Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Generic Approach for Accelerating Belief Propagation based DCOP
  Algorithms via A Branch-and-Bound Technique   Belief propagation approaches, such as Max-Sum and its variants, are a kind
of important methods to solve large-scale Distributed Constraint Optimization
Problems (DCOPs). However, for problems with n-ary constraints, these
algorithms face a huge challenge since their computational complexity scales
exponentially with the number of variables a function holds. In this paper, we
present a generic and easy-to-use method based on a branch-and-bound technique
to solve the issue, called Function Decomposing and State Pruning (FDSP). We
theoretically prove that FDSP can provide monotonically non-increasing upper
bounds and speed up belief propagation based DCOP algorithms without an effect
on solution quality. Also, our empirically evaluation indicates that FDSP can
reduce 97\% of the search space at least and effectively accelerate Max-Sum,
compared with the state-of-the-art.

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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph