def test_slsqp_optimizer(self):
""" Generic SLSQP Optimizer Test. """
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 SLSQP
slsqp = SlsqpOptimizer(trials=3)
result = slsqp.solve(problem)
self.assertAlmostEqual(result.fval, 5.8750)
def test_slsqp_bounded(self):
"""Same as above, but a bounded test"""
problem = QuadraticProgram()
problem.continuous_var(name="x", lowerbound=2.5)
problem.continuous_var(name="y", upperbound=0.5)
problem.maximize(linear=[2, 0], quadratic=[[-1, 2], [0, -2]])
slsqp = SlsqpOptimizer()
solution = slsqp.solve(problem)
self.assertIsNotNone(solution)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([2.5, 0.5], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(0.75, solution.fval, 3)
def test_slsqp_unbounded(self):
"""Unbounded test for optimization"""
problem = QuadraticProgram()
problem.continuous_var(name="x")
problem.continuous_var(name="y")
problem.maximize(linear=[2, 0], quadratic=[[-1, 2], [0, -2]])
slsqp = SlsqpOptimizer()
solution = slsqp.solve(problem)
self.assertIsNotNone(solution)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([2., 1.], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(2., solution.fval, 3)
def test_slsqp_inequality(self):
"""A test with inequality constraint"""
problem = QuadraticProgram()
problem.continuous_var(name="x")
problem.continuous_var(name="y")
problem.linear_constraint(linear=[1, -1], sense='>=', rhs=1)
problem.maximize(linear=[2, 0], quadratic=[[-1, 2], [0, -2]])
slsqp = SlsqpOptimizer()
solution = slsqp.solve(problem)
self.assertIsNotNone(solution)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([2., 1.], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(2., solution.fval, 3)
def test_multistart_properties(self):
"""
Tests properties of MultiStartOptimizer.
Since it is an abstract class, the test is here.
"""
trials = 5
clip = 200.
slsqp = SlsqpOptimizer(trials=trials, clip=clip)
self.assertEqual(trials, slsqp.trials)
self.assertAlmostEqual(clip, slsqp.clip)
trials = 6
clip = 300.
slsqp.trials = trials
slsqp.clip = clip
self.assertEqual(trials, slsqp.trials)
self.assertAlmostEqual(clip, slsqp.clip)
def test_slsqp_optimizer_full_output(self):
""" Generic SLSQP Optimizer Test. """
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 SLSQP
slsqp = SlsqpOptimizer(trials=3, full_output=True)
result = slsqp.solve(problem)
self.assertAlmostEqual(result.fval, 5.8750)
self.assertAlmostEqual(result.fx, 5.8750)
self.assertGreaterEqual(result.its, 1)
self.assertEqual(result.imode, 0)
self.assertIsNotNone(result.smode)
def test_slsqp_optimizer_with_quadratic_constraint(self):
"""A test with equality constraint"""
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)
slsqp = SlsqpOptimizer()
solution = slsqp.solve(problem)
self.assertIsNotNone(solution)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([0.5, 0.5], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(1., solution.fval, 3)