We propose a new gradient projection algorithm that compares favorably with the fastest algorithms available to date for ℓ1-constrained sparse recovery from noisy data, both in the compressed sensing and inverse problem frameworks. The method exploits a line-search along the feasible direction and an adaptive steplength selection based on recent strategies for the alternation of the well-known Barzilai–Borwein rules. The convergence of the proposed approach is discussed and a computational study on both well conditioned and ill-conditioned problems is carried out for performance evaluations in comparison with five other algorithms proposed in the literature.
Accelerating gradient projection methods for $ell_1$-constrained signal recovery by steplength selection rules / Loris, I; Bertero, M; DE MOL, C; Zanella, Riccardo; Zanni, Luca. - In: APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS. - ISSN 1063-5203. - STAMPA. - 27(2009), pp. 247-254.
Data di pubblicazione: | 2009 |
Titolo: | Accelerating gradient projection methods for $ell_1$-constrained signal recovery by steplength selection rules |
Autore/i: | Loris, I; Bertero, M; DE MOL, C; Zanella, Riccardo; Zanni, Luca |
Autore/i UNIMORE: | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.acha.2009.02.003. |
Rivista: | |
Volume: | 27 |
Pagina iniziale: | 247 |
Pagina finale: | 254 |
Codice identificativo ISI: | WOS:000268473400007 |
Codice identificativo Scopus: | 2-s2.0-67650436159 |
Citazione: | Accelerating gradient projection methods for $ell_1$-constrained signal recovery by steplength selection rules / Loris, I; Bertero, M; DE MOL, C; Zanella, Riccardo; Zanni, Luca. - In: APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS. - ISSN 1063-5203. - STAMPA. - 27(2009), pp. 247-254. |
Tipologia | Articolo su rivista |
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