2023/07/19 by Anari, Nima, Jain, Vishesh, Koehler, Frederic +2 · 2 citations
#Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Mathematical Physics (math-ph) #Probability (math.PR)
paper · doi:10.48550/arxiv.2307.10466
We study Glauber dynamics for sampling from discrete distributions μ on the hypercube \± 1\n. Recently, techniques based on spectral independence have successfully yielded optimal O(n) relaxation times for a host of different distributions μ. We show that spectral independence is universal: a relaxation time of O(n) implies spectral independence. We then study a notion of tractability for μ, defined in terms of smoothness of the multilinear extension of its Hamiltonian -- log μ -- over [-1,+1]n. We show that Glauber dynamics has relaxation time O(n) for such μ, and using the universality of spectral independence, we conclude that these distributions are also fractionally log-concave and consequently satisfy modified log-Sobolev inequalities. We sharpen our estimates and obtain approximate tensorization of entropy and the optimal \widetildeO(n) mixing time for random Hamiltonians, i.e. the classically studied mixed p-spin model at sufficiently high temperature. These results have significant downstream consequences for concentration of measure, statistical testing, and learning.