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RIP constants for deterministic compressed sensing matrices-beyond Gershgorin

by   Arman Arian, et al.

Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized as multiplying the signal with a "measurement matrix". If the measurement satisfies the so-called restricted isometry property (RIP), then it will be appropriate to be used in compressed sensing. While a wide class of random matrices provably satisfy the RIP with high probability, explicit and deterministic constructions have been shown (so far) to satisfy the RIP only in a significantly suboptimal regime. In this paper, we propose two novel approaches for improving the RIP constant estimates based on Gershgorin circle theorem for a specific deterministic construction based on Paley tight frames, obtaining an improvement over the Gershgorin bound by a multiplicative constant. In one approach we use a recent result on the spectra of the skew-adjacency matrices of oriented graphs. In the other approach, we use the so-called Dembo bounds on the extreme eigenvalues of a positive semidefinite Hermitian matrix. We also generalize these bounds and we combine the new bounds with a conjecture we make regarding the distribution of quadratic residues in a finite field to provide a potential path to break the so-called "square-root barrier"-we provide a proof based on the assumption that the conjecture holds.


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