Sampling from Convex Sets with a Cold Start using Multiscale Decompositions

Hariharan Narayanan, Amit Rajaraman, Piyush Srivastava

Abstract: Running a random walk in a convex body K⊆ℝn is a standard approach to sample approximately uniformly from the body. The requirement is that from a suitable initial distribution, the distribution of the walk comes close to the uniform distribution πK on K after a number of steps polynomial in n and the aspect ratio R/r (i.e., when rB2 ⊆ K ⊆ RB2). Proofs of rapid mixing of such walks often require the probability density η0 of the initial distribution with respect to πK to be at most poly(n): this is called a “warm start”. Achieving a warm start often requires non-trivial pre-processing before starting the random walk. This motivates proving rapid mixing from a “cold start”, wherein η0 can be as high as exp(poly(n)). Unlike warm starts, a cold start is usually trivial to achieve. However, a random walk need not mix rapidly from a cold start: an example being the well-known “ball walk”. On the other hand, Lovász and Vempala proved that the “hit-and-run” random walk mixes rapidly from a cold start. For the related coordinate hit-and-run (CHR) walk, which has been found to be promising in computational experiments, rapid mixing from a warm start was proved only recently but the question of rapid mixing from a cold start remained open. We construct a family of random walks inspired by classical decompositions of subsets of ℝn into countably many axis-aligned dyadic cubes. We show that even with a cold start, the mixing times of these walks are bounded by a polynomial in n and the aspect ratio. Our main technical ingredient is an isoperimetric inequality for K for a metric that magnifies distances between points close to the boundary of K. As a corollary, we show that the CHR walk also mixes rapidly from a cold start.