We consider the problem of the recovery of a k-sparse vector from compressed linear measurements when data are corrupted by a quantization noise. When the number of measurements is not sufficiently large, different k-sparse solutions may be present in the feasible set, and the classical `1 approach may be unsuccessful. For this motivation, we propose a non-convex quadratic programming method, which exploits prior information on the magnitude of the non-zero parameters. This results in a more efficient support recovery. We provide sufficient conditions for successful recovery and numerical simulations to illustrate the practical feasibility of the proposed method.

Sparse linear regression with compressed and low-precision data via concave quadratic programming / Cerone, V.; Fosson, S.; Regruto, D.. - ELETTRONICO. - (2019), pp. 6971-6976. ((Intervento presentato al convegno IEEE Conference on Decision and Control (CDC) tenutosi a Nice (France) nel 11-13 Dicembre 2019 [10.1109/CDC40024.2019.9030257].

Sparse linear regression with compressed and low-precision data via concave quadratic programming

Cerone, V.;Fosson, S.;Regruto, D.
2019

Abstract

We consider the problem of the recovery of a k-sparse vector from compressed linear measurements when data are corrupted by a quantization noise. When the number of measurements is not sufficiently large, different k-sparse solutions may be present in the feasible set, and the classical `1 approach may be unsuccessful. For this motivation, we propose a non-convex quadratic programming method, which exploits prior information on the magnitude of the non-zero parameters. This results in a more efficient support recovery. We provide sufficient conditions for successful recovery and numerical simulations to illustrate the practical feasibility of the proposed method.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2785501