Fan, Yufan ; Pesavento, Marius (2024)
Tail-STELA for Fast Signal Recovery via Basis Pursuit.
13rd Sensor Array and Multichannel Signal Processing Workshop. Corvallis, USA (08.07.2024 - 11.07.2024)
doi: 10.1109/SAM60225.2024.10636404
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper, noticing the accuracy enhancement of the tail minimization technique for basis pursuit, and the faster convergence speed of the Soft-Thresholding with Exact Line Search Algorithm (STELA), we propose the Tail-STELA that combines both methods to solve the sparse signal recovery problem via the basis pursuit. Moreover, we adopt the dual decomposition method that utilizes the augmented Lagrangian to reformulate the basis pursuit. In this way, the update in the primal domain is expressed as a LASSO problem and can be solved by LASSO solvers, e.g., the STELA. We call such an approach the Tail-DD-LASSO approach and propose the Tail-DD-STELA as an instance. Simulation results show that on the one hand, the Tail-DD-STELA and the Tail-DD-FISTA achieve the highest recovery rates for different sparsity levels. On the other hand, the Tail-STELA and the Tail-FISTA achieve almost the same recovery rate at a fraction of the computation time. More specifically, the proposed Tail-STELA has the fastest convergence speed.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Fan, Yufan ; Pesavento, Marius |
| Art des Eintrags: | Bibliographie |
| Titel: | Tail-STELA for Fast Signal Recovery via Basis Pursuit |
| Sprache: | Englisch |
| Publikationsjahr: | 26 August 2024 |
| Verlag: | IEEE |
| Buchtitel: | 2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM) |
| Veranstaltungstitel: | 13rd Sensor Array and Multichannel Signal Processing Workshop |
| Veranstaltungsort: | Corvallis, USA |
| Veranstaltungsdatum: | 08.07.2024 - 11.07.2024 |
| DOI: | 10.1109/SAM60225.2024.10636404 |
| Kurzbeschreibung (Abstract): | In this paper, noticing the accuracy enhancement of the tail minimization technique for basis pursuit, and the faster convergence speed of the Soft-Thresholding with Exact Line Search Algorithm (STELA), we propose the Tail-STELA that combines both methods to solve the sparse signal recovery problem via the basis pursuit. Moreover, we adopt the dual decomposition method that utilizes the augmented Lagrangian to reformulate the basis pursuit. In this way, the update in the primal domain is expressed as a LASSO problem and can be solved by LASSO solvers, e.g., the STELA. We call such an approach the Tail-DD-LASSO approach and propose the Tail-DD-STELA as an instance. Simulation results show that on the one hand, the Tail-DD-STELA and the Tail-DD-FISTA achieve the highest recovery rates for different sparsity levels. On the other hand, the Tail-STELA and the Tail-FISTA achieve almost the same recovery rate at a fraction of the computation time. More specifically, the proposed Tail-STELA has the fastest convergence speed. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme |
| Hinterlegungsdatum: | 30 Aug 2024 07:51 |
| Letzte Änderung: | 12 Dez 2024 11:45 |
| PPN: | 524553742 |
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