Computer Science – Information Theory
Scientific paper
2008-01-29
Computer Science
Information Theory
77 page double-spaced single-column submitted version to IEEE Transactions on Information Theory
Scientific paper
The Survey Propagation (SP) algorithm for solving $k$-SAT problems has been shown recently as an instance of the Belief Propagation (BP) algorithm. In this paper, we show that for general constraint-satisfaction problems, SP may not be reducible from BP. We also establish the conditions under which such a reduction is possible. Along our development, we present a unification of the existing SP algorithms in terms of a probabilistically interpretable iterative procedure -- weighted Probabilistic Token Passing.
Mao Yongyi
Tu Ronghui
Zhao Jiying
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