Sparse decomposition by iterating Lipschitzian-type mappings

Journal article published in 2016 by Alessandro Adamo, Giuliano Grossi, Raffaella LanzarottiORCID, Jianyi LinORCID

Full text: Unavailable

Publisher: Elsevier

Preprint: archiving allowed. Upload

Postprint: archiving allowed. Upload

Published version: archiving forbidden. Upload

Policy details (opens in a new window). Data provided by SHERPA/RoMEO

Contact authors Contact

combines nonconvex Lipschitzian-type mappings with canonical orthogonal projectors. The former are aimed at uniformly enhancing the sparsity level by shrinkage effects, the latter are used to project back onto the space of feasible solutions. The iterative process is driven by an increasing sequence of a scalar parameter that mainly contributes to approach the sparsest solutions. It is shown that the minima are locally asymptotically stable for a specific smooth . ℓ0-norm. Furthermore, it is shown that the points yielded by this iterative strategy are related to the optimal solutions measured in terms of a suitable smooth . ℓ1-norm. Numerical simulations on phase transition show that the performances of the proposed technique overcome those yielded by well known methods for sparse recovery.