We show that the existence of a ``good'' coupling w.r.t. Hamming distance for any local Markov chain on a discrete product space implies rapid mixing of the Glauber dynamics in a blackbox fashion. More specifically, we only require the expected distance between successive iterates under the coupling to be summable, as opposed to being one-step contractive in the worst case. Combined with recent local-to-global arguments CLV21, we establish asymptotically optimal lower bounds on the standard and modified log-Sobolev constants for the Glauber dynamics for sampling from spin systems on bounded-degree graphs when a curvature condition Oll09 is satisfied. To achieve this, we use Stein's method for Markov chains BN19, RR19 to show that a ``good'' coupling for a local Markov chain yields strong bounds on the spectral independence of the distribution in the sense of ALO20. Our primary application is to sampling proper list-colorings on bounded-degree graphs. In particular, combining the coupling for the flip dynamics given by Vig00, CDMPP19 with our techniques, we show optimal O(n\log n) mixing for the Glauber dynamics for sampling proper list-colorings on any bounded-degree graph with maximum degree \Delta whenever the size of the color lists are at least \left(11{6} - \epsilon\right)\Delta, where \epsilon \approx 10^{-5} is small constant. While O(n^{2}) mixing was already known before, our approach additionally yields Chernoff-type concentration bounds for Hamming Lipschitz functions in this regime, which was not known before. Our approach is markedly different from prior works establishing spectral independence for spin systems using spatial mixing ALO20, CLV20, CGSV20, FGYZ20, which crucially is still open in this regime for proper list-colorings.
We show that the existence of a "good"' coupling w.r.t. Hamming distance for any local Markov chain on a discrete product space implies rapid mixing of the Glauber dynamics in a blackbox fashion. More specifically, we only require the expected distance between successive iterates under the coupling to be summable, as opposed to being one-step contractive in the worst case. Combined with recent local-to-global arguments CLV21, we establish asymptotically optimal lower bounds on the standard and modified log-Sobolev constants for the Glauber dynamics for sampling from spin systems on bounded-degree graphs when a curvature condition Oll09 is satisfied. To achieve this, we use Stein's method for Markov chains BN19, RR19 to show that a "good" coupling for a local Markov chain yields strong bounds on the spectral independence of the distribution in the sense of ALO20. Our primary application is to sampling proper list-colorings on bounded-degree graphs. In particular, combining the coupling for the flip dynamics given by Vig00, CDMPP19 with our techniques, we show optimal O(n\log n) mixing for the Glauber dynamics for sampling proper list-colorings on any bounded-degree graph with maximum degree \Delta whenever the size of the color lists are at least \left(11{6} - \epsilon\right)\Delta, where \epsilon \approx 10^{-5} is small constant. While O(n^{2}) mixing was already known before, our approach additionally yields Chernoff-type concentration bounds for Hamming Lipschitz functions in this regime, which was not known before. Our approach is markedly different from prior works establishing spectral independence for spin systems using spatial mixing ALO20, CLV20, CGSV20, FGYZ20, which crucially is still open in this regime for proper list-colorings.
R
``good''
"good"'
R
``good''
"good"