sampling from time-varying log-concave distributions

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.