Message-Passing for inference and optimization of real variables on sparse graphs

Authors:
K. Y. Michael Wong;C. H. Yeung;David Saad
Affiliations:
Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China;Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China;NCRG, Aston University, Birmingham, UK
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 4
Cited 0

Linear network optimization: algorithms and codes

Linear network optimization: algorithms and codes
Learning in graphical models

Learning in graphical models
Computer networks: a systems approach

Computer networks: a systems approach
Information Theory, Inference & Learning Algorithms

Information Theory, Inference & Learning Algorithms

Quantified Score

Hi-index	0.00

Visualization

Abstract

The inference and optimization in sparse graphs with real variables is studied using methods of statistical mechanics. Efficient distributed algorithms for the resource allocation problem are devised. Numerical simulations show excellent performance and full agreement with the theoretical results.