Boolean Compressive Sensing: An Approximate Trust Region reconstruction


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Hachemi, S. et Massicotte, D. (2018, September 19-20). Boolean Compressive Sensing: An Approximate Trust Region reconstruction. Dans 2018 7th International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia.

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In this paper, we propose a direct nonlinear optimization method to solve the Boolean Compressive Sensing (BCS) problem for large signals when sparsity level is unknown. While traditional CS results from linear Algebra, BCS, is given by logical operation in the Boolean workspace. To overcome this inconvenience, we relax the problem in an equivalent formulation in the Real workspace using appropriate modeling as a first step. Thereafter we turn out the problem in an unconstrained form that can be solved directly by nonlinear optimization method called Trust Region methods (TRM). Our solution is based on an Approximate version of (TRM). Numerical results are presented to sustain efficiency of our proposal.

Type de document: Document issu d'une conférence ou d'un atelier
Mots-clés libres: Boolean Workspace Compressive Sensing (CS) Approximate Trust Region Methods Nonlinear optimization
Date de dépôt: 09 mai 2022 14:08
Dernière modification: 09 mai 2022 14:08
URI: https://depot-e.uqtr.ca/id/eprint/10151

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