Citation
Ng, Choong Boon and Leong, Wah June and Monsi, Mansor
(2014)
Diagonal preconditioned conjugate gradient algorithm for unconstrained optimization.
Pertanika Journal of Science & Technology, 22 (1).
pp. 213-224.
ISSN 0128-7680; ESSN: 2231-8526
Abstract
The nonlinear conjugate gradient (CG) methods have widely been used in solving unconstrained optimization problems. They are well-suited for large-scale optimization problems due to their low memory requirements and least computational costs. In this paper, a new diagonal preconditioned conjugate gradient (PRECG) algorithm is designed, and this is motivated by the fact that a pre-conditioner can greatly enhance the performance of the CG method. Under mild conditions, it is shown that the algorithm is globally convergent for strongly convex functions. Numerical results are presented to show that the new diagonal PRECG method works better than the standard CG method.
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Official URL or Download Paper: http://pertanika.upm.edu.my/Pertanika%20PAPERS/JST...
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Additional Metadata
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Science |
| Publisher: | Universiti Putra Malaysia Press |
| Keywords: | Conjugate gradient method; Diagonal approximation for Hessian; Preconditioning; Unconstrained optimization |
| Depositing User: | Najah Mohd Ali |
| Date Deposited: | 05 Nov 2015 06:33 |
| Last Modified: | 09 Oct 2019 08:28 |
| URI: | http://psasir.upm.edu.my/id/eprint/40566 |
| Statistic Details: | View Download Statistic |
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