This article is cited 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

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.

UDC: 519.7

Received: 29.06.2015
Revised: 30.09.2015

Language: English

DOI: 10.7868/S0044466917040068

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

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