![Geometry and Computation in High Dimensions.png](/sites/default/files/styles/workshop_banner_sm_1x/public/2023-05/Geometry%20and%20Computation%20in%20High%20Dimensions.png.jpg?itok=1JtiYLWR)
Abstract
We give an efficient algorithm to approximately count the solutions in the random $k$-SAT model when the density of the formula scales exponentially with $k$. The best previous counting algorithm was due to Montanari and Shah and was based on the correlation decay method, which works up to densities $(1+o_k(1))\frac{2\log k}{k}$, the Gibbs uniqueness threshold for the model. Instead, our algorithm harnesses a recent technique by Moitra to work for random formulas with much higher densities. The main challenge in our setting is to account for the presence of high-degree variables whose marginal distributions are hard to control and which cause significant correlations within the formula.