## optimal simulation on a convex set

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].

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