Re:Stochastic Superoptimization [pdf]
by pavpanchekha on Monday April 28, 2014 @10:01PM
(#7664474)
As per the evaluation, STOKE works great. So if they made a hard problem harder, they clearly didn't make it too hard.
Let's stipulate that it worked great on the problem they analyzed. That problem was really very simple. If they used a more appropriate technique, they could expand the domain of applicability significantly.
Like I said, I've used MCMC for loosely-structured inference problems (but more structured than the one treated in the paper). I was doing MAP estimation (i.e., maximizing the objective function), just as in the paper.
When speaking about my work, I was once asked "Why MCMC on such a hard problem if you're really just maximizing?" It's still a good question.
Also, from a technical point of view, it seems like they started out with an optimization problem on a hard domain, and then made their problem even harder by using a sampling algorithm with stochastic moves (i.e., MCMC). The particular usefulness of MCMC is sampling, not optimization, and for this problem, you don't really want to sample.