Convergent propagation algorithms via oriented trees

by Amir Globerson

Venue:	In UAI. 2007
Citations:	8 - 3 self

Datum	Value	Source
TITLE	Convergent propagation algorithms via oriented trees	INFERENCE
AUTHOR NAME	Amir Globerson	SVM HeaderParse 0.2
AUTHOR AFFIL	CSAIL; Massachusetts Institute of Technology	SVM HeaderParse 0.2
AUTHOR ADDR	Cambridge, MA 02139	SVM HeaderParse 0.2
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. 1	SVM HeaderParse 0.2
VENUE	In UAI. 2007	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	16 found	ParsCit 1.0