On the convergence to equilibrium of Kac’s random walk on matrices

Abstract

We consider Kac’s random walk on n-dimensional rotation matrices, where each step is a random rotation in the plane generated by two randomly picked coordinates. We show that this process converges to the Haar measure on SO(n) in the L² transportation cost (Wasserstein) metric in O(n²ln n) steps. We also prove that our bound is at most a O(ln n) factor away from optimal. Previous bounds, due to Diaconis/Saloff-Coste and Pak/Sidenko, had extra powers of n and held only for L¹ transportation cost.

Our proof method includes a general result of independent interest, akin to the path coupling method of Bubley and Dyer. Suppose that P is a Markov chain on a Polish length space (M, d) and that for all x, y∈M with d(x, y)≪1 there is a coupling (X, Y) of one step of P from x and y (resp.) that contracts distances by a (ξ+o(1)) factor on average. Then the map μ↦μP is ξ-contracting in the transportation cost metric.