2010

The dynamics of message passing on dense graphs, with applications to compressed sensing

Bayati, Mohsen, Montanari, Andrea

Understand

Approximate message passing algorithms proved to be extremely effective in reconstructing sparse signals from a small number of incoherent linear measurements.

  • Extensive numerical experiments further showed that their dynamics is accurately tracked by a simple one-dimensional iteration termed state evolution.
  • In this paper we provide the first rigorous foundation to state evolution.
  • We prove that indeed it holds asymptotically in the large system limit for sensing matrices with independent and identically distributed gaussian entries.

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