Discrete Uncertainty Principles and Sparse Signal Processing


We develop new discrete uncertainty principles in terms of numerical sparsity, which is a continuous proxy for the 0-norm. Unlike traditional sparsity, the continuity of numerical sparsity naturally accommodates functions which are nearly sparse. After studying these principles and the functions that achieve exact or near equality in them, we identify certain consequences in a number of sparse signal processing applications.

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    Recall that \(f(n)=O(g(n))\) if there exists \(C,n_0>0\) such that \(f(n)\le Cg(n)\) for all \(n>n_0\). We write \(f(n)=O_\delta (g(n))\) if the constant C is a function of \(\delta \). Also, \(f(n)=\Omega (g(n))\) if \(g(n)=O(f(n))\), and \(f(n)=o(g(n))\) if \(f(n)/g(n)\rightarrow 0\) as \(n\rightarrow \infty \).


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The authors thank Laurent Duval, Joel Tropp, and the anonymous referees for multiple suggestions that significantly improved the presentation of our results and our discussion of the relevant literature. ASB was supported by AFOSR Grant No. FA9550-12-1-0317. DGM was supported by an AFOSR Young Investigator Research Program award, NSF Grant No. DMS-1321779, and AFOSR Grant No. F4FGA05076J002. The views expressed in this article are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U.S. Government.

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Correspondence to Dustin G. Mixon.

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Communicated by Roman Vershynin.

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Bandeira, A.S., Lewis, M.E. & Mixon, D.G. Discrete Uncertainty Principles and Sparse Signal Processing. J Fourier Anal Appl 24, 935–956 (2018). https://doi.org/10.1007/s00041-017-9550-x

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  • Uncertainty principle
  • Sparsity
  • Compressed sensing