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Zh. Vychisl. Mat. Mat. Fiz., 2017, Volume 57, Number 4, Page 744 (Mi zvmmf10567)  

A conjugate subgradient algorithm with adaptive preconditioning for the least absolute shrinkage and selection operator minimization

A. Mirone, P. Paleo

European Synchrotron Radiation Facility, BP 220, F-38043 Grenoble Cedex, France

Abstract: This paper describes a new efficient conjugate subgradient algorithm which minimizes a convex function containing a least squares fidelity term and an absolute value regularization term. This method is successfully applied to the inversion of ill-conditioned linear problems, in particular for computed tomography with the dictionary learning method. A comparison with other state-of-art methods shows a significant reduction of the number of iterations, which makes this algorithm appealing for practical use.

DOI: https://doi.org/10.7868/S0044466917040068

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English version:
Computational Mathematics and Mathematical Physics, 2017, 57:4, 739–748

Bibliographic databases:

UDC: 519.7
Received: 29.06.2015
Revised: 30.09.2015
Language:

Citation: A. Mirone, P. Paleo, “A conjugate subgradient algorithm with adaptive preconditioning for the least absolute shrinkage and selection operator minimization”, Zh. Vychisl. Mat. Mat. Fiz., 57:4 (2017), 744; Comput. Math. Math. Phys., 57:4 (2017), 739–748

Citation in format AMSBIB
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  • Журнал вычислительной математики и математической физики Computational Mathematics and Mathematical Physics
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