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In Compressive Sensing, the Restricted Isometry Property (RIP) ensures that robust recovery of sparse vectors is possible from noisy, undersampled measurements via computationally tractable algorithms.
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Combining geometry and combinatorics: A unified approach to sparse signal recovery
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The restricted isometry property and its implications for compressed sensing
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An introduction to compressive sampling
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On sparse reconstruction from Fourier and Gaussian measurements
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Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing
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CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
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Improved bounds on restricted isometry constants for gaussian matrices
B. Bah and J. Tanner · 2010
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Signal processing with compressive measurements
M.A. Davenport, P.T. Boufounos, M.B. Wakin, and R.G. Baraniuk · 2010
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Analysis of orthogonal matching pursuit using the restricted isometry property
M.A. Davenport and M.B. Wakin · 2010
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A numerical exploration of compressed sampling recovery
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Toeplitz compressed sensing matrices with applications to sparse channel estimation
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Generalized power method for sparse principal component analysis
M. Journée, Y. Nesterov, P. Richtárik, and R. Sepulchre · 2010
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Concentration of measure for block diagonal matrices with repeated blocks
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Concentration of measure for block diagonal matrices with applications to compressive signal processing
J.Y. Park, H.L. Yap, C.J. Rozell, and M.B. Wakin · 2011
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Alternating direction algorithms for ℓ 1 \ell_{1} -problems in compressive sensing
J. Yang and Y. Zhang · 2011
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The restricted isometry property for block diagonal matrices
H.L. Yap, A. Eftekhari, M.B. Wakin, and C.J. Rozell · 2011
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Stable Takens’ embeddings for linear dynamical systems
H.L. Yap and C.J. Rozell · 2011
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Stable manifold embeddings with operators satisfying the restricted isometry property
H.L. Yap, M.B. Wakin, and C.J. Rozell · 2011
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Yall1: Your algorithms for l1
Y. Zhang, J. Yang, and W. Yin · 2011
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Concentration of measure for block diagonal measurement matrices
M.B. Wakin, J.Y. Park, H.L. Yap, and C.J. Rozell · 2010
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On the observability of linear systems from random, compressive measurements
M.B. Wakin, B.M. Sanandaji, and T.L. Vincent · 2010
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Estimation and dynamic updating of time-varying signals with sparse variations
S.M. Asif and A.S. Charles · 2011
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Compressed sensing: How sharp is the restricted isometry property?
J. D. Blanchard, C. Cartis, and J. Tanner · 2011
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Phase transitions for greedy sparse approximation algorithms
J. D. Blanchard, C. Cartis, J. Tanner, and A. Thompson · 2011
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Compressed sensing with coherent and redundant dictionaries
E.J. Candès, Y.C. Eldar, D. Needell, and P. Randall · 2011
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Restricted isometry property in coded aperture compressive spectral imaging
H. Arguello and G. Arce · 2012
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Fast and efficient compressive sensing using structurally random matrices
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Sparse recovery algorithms: Sufficient conditions in terms of restricted isometry constants
S. Foucart · 2012
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Restricted isometries for partial random circulant matrices
H. Rauhut, J. Romberg, and J.A. Tropp · 2012
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The computational complexity of RIP, NSP, and related concepts in compressed sensing
A.M. Tillmann and M.E. Pfetsch · 2012
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Introduction to the non-asymptotic analysis of random matrices
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Dynamic filtering of sparse signals using reweighted ℓ 1 \ell_{1}
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Deterministic matrices matching the compressed sensing phase transitions of gaussian random matrices
H. Monajemi, S. Jafarpour, M. Gavish, D. L. Donoho, S. Ambikasaran, S. Bacallado, D. Bharadia, Y. Chen, Y. Choi, M. Chowdhury, et al · 2013
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