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Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2017, Volume 57, Number 4, Page 744
DOI: https://doi.org/10.7868/S0044466917040068 (Mi zvmmf10567) 		 
This article is cited in 4 scientific papers (total in 4 papers)

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
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DOI: https://doi.org/10.7868/S0044466917040068
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.
Received: 29.06.2015
Revised: 30.09.2015
English version:
Computational Mathematics and Mathematical Physics, 2017, Volume 57, Issue 4, Pages 739–748
DOI: https://doi.org/10.1134/S0965542517040066
Bibliographic databases:
Document Type: Article
UDC: 519.7
Language: English
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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    • This publication is cited in the following 4 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Журнал вычислительной математики и математической физики Computational Mathematics and Mathematical Physics
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