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A Simple Proof of the Restricted Isometry Property for Random Matrices
- Richard Baraniuk, M. Davenport, R. DeVore, M. Wakin
- Mathematics
- 15 January 2008
Abstract
We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices that underlies Compressed Sensing. Our approach has two main…
Constructive Approximation
- R. DeVore, G. Lorentz
- MathematicsGrundlehren der mathematischen Wissenschaften
- 1993
TLDR
Iteratively reweighted least squares minimization for sparse recovery
- I. Daubechies, R. DeVore, M. Fornasier, C. S. Güntürk
- Mathematics
- 3 July 2008
Under certain conditions (known as the restricted isometry property, or RIP) on the m × N matrix Φ (where m < N), vectors x ∈ ℝN that are sparse (i.e., have most of their entries equal to 0) can be…
Compressed sensing and best k-term approximation
The typical paradigm for obtaining a compressed version of a discrete signal represented by a vector x ∈ R is to choose an appropriate basis, compute the coefficients of x in this basis, and then…
Nonlinear approximation
TLDR
Adaptive wavelet methods for elliptic operator equations: Convergence rates
TLDR
Adaptive Finite Element Methods with convergence rates
Summary.Adaptive Finite Element Methods for numerically solving elliptic equations are used often in practice. Only recently [12], [17] have these methods been shown to converge. However, this…
Some remarks on greedy algorithms
- R. DeVore, V. Temlyakov
- Computer Science, MathematicsAdv. Comput. Math.
- 1 December 1996
TLDR
Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage
- A. Chambolle, R. DeVore, N. Lee, B. Lucier
- MathematicsIEEE Trans. Image Process.
- 1 March 1998
TLDR
Approximation and learning by greedy algorithms
TLDR
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