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A class of diagonal quasi-newton methods for large-scale convex minimization


Leong, Wah June (2015) A class of diagonal quasi-newton methods for large-scale convex minimization. Bulletin of the Malaysian Mathematical Sciences Society. pp. 1-14. ISSN 0126-6705


We study the convergence properties of a class of low memory methods for solving large-scale unconstrained problems. This class of methods belongs to that of quasi-Newton family, except for which the approximation to Hessian, at each step, is updated by means of a diagonal matrix. Using appropriate scaling, we show that the methods can be implemented so as to be globally and \(R\) -linearly convergent with standard inexact line searches. Preliminary numerical results suggest that the methods are good alternative to other low memory methods such as the CG and spectral gradient methods.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
DOI Number: https://doi.org/10.1007/s40840-015-0117-1
Publisher: USM Publishing
Keywords: Large-scale convex minimization; Quasi-Newton methods; Diagonal updating; Scaling; Globally and R-linearly convergent
Depositing User: Mohd Hafiz Che Mahasan
Date Deposited: 28 Jun 2016 08:04
Last Modified: 28 Jun 2016 08:04
Altmetrics: httphttp://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1007/s40840-015-0117-1
URI: http://psasir.upm.edu.my/id/eprint/43466
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