def test_eval_ad_nonsmooth(self):
dut = al.AugmentedLagrangianNonsmooth(prog=self.prog,
include_x_bounds=True)
x_val = InitializeAutoDiff(np.array([1., 3]))
al_val, constraint_residue, cost = dut.Eval(x=x_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_math_utils(self):
a = InitializeAutoDiff(value=[1, 2, 3],
num_derivatives=3,
deriv_num_start=0)
np.testing.assert_array_equal(ExtractValue(auto_diff_matrix=a),
np.array([[1, 2, 3]]).T)
np.testing.assert_array_equal(ExtractGradient(auto_diff_matrix=a),
np.eye(3))
a, b = InitializeAutoDiffTuple([1], [2, 3])
np.testing.assert_array_equal(ExtractValue(a), np.array([[1]]))
np.testing.assert_array_equal(ExtractValue(b), np.array([[2, 3]]).T)
np.testing.assert_array_equal(ExtractGradient(a), np.eye(1, 3))
np.testing.assert_array_equal(ExtractGradient(b),
np.hstack((np.zeros((2, 1)), np.eye(2))))
c_grad = [[2, 4, 5], [1, -1, 0]]
c = InitializeAutoDiff(value=[2, 3], gradient=c_grad)
np.testing.assert_array_equal(ExtractValue(c),
np.array([2, 3]).reshape((2, 1)))
np.testing.assert_array_equal(ExtractGradient(c), np.array(c_grad))