Gaussian Belief Propagation for Solving Systems of Linear Equations: Theory and Application

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dc.creatorShental, Ori
dc.creatorBickson, Danny
dc.creatorSiegel, Paul H.
dc.creatorWolf, Jack K.
dc.creatorDolev, Danny
dc.date2008-10-07
dc.date.accessioned2026-07-07T10:08:04Z
dc.date.available2026-07-07T10:08:04Z
dc.descriptionThe canonical problem of solving a system of linear equations arises in numerous contexts in information theory, communication theory, and related fields. In this contribution, we develop a solution based upon Gaussian belief propagation (GaBP) that does not involve direct matrix inversion. The iterative nature of our approach allows for a distributed message-passing implementation of the solution algorithm. We address the properties of the GaBP solver, including convergence, exactness, computational complexity, message-passing efficiency and its relation to classical solution methods. We use numerical examples and applications, like linear detection, to illustrate these properties through the use of computer simulations. This empirical study demonstrates the attractiveness (e.g., faster convergence rate) of the proposed GaBP solver in comparison to conventional linear-algebraic iterative solution methods.
dc.descriptionSubmitted to IEEE Transactions on Information Theory
dc.identifierhttps://arxiv.org/abs/0810.1119
dc.identifierhttp://arxiv.org/abs/0810.1119
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170864
dc.subjectInformation Theory
dc.subjectE.4
dc.titleGaussian Belief Propagation for Solving Systems of Linear Equations: Theory and Application
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