A class of diagonal quasi-newton methods for large-scale convex minimization
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...
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| Format: | Article |
| Language: | English |
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USM Publishing
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/43466/1/abstract01.pdf |
| Summary: | 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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