  The time to converge to the steady state of a finite Markov chain can be
greatly reduced by a lifting operation, which creates a new Markov chain on an
expanded state space. For a class of quadratic objectives, we show an analogous
behavior where a distributed ADMM algorithm can be seen as a lifting of
Gradient Descent algorithm. This provides a deep insight for its faster
convergence rate under optimal parameter tuning. We conjecture that this gain
is always present, as opposed to the lifting of a Markov chain which sometimes
only provides a marginal speedup.
