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- Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner
- SIAM Review
- 2011

Compressed sensing (CS) seeks to recover an unknown vector with N entries by making far fewer than N measurements; it posits that the number of CS measurements should be comparable to the information… (More)

We introduce the Conjugate Gradient Iterative Hard Thresholding (CGIHT) family of algorithms for the efficient solution of constrained underdetermined linear systems of equations arising in… (More)

A major enterprise in compressed sensing and sparse approximation is the design and analysis of computationally tractable algorithms for recovering sparse, exact or approximate, solutions of… (More)

- Tolu Alabi, Jeffrey D. Blanchard, Bradley Gordon, Russel Steinbach
- ACM Journal of Experimental Algorithmics
- 2012

Finding the <it>k</it>th-largest value in a list of <it>n</it> values is a well-studied problem for which many algorithms have been proposed. A naïve approach is to sort the list and then simply… (More)

- Jeffrey D. Blanchard, Jared Tanner
- Math. Program. Comput.
- 2013

For appropriate matrix ensembles, greedy algorithms have proven to be an efficient means of solving the combinatorial optimization problem associated with compressed sensing. This paper describes an… (More)

- Jeffrey D. Blanchard, Michael Cermak, David Hanle, Yirong Jing
- IEEE Transactions on Signal Processing
- 2014

Five known greedy algorithms designed for the single measurement vector setting in compressed sensing and sparse approximation are extended to the multiple measurement vector scenario: Iterative Hard… (More)

Consider a measurement matrix A of size n×N , with n < N , y a signal in RN , and b = Ay the observed measurement of the vector y. From knowledge of (b, A), compressed sensing seeks to recover the… (More)

Currently there is no framework for the transparent comparison of sparse approximation recoverability results derived using different methods of analysis. We cast some of the most recent… (More)

- Jeffrey D. Blanchard, Jared Tanner
- Numerical Lin. Alg. with Applic.
- 2015

Compressed sensing has motivated the development of numerous sparse approximation algorithms designed to return a solution to an underdetermined system of linear equations where the solution has the… (More)

Consider a measurement matrix A of size n×N , with n < N , y a signal in RN , and b = Ay the observed measurement of the vector y. From knowledge of (b, A), compressed sensing seeks to recover the… (More)