• Publications
  • Influence
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
  • 3,185
  • 359
  • PDF
Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit
TLDR
This paper seeks to bridge the two major algorithmic approaches to sparse signal recovery from an incomplete set of linear measurements—L1-minimization methods and iterative methods (Matching Pursuits). Expand
  • 880
  • 82
  • PDF
Compressed Sensing with Coherent and Redundant Dictionaries
TLDR
This article presents novel results concerning the recovery of signals from undersampled data in the common situation where such signals are not sparse in an orthonormal basis or incoherent dictionary, but in a truly redundant dictionary. Expand
  • 777
  • 72
  • PDF
Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit
  • D. Needell, R. Vershynin
  • Mathematics, Computer Science
  • IEEE Journal of Selected Topics in Signal…
  • 9 December 2007
TLDR
We demonstrate a simple greedy algorithm that can reliably recover a vector <i>v</i> ¿ ¿<sup>d</sup> from incomplete and inaccurate measurements . Expand
  • 764
  • 58
  • PDF
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
Abstract Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is toExpand
  • 1,112
  • 41
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
TLDR
We obtain an improved finite-sample guarantee on the linear convergence of stochastic gradient descent for smooth and strongly convex objectives, improving from a quadratic dependence on the conditioning $$(L/\mu )^2$$ (L/μ)2 (where $$L$$L is a bound on the smoothness and $$\mu $$μ on the strong convexity) to a linear dependence on $$L/ \mu $$ L/μ. Expand
  • 325
  • 33
  • PDF
Linear Convergence of Stochastic Iterative Greedy Algorithms With Sparse Constraints
TLDR
We develop two stochastic variants of greedy algorithms for possibly non-convex optimization problems with sparsity constraints that outperform their deterministic counterparts. Expand
  • 69
  • 18
  • PDF
Paved with Good Intentions: Analysis of a Randomized Block Kaczmarz Method
TLDR
This paper introduces a randomized version of the block Kaczmarz method that converges with an expected linear rate, and we characterize the performance of this algorithm using geometric properties of the blocks of equations. Expand
  • 135
  • 16
  • PDF
Stable Image Reconstruction Using Total Variation Minimization
TLDR
This paper presents near-optimal guarantees for stable and robust image recovery from undersampled noisy measurements using total variation minimization. Expand
  • 191
  • 15
  • PDF
Signal Space CoSaMP for Sparse Recovery With Redundant Dictionaries
TLDR
Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals. Expand
  • 113
  • 12
  • PDF