Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2004-12-23
Phys. Rev. E 72, 015103 (2005)
Physics
Condensed Matter
Disordered Systems and Neural Networks
4 pages, 4 figures
Scientific paper
10.1103/PhysRevE.72.015103
In this letter, we show how the Survey Propagation algorithm can be generalized to include external forcing messages, and used to address selectively an exponential number of glassy ground states. These capabilities can be used to explore efficiently the space of solutions of random NP-complete constraint satisfaction problems, providing a direct experimental evidence of replica symmetry breaking in large-size instances. Finally, a new lossy data compression protocol is introduced, exploiting as a computational resource the clustered nature of the space of addressable states.
Battaglia Demian
Braunstein Alfredo
Chavas Joel
Zecchina Riccardo
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