We take an algorithmic approach to studying the solution space geometry of relatively
sparse random and bounded degree k-CNFs for large k. In the course of doing so, we …

We present a new framework to derandomise certain Markov chain Monte Carlo (MCMC)
algorithms. As in MCMC, we first reduce counting problems to sampling from a sequence of …

We give new algorithms based on Markov chains to sample and approximately count
satisfying assignments to k-uniform CNF formulas where each variable appears at most d …

Let Φ be a random k-CNF formula on n variables and m clauses, where each clause is a
disjunction of k literals chosen independently and uniformly. Our goal is, for most Φ, to …

We give a fast algorithm for sampling uniform solutions of general constraint satisfaction
problems (CSPs) in a local lemma regime. Ihe expected running time of our algorithm is …