Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2005-04-20
Phys. Rev. Lett. 95, 038701 (2005)
Physics
Condensed Matter
Disordered Systems and Neural Networks
Scientific paper
10.1103/PhysRevLett.95.038701
We introduce a new protocol for a lossy data compression algorithm which is based on constraint satisfaction gates. We show that the theoretical capacity of algorithms built from standard parity-check gates converges exponentially fast to the Shannon's bound when the number of variables seen by each gate increases. We then generalize this approach by introducing random gates. They have theoretical performances nearly as good as parity checks, but they offer the great advantage that the encoding can be done in linear time using the Survey Inspired Decimation algorithm, a powerful algorithm for constraint satisfaction problems derived from statistical physics.
Ciliberti Stefano
Mezard Marc
Zecchina Riccardo
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