Treffer: Applications of correlation inequalities to low density graphical codes

Title:
Applications of correlation inequalities to low density graphical codes
Authors:
Source:
Modern problems in complexityThe European physical journal. B, Condensed matter physics. 50(1-2):51-55
Publisher Information:
Les Ulis; Berlin: EDP sciences, Springer, 2006.
Publication Year:
2006
Physical Description:
print, 13 ref
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Laboratoire de Théorie des Communications, École Polytechnique Fédérale de Lausanne, Station 14 - LTHC -EPFL, 1015 Lausanne, Switzerland
ISSN:
1434-6028
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics

Theoretical physics
Accession Number:
edscal.17752815
Database:
PASCAL Archive

Weitere Informationen

This contribution is based on the contents of a talk delivered at the Next-SigmaPhi conference held in Crete in August 2005. It is adressed to an audience of physicists with diverse horizons and does not assume any background in communications theory. Capacity approaching error correcting codes for channel communication known as Low Density Parity Check (LDPC) codes have attracted considerable attention from coding theorists in the last decade. Surprisingly strong connections with the theory of diluted spin glasses have been discovered. In this work we elucidate one new connection, namely that a class of correlation inequalities valid for Gaussian spin glasses can be applied to the theoretical analysis of LDPC codes. This allows for a rigorous comparison between the so called (optimal) maximum a posteriori and the computationaly efficient belief propagation decoders. The main ideas of the proofs are explained and we refer to recent works for the more lengthy technical details.