Simplifying generalized belief propagation on redundant region graphs
Wang, C; Zhou, HJ
2013
会议日期JUL 28-30, 2013
会议地点Sapporo, JAPAN
关键词CLUSTER VARIATION METHOD MODEL STATISTICS
卷号473
DOI10.1088/1742-6596/473/1/012004
页码12004
英文摘要The cluster variation method has been developed into a general theoretical framework for treating short-range correlations in many-body systems after it was first proposed by Kikuchi in 1951. On the numerical side, a message-passing approach called generalized belief propagation (GBP) was proposed by Yedidia, Freeman and Weiss about a decade ago as a way of computing the minimal value of the cluster variational free energy and the marginal distributions of clusters of variables. However the GBP equations are often redundant, and it is quite a non-trivial task to make the GBP iteration converges to a fixed point. These drawbacks hinder the application of the GBP approach to finite-dimensional frustrated and disordered systems. In this work we report an alternative and simple derivation of the GBP equations starting from the partition function. Based on this derivation we propose a natural and systematic way of removing the redundance of the GBP equations. We apply the simplified generalized belief propagation (SGBP) equations to the two-dimensional and the three-dimensional ferromagnetic Ising model and Edwards-Anderson spin glass model. The numerical results confirm that the SGBP message-passing approach is able to achieve satisfactory performance on these model systems. We also suggest that a subset of the SGBP equations can be neglected in the numerical iteration process without affecting the final results.
会议录ELC INTERNATIONAL MEETING ON INFERENCE, COMPUTATION, AND SPIN GLASSES (ICSG2013)
会议录出版者IOP PUBLISHING LTD
会议录出版地BRISTOL
语种英语
URL标识查看原文
ISSN号1742-6588
WOS研究方向Physics
内容类型会议论文
源URL[http://ir.itp.ac.cn/handle/311006/23603]  
专题SCI会议论文
作者单位1.Chinese Acad Sci, Inst Theoret Phys, State Key Lab Theoret Phys, Beijing 100190, Peoples R China
2.[Wang, Chuang
推荐引用方式
GB/T 7714
Wang, C,Zhou, HJ. Simplifying generalized belief propagation on redundant region graphs[C]. 见:. Sapporo, JAPAN. JUL 28-30, 2013.
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