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Diagonal preconditioned conjugate gradient algorithm for unconstrained optimization


Citation

Ng, Choong Boon and Wah, June Leong 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 / Synopsis

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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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 14:33
Last Modified: 05 Nov 2015 14:33
URI: http://psasir.upm.edu.my/id/eprint/40566
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