Name
Abstract | ||
|---|---|---|
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 implementation for graphics processing units (GPU) of hard thresholding, iterative hard thresholding, normalized iterative hard thresholding, hard thresholding pursuit, and a two-stage thresholding algorithm based on compressive sampling matching pursuit and subspace pursuit. The GPU acceleration of the former bottleneck, namely the matrix–vector multiplications, transfers a significant portion of the computational burden to the identification of the support set. The software solves high-dimensional problems in fractions of a second which permits large-scale testing at dimensions currently unavailable in the literature. The GPU implementations exhibit up to 70\(\times \) acceleration over standard Matlab central processing unit implementations using automatic multi-threading. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s12532-013-0056-5 | Math. Program. Comput. |
Keywords | Field | DocType |
Combinatorial optimization, Compressed sensing, Sparse approximation, Greedy algorithms, Graphics processing units, Parallel computing, IHT, NIHT, HTP, CoSaMP, Subspace pursuit, Primary 15–04, 65Y05, 65F30, Secondary 90C27, 94A12, 94A20 | Matching pursuit,Central processing unit,Mathematical optimization,Computer science,CUDA,Sparse approximation,Greedy algorithm,Combinatorial optimization,Thresholding,Compressed sensing | Journal |
Volume | Issue | ISSN |
5 | 3 | 1867-2957 |
Citations | PageRank | References |
16 | 1.04 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey D. Blanchard | 1 | 168 | 10.45 |
| Jared Tanner | 2 | 525 | 42.48 |