Trickle-Down in Localization Schemes and Applications

Trickle-down is a phenomenon in high-dimensional expanders with many important applications — for example, it is a key ingredient in various constructions of high-dimensional expanders or the proof of rapid mixing for the basis exchange walk on matroids and in the analysis of log-concave polynomials. We formulate a generalized trickle-down equation in the abstract context of linear-tilt localization schemes. Building on this generalization, we improve the best-known results for several Markov chain mixing or sampling problems — for example, we improve the threshold up to which Glauber dynamics is known to mix rapidly in the Sherrington-Kirkpatrick spin glass model. Other applications of our framework include near-linear time sampling algorithms from the antiferromagnetic Ising model and the fixed magnetization (antiferromagnetic or ferromagnetic) Ising model on expanders. For this application, we use a new dynamics inspired by polarization, a technique from the theory of stable polynomials.