DeepAI

# The random 2-SAT partition function

We show that throughout the satisfiable phase the normalised number of satisfying assignments of a random 2-SAT formula converges in probability to an expression predicted by the cavity method from statistical physics. The proof is based on showing that the Belief Propagation algorithm renders the correct marginal probability that a variable is set to `true' under a uniformly random satisfying assignment.

• 8 publications
• 18 publications
• 18 publications
• 10 publications
• 8 publications
• 14 publications
• 5 publications
06/03/2021

### The Algorithmic Phase Transition of Random k-SAT for Low Degree Polynomials

Let Φ be a uniformly random k-SAT formula with n variables and m clauses...
11/04/2020

### Belief Propagation on the random k-SAT model

Corroborating a prediction from statistical physics, we prove that the B...
11/24/2018

### Streamlining Variational Inference for Constraint Satisfaction Problems

Several algorithms for solving constraint satisfaction problems are base...
02/01/2014

### Performance of the Survey Propagation-guided decimation algorithm for the random NAE-K-SAT problem

We show that the Survey Propagation-guided decimation algorithm fails to...
11/07/2022

### NSNet: A General Neural Probabilistic Framework for Satisfiability Problems

We present the Neural Satisfiability Network (NSNet), a general neural f...
11/03/2020

### The Long, the Short and the Random

We furnish solid evidence, both theoretical and empirical, towards the e...
03/25/2019

### Faster Random k-CNF Satisfiability

We describe an algorithm to solve the problem of Boolean CNF-Satisfiabil...