An augmented memoryless BFGS method based on a modified secant equation with application to compressed sensing-Reference-Cited by-同舟云学术

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An augmented memoryless BFGS method based on a modified secant equation with application to compressed sensing

Published:2021-09 Issue: Volume:167 Page:187-201
ISSN:0168-9274
Container-title:Applied Numerical Mathematics
language:en
Short-container-title:Applied Numerical Mathematics

Author:

Aminifard Zohre,Babaie-Kafaki Saman,Ghafoori Saeide

Funder

Iran National Science Foundation

Semnan University

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Mathematics,Numerical Analysis

Reference21 articles.

1. A modified descent Polak–Ribiére–Polyak conjugate gradient method with global convergence property for nonconvex functions;Aminifard;Calcolo,2019

2. An extended Dai–Liao conjugate gradient method with global convergence for nonconvex functions;Arazm;Glas. Mat.,2017

3. A modified scaled memoryless BFGS preconditioned conjugate gradient method for unconstrained optimization;Babaie-Kafaki;4OR,2013

4. On optimality of the parameters of self-scaling memoryless quasi–Newton updating formulae;Babaie-Kafaki;J. Optim. Theory Appl.,2015

5. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision;Black;Int. J. Comput. Vis.,1996

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