Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2006-09-15
Phys. Rev. E 76, 011115 (22 pages) (2007)
Physics
Condensed Matter
Disordered Systems and Neural Networks
21 pages, 10 figures, major changes: Sections IV to VII updated, Figs. 1 to 3 replaced
Scientific paper
10.1103/PhysRevE.76.011115
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.
Michael Wong K. Y.
Saad David
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