J. Tropp

Publications151
h-index54
Citations33,234
Highly Influential Citations3,632
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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
  • J. Tropp, A. Gilbert
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 1 August 2007
TLDR
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. Expand
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
TLDR
This paper presents a modular framework for constructing randomized algorithms that compute partial matrix decompositions. Expand
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
TLDR
This paper presents and analyzes a novel signal reconstruction algorithm, called, CoSaMP, that accomplishes the data recovery task. Expand
Greed is good: algorithmic results for sparse approximation
  • J. Tropp
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 1 October 2004
TLDR
This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. Expand
User-Friendly Tail Bounds for Sums of Random Matrices
  • J. Tropp
  • Mathematics, Computer Science
  • Found. Comput. Math.
  • 25 April 2010
TLDR
This paper presents new probability inequalities for sums of independent, random, self-adjoint matrices. Expand
Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit
TLDR
A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. Expand
Just relax: convex programming methods for identifying sparse signals in noise
  • J. Tropp
  • Mathematics, Computer Science
  • IEEE Transactions on Information Theory
  • 1 March 2006
TLDR
This paper studies a method called convex relaxation, which attempts to recover the ideal sparse signal by solving a convex program. Expand
An Introduction to Matrix Concentration Inequalities
  • J. Tropp
  • Computer Science, Mathematics
  • Found. Trends Mach. Learn.
  • 7 January 2015
TLDR
In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts. Expand
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
TLDR
This paper describes a new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components. Expand
SIGNAL RECOVERY FROM PARTIAL INFORMATION VIA ORTHOGONAL MATCHING PURSUIT
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… Expand
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