STLS: Cycle-Cutset-Driven Local Search For MPE
نویسندگان
چکیده
In this paper we present Stochastic Tree-based Local Search or STLS, a local search algorithm combining the notion of cycle-cutsets with the well-known Belief Propagation to approximate the optimum of sums of unary and binary potentials. This is done by the previously unexplored concept of traversal from one cutset to another and updating the induced forest, thus creating a local search algorithm, whose update phase spans over all the forest variables.We study empirically two pure variants of STLS against the state-of-the art GLS scheme and against a hybrid.
منابع مشابه
STLS: Cutset-Driven Local Search For MPE
In this paper we present a cycle-cutset driven stochastic local search algorithm which approximates the optimum of sums of unary and binary potentials, called Stochastic Tree Local Search or STLS. We study empirically two pure variants of STLS against the state-of-the art GLS scheme and against a hybrid.
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