Limited memory methods with improved symmetric rank-one updates and its applications on nonlinear image restoration
The iterative solution of unconstrained optimization problems has been found in a variety of significant applications of research areas, such as image restoration. In this paper, we present an efficient limited memory quasi-Newton technique based on symmetric rank-one updating formula to compute mea...
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| Main Authors: | , |
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| Format: | Article |
| Published: |
Springer Berlin Heidelberg
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/34383/ http://link.springer.com/article/10.1007%2Fs13369-014-1357-3 |
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| Institution: | Universiti Putra Malaysia |
| Summary: | The iterative solution of unconstrained optimization problems has been found in a variety of significant applications of research areas, such as image restoration. In this paper, we present an efficient limited memory quasi-Newton technique based on symmetric rank-one updating formula to compute meaningful solutions for large-scale problems arising in some image restoration problems. Numerical experiments and comparisons on various well-known methods in the literature are presented to illustrate the effectiveness of the proposed method particularly for images of large size. |
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