Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2003-10-18
Physics
Condensed Matter
Disordered Systems and Neural Networks
10 pages, 3 figures
Scientific paper
10.1103/PhysRevE.69.035105
We study an ill-posed linear inverse problem, where a binary sequence will be reproduced using a sparce matrix. According to the previous study, this model can theoretically provide an optimal compression scheme for an arbitrary distortion level, though the encoding procedure remains an NP-complete problem. In this paper, we focus on the consistency condition for a dynamics model of Markov-type to derive an iterative algorithm, following the steps of Thouless-Anderson-Palmer's. Numerical results show that the algorithm can empirically saturate the theoretical limit for the sparse construction of our codes, which also is very close to the rate-distortion function.
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