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Preconditioned subspace quasi-newton method for large scale optimization


Citation

Sim, Hong Seng and Wah, June Leong and Abu Hassan, Malik and Ismail, Fudziah (2014) Preconditioned subspace quasi-newton method for large scale optimization. Pertanika Journal of Science & Technology, 22 (1). pp. 175-192. ISSN 0128-7680; ESSN: 2231-8526

Abstract / Synopsis

Subspace quasi-Newton (SQN) method has been widely used in large scale unconstrained optimization problem. Its popularity is due to the fact that the method can construct subproblems in low dimensions so that storage requirement as well as the computation cost can be minimized. However, the main drawback of the SQN method is that it can be very slow on certain types of non-linear problem such as ill-conditioned problems. Hence, we proposed a preconditioned SQN method, which is generally more effective than the SQN method. In order to achieve this, we proposed that a diagonal updating matrix that was derived based on the weak secant relation be used instead of the identity matrix to approximate the initial inverse Hessian. Our numerical results show that the proposed preconditioned SQN method performs better than the SQN method which is without preconditioning.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Institute for Mathematical Research
Publisher: Universiti Putra Malaysia Press
Keywords: Large scale; Limited memory quasi-Newton methods; Preconditioned; Subspace method; Unconstrained optimization
Depositing User: Najah Mohd Ali
Date Deposited: 05 Nov 2015 12:57
Last Modified: 05 Nov 2015 12:57
URI: http://psasir.upm.edu.my/id/eprint/40563
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