def test_lagrangian_size(self):
self.assertEqual(
al.AugmentedLagrangianNonsmooth(
prog=self.prog, include_x_bounds=True).lagrangian_size(), 1)
self.assertEqual(
al.AugmentedLagrangianNonsmooth(
prog=self.prog, include_x_bounds=False).lagrangian_size(), 1)
self.assertEqual(
al.AugmentedLagrangianSmooth(
prog=self.prog, include_x_bounds=True).lagrangian_size(), 1)
self.assertEqual(
al.AugmentedLagrangianSmooth(
prog=self.prog, include_x_bounds=False).lagrangian_size(), 1)
def test_is_equality(self):
self.assertEqual(
al.AugmentedLagrangianNonsmooth(
prog=self.prog, include_x_bounds=True).is_equality(), [False])
self.assertEqual(
al.AugmentedLagrangianSmooth(prog=self.prog,
include_x_bounds=True).is_equality(),
[False])
def test_eval_ad_smooth(self):
dut = al.AugmentedLagrangianSmooth(prog=self.prog,
include_x_bounds=True)
x_val = InitializeAutoDiff(np.array([1., 3]))
s_val = np.array([AutoDiffXd(1.)])
al_val, constraint_residue, cost = dut.Eval(x=x_val,
s=s_val,
lambda_val=np.array([0.5]),
mu=0.1)
self.assertIsInstance(al_val, AutoDiffXd)
self.assertIsInstance(constraint_residue, np.ndarray)
self.assertIsInstance(cost, AutoDiffXd)
def test_eval_double_smooth(self):
dut = al.AugmentedLagrangianSmooth(prog=self.prog,
include_x_bounds=True)
self.assertEqual(dut.s_size(), 1)
x_val = np.array([1., 3])
s_val = np.array([3.])
lambda_val = np.array([0.5])
al_val, constraint_residue, cost = dut.Eval(x=x_val,
s=s_val,
lambda_val=lambda_val,
mu=0.1)
self.assertIsInstance(al_val, float)
self.assertIsInstance(constraint_residue, np.ndarray)
self.assertIsInstance(cost, float)