Algorithms and complexity results for #SAT and Bayesian inference

被引：77
|
作者：
Bacchus, F [1 ]
Dalmao, S [1 ]
Pitassi, T [1 ]
机构：
[1] Univ Toronto, Dept Comp Sci, Toronto, ON M5S 3G4, Canada
来源：
44TH ANNUAL IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, PROCEEDINGS | 2003年
关键词：
D O I：
10.1109/SFCS.2003.1238208
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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 cat, 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.
引用
收藏
页码：340 / 351
页数：12
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