Sampling from convex sets with a cold start using multiscale decompositions

Hariharan Narayanan, Amit Rajaraman, Piyush Srivastava

Submitted on 8 November 2022, last revised on 30 November 2022


Running a random walk in a convex body KRn 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 rB2KRB2). 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, Lov\'asz 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 both from a cold start and from a point not too close to the boundary of K.


Comment: Changes from v1: Added a corollary on mixing of coordinate hit-and-run from a point. Also includes some minor corrections/simplifications and explanations

Subjects: Computer Science - Data Structures and Algorithms; Computer Science - Computational Geometry; Mathematics - Probability