Phase transitions for greedy sparse approximation algorithms by Tanner, J, Blanchard, J, Cartis, C, Thompson, A

Performance Comparisons of Greedy Algorithms in Compressed Sensing by Blanchard, J, Tanner, J

“…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 fewest number of nonzeros possible, referred to as the sparsest solution. …”

Conjugate Gradient Iterative Hard Thresholding: Observed Noise Stability for Compressed Sensing by Blanchard, J, Tanner, J, Wei, K

“…Conjugate Gradient Iterative Hard Thresholding (CGIHT) for compressed sensing combines the low per iteration complexity of fast greedy sparse approximation algorithms with the improved convergence rates of more complicated, projection based algorithms. …”

Performance comparisons of greedy algorithms in compressed sensing by Blanchard, J, Tanner, J

“…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 fewest number of nonzeros possible, referred to as the sparsest solution. …”

Performance comparisons of greedy algorithms in compressed sensing by Blanchard, J, Tanner, J

“…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 fewest number of nonzeros possible, referred to as the sparsest solution. …”

Conjugate Gradient Iterative Hard Thresholding: Observed Noise Stability for Compressed Sensing by Blanchard, J, Tanner, J, Wei, K

“…Conjugate gradient iterative hard thresholding (CGIHT) for compressed sensing combines the low per iteration computational cost of simple line search iterative hard thresholding algorithms with the improved convergence rates of more sophisticated sparse approximation algorithms. This paper shows that the average case performance of CGIHT is robust to additive noise well beyond its theoretical worst case guarantees and, in this setting, is typically the fastest iterative hard thresholding algorithm for sparse approximation. …”