Message passing for task redistribution on sparse graphs

K. Y. Michael Wong, David Saad, Zhuo Gao

Research output: Contribution to conferencePaper

Abstract

The problem of resource allocation in sparse graphs with real variables is studied using methods of statistical physics. An efficient distributed algorithm is devised on the basis of insight gained from the analysis and is examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.
Original languageEnglish
Pages1-8
Number of pages8
Publication statusPublished - 23 Oct 2006
EventNeural Information Processing Systems 18 -
Duration: 23 Oct 200623 Oct 2006

Conference

ConferenceNeural Information Processing Systems 18
Period23/10/0623/10/06

Fingerprint

Real variables
Message passing
Parallel algorithms
Resource allocation
Physics
Computer simulation

Keywords

  • problem of resource allocation
  • sparse graphs with real variables
  • optimal resource allocation

Cite this

Wong, K. Y. M., Saad, D., & Gao, Z. (2006). Message passing for task redistribution on sparse graphs. 1-8. Paper presented at Neural Information Processing Systems 18, .
Wong, K. Y. Michael ; Saad, David ; Gao, Zhuo. / Message passing for task redistribution on sparse graphs. Paper presented at Neural Information Processing Systems 18, .8 p.
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author = "Wong, {K. Y. Michael} and David Saad and Zhuo Gao",
year = "2006",
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Wong, KYM, Saad, D & Gao, Z 2006, 'Message passing for task redistribution on sparse graphs', Paper presented at Neural Information Processing Systems 18, 23/10/06 - 23/10/06 pp. 1-8.

Message passing for task redistribution on sparse graphs. / Wong, K. Y. Michael; Saad, David; Gao, Zhuo.

2006. 1-8 Paper presented at Neural Information Processing Systems 18, .

Research output: Contribution to conferencePaper

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AU - Saad, David

AU - Gao, Zhuo

PY - 2006/10/23

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AB - The problem of resource allocation in sparse graphs with real variables is studied using methods of statistical physics. An efficient distributed algorithm is devised on the basis of insight gained from the analysis and is examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.

KW - problem of resource allocation

KW - sparse graphs with real variables

KW - optimal resource allocation

M3 - Paper

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Wong KYM, Saad D, Gao Z. Message passing for task redistribution on sparse graphs. 2006. Paper presented at Neural Information Processing Systems 18, .