Name
Title | ||
|---|---|---|
The convergence guarantee of the iterative hard thresholding algorithm with suboptimal feedbacks for large systems. |
Abstract | ||
|---|---|---|
Thresholding based iterative algorithms have the trade-off between effectiveness and optimality. Some are effective but involving sub-matrix inversions in every step of iterations. For systems of large sizes, such algorithms can be computationally expensive and/or prohibitive. The null space tuning algorithm with hard thresholding and feedbacks (NST+HT+FB) has a mean to expedite its procedure by a suboptimal feedback, in which sub-matrix inversion is replaced by an eigenvalue-based approximation. The resulting suboptimal feedback scheme becomes exceedingly effective for large system recovery problems. An adaptive algorithm based on thresholding, suboptimal feedback and null space tuning (AdptNST+HT+subOptFB) without a prior knowledge of the sparsity level is also proposed and analyzed. Convergence analysis is the focus of this article. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.aml.2019.06.001 | Applied Mathematics Letters |
Keywords | Field | DocType |
Sparse signal,Null space tuning,Thresholding,Feedback,Large-scale data | Convergence (routing),Kernel (linear algebra),Mathematical optimization,Thresholding algorithm,Inversion (meteorology),System recovery,Adaptive algorithm,Thresholding,Eigenvalues and eigenvectors,Mathematics | Journal |
Volume | ISSN | Citations |
98 | 0893-9659 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ningning Han | 1 | 1 | 0.72 |
| Shidong Li | 2 | 6 | 3.58 |
| Zhanjie Song | 3 | 11 | 3.93 |