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Accelerating gradient projection methods for l1-constrained signal recovery by steplength selection rules

par Loris, Ignace ;Bertero, Mario ;De Mol, Christine ;Zanella, Riccardo;Zanni, Luca
Référence Applied and computational harmonic analysis, 27, 2, page (247-254)
Publication Publié, 2009

Article révisé par les pairs

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Résumé :

We propose a new gradient projection algorithm that compares favorably with the fastest algorithms available to date for {$\ell_1$}-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.