def test_inactive_constraints(self, optimization_variables):
l, Q, P = optimization_variables
C = np.eye(len(l))
x0 = np.zeros(len(l))
d = 1e3 * np.ones(C.shape[0])
x = optimization_methods._active_set_QP(l, Q, C, d, x0)
x_sp = optimize_scipy(l, Q, C, d)
assert np.linalg.norm(C.dot(x) <= d)
assert np.isclose(objective(l, Q, 0, x), objective(l, Q, 0, x_sp),
rtol=1e-4, atol=1e-4)
def test_remove_constraint(self, optimization_variables):
optimum = np.array([2.5, 3, 1])
Q = np.array([[8, 0, 0], [0, 4, 0], [0, 0, 1]])
l = Q.dot(optimum)
C = np.vstack([np.eye(len(l)), -np.eye(len(l))])
x0 = 2 * np.ones(len(l))
d = 2 * np.ones(C.shape[0])
x = optimization_methods._active_set_QP(l, Q, C, d, x0)
x_sp = optimize_scipy(l, Q, C, d)
assert np.linalg.norm(C.dot(x) <= d + 1e-4)
assert np.isclose(objective(l, Q, 0, x), objective(l, Q, 0, x_sp),
rtol=1e-4, atol=1e-4)
def test_equality_constraints(self, optimization_variables):
l, Q, P = optimization_variables
C = np.vstack([np.eye(len(l)), -np.eye(len(l))])
A = np.ones((1, len(l)))
b = np.zeros(1)
x0 = np.zeros(len(l))
d = 0.3 * np.ones(C.shape[0])
x = optimization_methods._active_set_QP(l, Q, C, d, x0, A=A, b=b)
x_sp = optimize_scipy(l, Q, C, d, A, b)
assert np.isclose(A.dot(x), b)
assert np.linalg.norm(C.dot(x) <= d + 1e-4)
assert np.isclose(objective(l, Q, 0, x), objective(l, Q, 0, x_sp),
rtol=1e-4, atol=1e-4)