Inference and optimization of real edges on sparse graphs: A statistical physics perspective

K.Y. Michael Wong, David Saad

Research output: Contribution to journalArticle

Abstract

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

Original languageEnglish
Article number011115
Pages (from-to)011115
Number of pages1
JournalPhysical Review E
Volume76
Issue number1
DOIs
Publication statusPublished - 20 Jul 2007

Bibliographical note

© 2007 The American Physical Society.

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