### 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 language | English |
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Article number | 011115 |

Pages (from-to) | 011115 |

Number of pages | 1 |

Journal | Physical Review E |

Volume | 76 |

Issue number | 1 |

DOIs | |

Publication status | Published - 20 Jul 2007 |

### Bibliographical note

© 2007 The American Physical Society.## Fingerprint Dive into the research topics of 'Inference and optimization of real edges on sparse graphs: A statistical physics perspective'. Together they form a unique fingerprint.

## Cite this

*Physical Review E*,

*76*(1), 011115. [011115]. https://doi.org/10.1103/PhysRevE.76.011115