Citation
Abstract
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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Official URL or Download Paper: http://pertanika.upm.edu.my/Pertanika%20PAPERS/JST...
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Additional Metadata
Item Type: | Article |
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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 04:57 |
Last Modified: | 09 Oct 2019 08:26 |
URI: | http://psasir.upm.edu.my/id/eprint/40563 |
Statistic Details: | View Download Statistic |
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