Entropic Independence in High-Dimensional Expanders: Modified Log-Sobolev Inequalities for Fractionally Log-Concave Polynomials and the Ising Model

Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong

We introduce a notion called entropic independence for distributions $\mu$ defined on pure simplicial complexes, i.e., subsets of size $k$ of a ground set of elements. Informally, we call a background measure $\mu$ entropically independent if for any (possibly randomly chosen) set $S$, the relative entropy of an element of $S$ drawn uniformly at random carries at most $O(1/k)$ fraction of the relative entropy of $S$, a constant multiple of its ``share of entropy.'' Entropic independence is the natural analog of spectral independence, another recently established notion, if one replaces variance by entropy. In our main result, we show that $\mu$ is entropically independent exactly when a transformed version of the generating polynomial of $\mu$ can be upper bounded by its linear tangent, a property implied by concavity of the said transformation. We further show that this concavity is equivalent to spectral independence under arbitrary external fields, an assumption that also goes by the name of fractional log-concavity. Our result can be seen as a new tool to establish entropy contraction from the much simpler variance contraction inequalities. A key differentiating feature of our result is that we make no assumptions on marginals of $\mu$ or the degrees of the underlying graphical model when $\mu$ is based on one. We leverage our results to derive tight modified log-Sobolev inequalities for multi-step down-up walks on fractionally log-concave distributions. As our main application, we establish the tight mixing time of $O(n\log n)$ for Glauber dynamics on Ising models with interaction matrix of operator norm smaller than $1$, improving upon the prior quadratic dependence on $n$.

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