Equivalence of LP Relaxation and Max-Product for Weighted Matching in General Graphs

dc.creatorSanghavi, Sujay
dc.date2007-05-05
dc.date.accessioned2026-07-07T08:16:02Z
dc.date.available2026-07-07T08:16:02Z
dc.descriptionMax-product belief propagation is a local, iterative algorithm to find the mode/MAP estimate of a probability distribution. While it has been successfully employed in a wide variety of applications, there are relatively few theoretical guarantees of convergence and correctness for general loopy graphs that may have many short cycles. Of these, even fewer provide exact ``necessary and sufficient'' characterizations. In this paper we investigate the problem of using max-product to find the maximum weight matching in an arbitrary graph with edge weights. This is done by first constructing a probability distribution whose mode corresponds to the optimal matching, and then running max-product. Weighted matching can also be posed as an integer program, for which there is an LP relaxation. This relaxation is not always tight. In this paper we show that \begin{enumerate} \item If the LP relaxation is tight, then max-product always converges, and that too to the correct answer. \item If the LP relaxation is loose, then max-product does not converge. \end{enumerate} This provides an exact, data-dependent characterization of max-product performance, and a precise connection to LP relaxation, which is a well-studied optimization technique. Also, since LP relaxation is known to be tight for bipartite graphs, our results generalize other recent results on using max-product to find weighted matchings in bipartite graphs.
dc.description6 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/0705.0760
dc.identifierhttp://arxiv.org/abs/0705.0760
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133646
dc.subjectInformation Theory
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.subjectNetworking and Internet Architecture
dc.titleEquivalence of LP Relaxation and Max-Product for Weighted Matching in General Graphs
dc.typetext

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