Algorithms and Complexity Results for #SAT and Bayesian Inference

Authors:
Fahiem Bacchus;Shannon Dalmao;Toniann Pitassi
Affiliations:
-;-;-
Venue:
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Year:
2003

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Abstract

Bayesian inference is an important problem with numerous applications in probabilistic reasoning. Counting satisfying assignments is a closely related problem of fundamental theoretical importance. In this paper, we show that plain old DPLL equipped with memoization (an algorithm we call #DPLLCache) can solve both of these problems with time complexity that is at least as good as state-of-the-art exact algorithms, and that it can also achieve the best known time-space tradeoff. We then proceed to show that there are instances where #DPLLCache can achieve an exponential speedup over existing algorithms.