def METHOD_NAME(self, velocity_pc_data):
    """ Test constraint satisfaction with cvxpy.
    """
    data, pc = velocity_pc_data
    path, ss, vlim = data
    constraint_param = pc.compute_constraint_params(path, ss)
    _, _, _, _, _, _, xlimit = constraint_param
    qs = path(ss, 1)
    N = ss.shape[0] - 1
    sd = cvx.Variable()
    for i in range(0, N + 1):
        # 2. Compute max sd from the data
        constraints = [qs[i] * sd <= vlim[:, 1],
                       qs[i] * sd >= vlim[:, 0],
                       sd >= 0, sd <= JVEL_MAXSD]
        prob = cvx.Problem(cvx.Maximize(sd), constraints)
        try:
            prob.solve(solver=cvx.ECOS, abstol=1e-9)
            xmax = sd.value ** 2
            prob = cvx.Problem(cvx.Minimize(sd), constraints)
            prob.solve(solver=cvx.ECOS, abstol=1e-9)
            xmin = sd.value ** 2
        except cvx.SolverError:
            continue
        # 3. They should agree
        npt.assert_allclose([xmin, xmax], xlimit[i], atol=SMALL)
        # Assert non-negativity
        assert xlimit[i, 0] >= 0