  A lift-and-permute scheme of alternating direction method of multipliers
(ADMM) is proposed for linearly constrained convex programming. It contains not
only the newly developed balanced augmented Lagrangian method and its
dual-primal variation, but also the proximal ADMM and Douglas-Rachford
splitting algorithm. It helps to propose accelerated algorithms with worst-case
$O(1/k^2)$ convergence rates in the case that the objective function to be
minimized is strongly convex.
