def METHOD_NAME(self):
    H = np.array([[1.812159, 0.82687265, 0.21838879, -0.52487006, 0.25436988],
                  [0.82687265, 2.66380283, 0.31508988, -0.40144163, 0.08811588],
                  [0.21838879, 0.31508988, 2.38020726, -0.3166346, 0.27363867],
                  [-0.52487006, -0.40144163, -0.3166346, 1.61927182, -0.42140166],
                  [0.25436988, 0.08811588, 0.27363867, -0.42140166, 1.33243101]])
    g = np.array([0.75798952, 0.01421945, 0.33847612, 0.83725004, -0.47909534])
    trust_radius = 1.1
    # Solve Subproblem
    subprob = KrylovQP(x=0,
                       fun=lambda x: 0,
                       jac=lambda x: g,
                       hess=lambda x: None,
                       hessp=lambda x, y: H.dot(y))
    p, hits_boundary = subprob.solve(trust_radius)
    # check kkt satisfaction
    assert_almost_equal(
            np.linalg.norm(H.dot(p) + subprob.lam * p + g),
            0.0)
    assert_array_almost_equal(p, [-0.68585435, 0.1222621, -0.22090999,
                                  -0.67005053, 0.31586769])
    assert_array_almost_equal(hits_boundary, False)