Convergent propagation algorithms via oriented trees

Amir Globerson*, Tommi Jaakkola

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have previously been suggested as methods for solving these optimization problems. However, there are few convergence guarantees for such algorithms, and the algorithms are therefore not guaranteed to solve the corresponding optimization problem. Here we present an oriented tree decomposition algorithm that is guaranteed to converge to the global optimum of the Tree-Reweighted (TRW) variational problem. Our algorithm performs local updates in the convex dual of the TRW problem - an unconstrained generalized geometric program. Primal updates, also local, correspond to oriented reparametrization operations that leave the distribution intact.

Original languageEnglish
Title of host publicationProceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007
Pages133-140
Number of pages8
StatePublished - 2007
Externally publishedYes
Event23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007 - Vancouver, BC, Canada
Duration: 19 Jul 200722 Jul 2007

Publication series

NameProceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007

Conference

Conference23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007
Country/TerritoryCanada
CityVancouver, BC
Period19/07/0722/07/07

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