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- Joel A. Tropp, Anna C. Gilbert
- IEEE Transactions on Information Theory
- 2007

This paper demonstrates theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous results, which require <i>O</i>(m<sup>2</sup>) measurements. The newâ€¦ (More)

- Deanna Needell, Joel A. Tropp
- Commun. ACM
- 2010

Compressive sampling (CoSa) is a new paradigm for developing data sampling technologies. It is based on the principle that many types of vector-space data are <i>compressible</i>, which is a term of art in mathematical signal processing. The key ideas are that randomized dimension reduction preserves the information in a compressible signal and that it isâ€¦ (More)

- Joel A. Tropp
- IEEE Transactions on Information Theory
- 2004

This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho's basis pursuit (BP) paradigm can recover the optimal representation of an exactly sparse signal. It leverages this theoryâ€¦ (More)

- Joel A. Tropp, Anna C. Gilbert, Martin Strauss
- Signal Processing
- 2006

A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals that participate. These elementary signals typically model coherentâ€¦ (More)

- Joel A. Tropp
- IEEE Transactions on Information Theory
- 2006

This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that has been contaminated with additive noise, the goal is toâ€¦ (More)

- Joel A. Tropp
- Foundations of Computational Mathematics
- 2012

This work presents probability inequalities for sums of independent, random, selfadjoint matrices. The results frame simple, easily verifiable hypotheses on the summands, and they yield strong conclusions about the large-deviation behavior of the maximum eigenvalue of the sum. Tail bounds for the norm of a sum of rectangular matrices follow as an immediateâ€¦ (More)

- Joel A. Tropp, Jason N. Laska, Marco F. Duarte, Justin K. Romberg, Richard G. Baraniuk
- IEEE Transactions on Information Theory
- 2010

Wideband analog signals push contemporary analog-to-digital conversion (ADC) systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficient because the signals of interest contain only a small number of significant frequencies relative to the band limit, although the locations of the frequencies may not beâ€¦ (More)

- Joel A. Tropp
- 2005

This article demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous results for OMP, which require O(m) measurements. The new resultsâ€¦ (More)

- Joel A. Tropp
- Signal Processing
- 2006

A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals that participate. These elementary signals typically model coherentâ€¦ (More)

- Joel A. Tropp, Anna C. Gilbert, Martin Strauss
- Proceedings. (ICASSP '05). IEEE Internationalâ€¦
- 2005

A simple sparse approximation problem requests an approximation of a given input signal as a linear combination of T elementary signals drawn from a large, linearly dependent collection. An important generalization is simultaneous sparse approximation. Now one must approximate several input signals at once using different linear combinations of the same Tâ€¦ (More)