def METHOD_NAME(self):
    n = 5
    lamda = 0.1
    A, x_numpy, y = self.Ax_y_setup(n, lamda)
    y = np.expand_dims(y, 1)
    A = linop.MatMul(np.expand_dims(x_numpy, 1).shape, A)
    c_norm = None
    c_max = None
    mu = 10**8  # big mu ok since no constraints used
    rho_norm = 1
    rho_max = 1
    lam = 0.1
    cg_iters = 5
    max_iter = 10
    L = []
    c = [1]
    rho = [1]
    for ii in range(len(y) - 1):
        c.append(0.00012**2)
        rho.append(0.001)
    alg_method = alg.SDMM(
        A,
        y,
        lam,
        L=L,
        c=c,
        c_max=c_max,
        c_norm=c_norm,
        mu=mu,
        rho=rho,
        rho_max=rho_max,
        rho_norm=rho_norm,
        eps_pri=10**-5,
        eps_dual=10**-2,
        max_cg_iter=cg_iters,
        max_iter=max_iter,
    )
    while not alg_method.done():
        alg_method.update()
    npt.assert_allclose(np.squeeze(abs(alg_method.x)), x_numpy)