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Carsten Peterson

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Neural networks and NP-complete problems; a performance study of the graph bisectioning problem

Författare

  • Carsten Peterson
  • James R Anderson

Summary, in English

Th e performance of a mean field th eory (MFT) neu ral
network technique for finding approximate solutions to optimi zation
problems is invest igat ed for the case of th e minimum cut graph bisectio
n problem, which is NP- complete. We address the issues of solut ion
quality, programming complexity, convergence tim es and scala bility.
Both standard random gr aphs an d mor e st ruct ured geomet ric graphs
are considered. We find very encouraging res ult s for all t hese aspects
for bisection of graphs with sizes rang ing from 20 to 2000 vertice s. Solution
quality app ear s to be competitive with ot her methods, and the
effort required to apply th e MFT method is minimal . Although th e
MFT neural network approach is inh erently a parallel method , we find
that t he MFT algorithm executes in less tim e than ot her approaches
even when it is simula ted in a serial manner .

Avdelning/ar

  • Beräkningsvetenskap för hälsa och miljö
  • Centrum för miljö- och klimatvetenskap (CEC)
  • Department of Astronomy and Theoretical Physics - Has been reorganised

Publiceringsår

1989

Språk

Engelska

Sidor

59-89

Publikation/Tidskrift/Serie

Complex Systems

Volym

2

Avvikelse

1

Dokumenttyp

Artikel i tidskrift

Ämne

  • Bioinformatics (Computational Biology)

Aktiv

Published

Forskningsgrupp

  • Computational Science for Health and Environment