize(linear=[1, 1])
# solve problem with cobyla
result = cobyla.solve(problem)
# analyze results
self.assertAlmostEqual(result.x[0], -1.0, places=6)
self.assertAlmostEqual(result.x[1], -2.0, places=6)
# set objective and minimize
problem.maximize(linear=[1, 1])
# solve problem with cobyla
result = cobyla.solve(problem)
# analyze results
self.assertAlmostEqual(result.x[0], 1.0, places=6)
self.assertAlmostEqual(result.x[1], 2.0, places=6)
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_cobyla_optimizer_with_trials(self):
"""Cobyla Optimizer Test."""
# load optimization problem
problem = QuadraticProgram()
problem.continuous_var(upperbound=4)
problem.continuous_var(upperbound=4)
problem.linear_constraint(linear=[1, 1], sense="=", rhs=2)
problem.minimize(linear=[2, 2], quadratic=[[2, 0.25], [0.25, 0.5]])
# solve problem with cobyla
cobyla = CobylaOptimizer(trials=3)
result = cobyla.solve(problem)
# analyze results
self.assertAlmostEqual(result.fval, 5.8750)
def test_cobyla_optimizer_with_quadratic_constraint(self):
"""Cobyla Optimizer Test With Quadratic Constraints."""
# load optimization problem
problem = QuadraticProgram()
problem.continuous_var(upperbound=1)
problem.continuous_var(upperbound=1)
problem.minimize(linear=[1, 1])
linear = [-1, -1]
quadratic = [[1, 0], [0, 1]]
problem.quadratic_constraint(linear=linear,
quadratic=quadratic,
rhs=-1 / 2)
# solve problem with cobyla
cobyla = CobylaOptimizer()
result = cobyla.solve(problem)
# analyze results
self.assertAlmostEqual(result.fval, 1.0, places=2)
def test_cobyla_optimizer_with_invalid_num_trials(self):
"""Cobyla Optimizer Test with an invalid number of trials."""
with self.assertRaises(ValueError):
_ = CobylaOptimizer(trials=0)
with self.assertRaises(ValueError):
_ = CobylaOptimizer(trials=-1)
with self.assertRaises(ValueError):
cobyla = CobylaOptimizer()
cobyla.trials = 0
with self.assertRaises(ValueError):
cobyla = CobylaOptimizer()
cobyla.trials = -1