Dynamical Functional Theory for Compressed Sensing

Burak Cakmak, Manfred Opper, Ole Winther, Bernard H. Fleury

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We introduce a theoretical approach for designing generalizations of the approximate message passing (AMP) algorithm for compressed sensing which are valid for large observation matrices that are drawn from an invariant random matrix ensemble. By design, the fixed points of the algorithm obey the Thouless-Anderson-Palmer (TAP) equations corresponding to the ensemble. Using a dynamical functional approach we are able to derive an effective stochastic process for the marginal statistics of a single component of the dynamics. This allows us to design memory terms in the algorithm in such a way that the resulting fields become Gaussian random variables allowing for an explicit analysis. The asymptotic statistics of these fields are consistent with the replica ansatz of the compressed sensing problem.
Original languageEnglish
Title of host publication2017 IEEE International Symposium on Information Theory (ISIT)
Number of pages5
Publication date2017
Publication statusPublished - 2017
Event2017 IEEE International Symposium on Information Theory - Aachen, Germany
Duration: 25 Jun 201730 Jun 2017


Conference2017 IEEE International Symposium on Information Theory
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