A Generic Approach to Accelerating Belief Propagation Based Incomplete Algorithms for DCOPs via a Branch-and-Bound Technique Article Swipe

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Ziyu Chen , Xingqiong Jiang , Yanchen Deng , Dingding Chen , Zhongshi He ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1609/aaai.v33i01.33016038

Belief propagation approaches, such as Max-Sum and its variants, are 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-touse 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 incomplete 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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Pruning Computer science Function (biology) Algorithm Monotonic function Constraint (computer-aided design) Branch and bound Upper and lower bounds Mathematical optimization Face (sociological concept) Belief propagation Space (punctuation) Local consistency Mathematics Constraint satisfaction Artificial intelligence Probabilistic logic Operating system Evolutionary biology Sociology Social science Decoding methods Geometry Agronomy Mathematical analysis Biology

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Type: article
Language: en
Landing Page: https://doi.org/10.1609/aaai.v33i01.33016038
PDF: https://ojs.aaai.org/index.php/AAAI/article/download/4559/4437
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Cited By: 8
References: 28
Related Works: 10
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DOI: https://doi.org/10.1609/aaai.v33i01.33016038

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Title: A Generic Approach to Accelerating Belief Propagation Based Incomplete Algorithms for DCOPs via a Branch-and-Bound Technique

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Language: en

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Publication year: 2019

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Publication date: 2019-07-17

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Authors: Ziyu Chen, Xingqiong Jiang, Yanchen Deng, Dingding Chen, Zhongshi He

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Landing page: https://doi.org/10.1609/aaai.v33i01.33016038

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Concepts: Pruning, Computer science, Function (biology), Algorithm, Monotonic function, Constraint (computer-aided design), Branch and bound, Upper and lower bounds, Mathematical optimization, Face (sociological concept), Belief propagation, Space (punctuation), Local consistency, Mathematics, Constraint satisfaction, Artificial intelligence, Probabilistic logic, Operating system, Evolutionary biology, Sociology, Social science, Decoding methods, Geometry, Agronomy, Mathematical analysis, Biology

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Cited by: 8

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Citations by year (recent): 2024: 2, 2023: 2, 2022: 1, 2021: 2, 2020: 1

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References (count): 28

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