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

K.Y. Michael Wong, C.H. Yeung, David Saad

Research output: Chapter in Book/Report/Conference proceedingConference publication

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.
Original languageEnglish
Title of host publicationNeural information processing
Subtitle of host publication13th international conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006. Proceedings, Part II
EditorsIrwin King, Jun Wang, Lai-Wan Chan, DeLiang Wang
Place of PublicationBerlin (DE)
PublisherSpringer
Pages754-763
Number of pages10
ISBN (Electronic)978-3-540-46482-2
ISBN (Print)978-3-540-46481-5
DOIs
Publication statusPublished - 2006
Event13th International Conference on Neural Information Processing - Hong Kong, China
Duration: 3 Oct 20066 Oct 2006

Publication series

NameLecture notes in computer science
PublisherSpringer
Volume4233
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Neural Information Processing
Abbreviated titleICONIP 2006
CountryChina
CityHong Kong
Period3/10/066/10/06

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  • Cite this

    Wong, K. Y. M., Yeung, C. H., & Saad, D. (2006). Message-passing for inference and optimization of real variables on sparse graphs. In I. King, J. Wang, L-W. Chan, & D. Wang (Eds.), Neural information processing: 13th international conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006. Proceedings, Part II (pp. 754-763). (Lecture notes in computer science; Vol. 4233). Springer. https://doi.org/10.1007/11893257_84