Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Threshold values of random K-SAT from the cavity method
Random Structures & Algorithms
The probabilistic analysis of a greedy satisfiability algorithm
Random Structures & Algorithms
A new look at survey propagation and its generalizations
Journal of the ACM (JACM)
Leveraging belief propagation, backtrack search, and statistics for model counting
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Characterizing propagation methods for boolean satisfiability
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Relaxed survey propagation for the weighted maximum satisfiability problem
Journal of Artificial Intelligence Research
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
The decimation process in random k-SAT
ICALP'11 Proceedings of the 38th international colloquim conference on Automata, languages and programming - Volume Part I
On belief propagation guided decimation for random k-SAT
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Explaining adaptation in genetic algorithms with uniform crossover: the hyperclimbing hypothesis
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Explaining optimization in genetic algorithms with uniform crossover
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Going after the k-SAT threshold
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
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Decimation is a simple process for solving constraint satisfaction problems, by repeatedly fixing variable values and simplifying without reconsidering earlier decisions. We investigate different decimation strategies, contrasting those based on local, syntactic information from those based on message passing, such as statistical physics based Survey Propagation (SP) and the related and more well-known Belief Propagation (BP). Our results reveal that once we resolve convergence issues, BP itself can solve fairly hard random k-SAT formulas through decimation; the gap between BP and SP narrows down quickly as k increases. We also investigate observable differences between BP/SP and other common CSP heuristics as decimation proceeds, exploring the hardness of the decimated formulas and identifying a somewhat unexpected feature of message passing heuristics, namely, unlike other heuristics for satisfiability, they avoid unit propagation as variables are fixed.