survey propagation
Recently Published Documents


TOTAL DOCUMENTS

27
(FIVE YEARS 3)

H-INDEX

8
(FIVE YEARS 1)

2020 ◽  
Vol 2020 (12) ◽  
pp. 124003
Author(s):  
Luca Saglietti ◽  
Yue M Lu ◽  
Carlo Lucibello

2019 ◽  
Vol 2019 (2) ◽  
pp. 023401 ◽  
Author(s):  
Fabrizio Antenucci ◽  
Florent Krzakala ◽  
Pierfrancesco Urbani ◽  
Lenka Zdeborová

2017 ◽  
Vol 47 (12) ◽  
pp. 1646-1661
Author(s):  
Jiulei JIANG ◽  
Yanhui TANG ◽  
Xiaofeng WANG ◽  
Daoyun XU

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Raffaele Marino ◽  
Giorgio Parisi ◽  
Federico Ricci-Tersenghi

2011 ◽  
Vol 22 (7) ◽  
pp. 1538-1550
Author(s):  
Ming-Hao YIN ◽  
Jun-Ping ZHOU ◽  
Ji-Gui SUN ◽  
Wen-Xiang GU

2009 ◽  
Vol 36 ◽  
pp. 229-266 ◽  
Author(s):  
H.L. Chieu ◽  
W.S. Lee

The survey propagation (SP) algorithm has been shown to work well on large instances of the random 3-SAT problem near its phase transition. It was shown that SP estimates marginals over covers that represent clusters of solutions. The SP-y algorithm generalizes SP to work on the maximum satisfiability (Max-SAT) problem, but the cover interpretation of SP does not generalize to SP-y. In this paper, we formulate the relaxed survey propagation (RSP) algorithm, which extends the SP algorithm to apply to the weighted Max-SAT problem. We show that RSP has an interpretation of estimating marginals over covers violating a set of clauses with minimal weight. This naturally generalizes the cover interpretation of SP. Empirically, we show that RSP outperforms SP-y and other state-of-the-art Max-SAT solvers on random Max-SAT instances. RSP also outperforms state-of-the-art weighted Max-SAT solvers on random weighted Max-SAT instances.


2009 ◽  
Vol 18 (6) ◽  
pp. 881-912 ◽  
Author(s):  
AMIN COJA-OGHLAN ◽  
ELCHANAN MOSSEL ◽  
DAN VILENCHIK

Belief propagation (BP) is a message-passing algorithm that computes the exact marginal distributions at every vertex of a graphical model without cycles. While BP is designed to work correctly on trees, it is routinely applied to general graphical models that may contain cycles, in which case neither convergence, nor correctness in the case of convergence is guaranteed. Nonetheless, BP has gained popularity as it seems to remain effective in many cases of interest, even when the underlying graph is ‘far’ from being a tree. However, the theoretical understanding of BP (and its new relative survey propagation) when applied to CSPs is poor.Contributing to the rigorous understanding of BP, in this paper we relate the convergence of BP to spectral properties of the graph. This encompasses a result for random graphs with a ‘planted’ solution; thus, we obtain the first rigorous result on BP for graph colouring in the case of a complex graphical structure (as opposed to trees). In particular, the analysis shows how belief propagation breaks the symmetry between the 3! possible permutations of the colour classes.


Sign in / Sign up

Export Citation Format

Share Document