Approximate Counting, the Lovasz Local Lemma and Inference in Graphical Models
This addresses a fundamental challenge in computational complexity and inference for researchers in theoretical computer science and machine learning, representing a significant departure from prior methods.
The paper tackles the problem of approximately counting solutions to CNF formulas with higher-order constraints, closing an exponential gap between known bounds by introducing an algorithm that works under logarithmic width relative to maximum degree, and extends to approximate sampling and graphical model inference.
In this paper we introduce a new approach for approximately counting in bounded degree systems with higher-order constraints. Our main result is an algorithm to approximately count the number of solutions to a CNF formula $Φ$ when the width is logarithmic in the maximum degree. This closes an exponential gap between the known upper and lower bounds. Moreover our algorithm extends straightforwardly to approximate sampling, which shows that under Lovász Local Lemma-like conditions it is not only possible to find a satisfying assignment, it is also possible to generate one approximately uniformly at random from the set of all satisfying assignments. Our approach is a significant departure from earlier techniques in approximate counting, and is based on a framework to bootstrap an oracle for computing marginal probabilities on individual variables. Finally, we give an application of our results to show that it is algorithmically possible to sample from the posterior distribution in an interesting class of graphical models.