Stochastic Boolean Satisfiability
Journal of Automated Reasoning
The Density of States - A Measure of the Difficulty of Optimisation Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
SAT, Local Search Dynamics and Density of States
Selected Papers from the 5th European Conference on Artificial Evolution
Counting good truth assignments of random k-SAT formulae
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Tail bounds for occupancy and the satisfiability threshold conjecture
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Towards efficient sampling: exploiting random walk strategies
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Model counting: a new strategy for obtaining good bounds
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Approximate counting by sampling the backtrack-free search space
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
From sampling to model counting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Approximating the distribution of fitness over hamming regions
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
A flat histogram method for computing the density of states of combinatorial problems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Hi-index | 0.00 |
In this paper we consider the problem of computing the density of states of a Boolean formula in CNF, a generalization of both MAX-SAT and model counting. Given a Boolean formula F, its density of states counts the number of configurations that violate exactly E clauses, for all values of E. We propose a novel Markov Chain Monte Carlo algorithm based on flat histogram methods that, despite the hardness of the problem, converges quickly to a very accurate solution. Using this method, we show the first known results on the density of states of several widely used formulas and we provide novel insights about the behavior of random 3-SAT formulas around the phase transition.