## optimal simulation on a convex set

Posted in R, Statistics with tags , , , , , , on February 4, 2016 by xi'an

This morning, we had a jam session at the maths department of Paris-Dauphine where a few researchers & colleagues of mine presented their field of research to the whole department. Very interesting despite or thanks to the variety of topics, with forays into the three-body problem(s) [and Poincaré‘s mistake], mean fields for Nash equilibrium (or how to exit a movie theatre), approximate losses in machine learning and so on. Somehow, there was some unity as well through randomness, convexity and optimal transport. One talk close to my own interests was obviously the study of simulation within convex sets by Joseph Lehec from Paris-Dauphine [and Sébastien Bubeck & Ronen Eldan] as they had established a total variation convergence result at a speed only increasing polynomially with the dimension.  The underlying simulation algorithm is rather theoretical in that it involves random walk (or Langevin corrected) moves where any excursion outside the convex support is replaced with its projection on the set. Projection that may prove pretty expensive to compute if the convex set is defined for instance as the intersection of many hyperplanes. So I do not readily see how the scheme can be recycled into a competitor to a Metropolis-Hastings solution in that the resulting chain hits the boundary from time to time. With the same frequency over iterations. A solution is to instead use Metropolis-Hastings of course, while another one is to bounce on the boundary and then correct by Metropolis-Hastings… The optimal scales in the three different cases are quite different, from √d in the Metropolis-Hastings cases to d√d in the projection case. (I did not follow the bouncing option to the end, as it lacks a normalising constant.) Here is a quick and not particularly helpful comparison of the exploration patterns of both approaches in dimension 50 for the unit sphere and respective scales of 10/d√d [blue] and 1/√d [gold].

## sampling from time-varying log-concave distributions

Posted in Statistics, University life with tags , , , , , on October 2, 2013 by xi'an

Sasha Rakhlin from Wharton sent me this paper he wrote (and arXived) with Hariharan Narayanan on a specific Markov chain algorithm that handles sequential Monte Carlo problems for log-concave targets. By relying on novel (by my standards) mathematical techniques, they manage to obtain geometric ergodicity results for random-walk based algorithms and log-concave targets. One of the new tools is the notion of self-concordant barrier, a sort of convex potential function F associated with a reference convex set and with Lipschitz properties. The second tool is a Gaussian distribution based on the metric induced by F. The third is the Dikin walk Markov chain, which uses this Gaussian as proposal and moves almost like the Metropolis-Hastings algorithm, except that it rejects with at least a probability of ½. The scale (or step size) of the Gaussian proposal is determined by the regularity of the log-concave target. In that setting, the total variation distance between the target at the t-th level and the distribution of the Markov chain can be fairly precisely approximated. Which leads in turn to a scaling of the number of random walk steps that are necessary to ensure convergence. Depending on the pace of the moving target, a single step of the random walk may be sufficient, which is quite an interesting feature.