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An efficient conjugate gradient method with strong convergence properties for non-smooth optimization | ||
| Journal of Mathematical Modeling | ||
| مقاله 4، دوره 9، شماره 3، آذر 2021، صفحه 375-390 اصل مقاله (878.49 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22124/jmm.2020.16747.1452 | ||
| نویسندگان | ||
| Fahimeh Abdollahi؛ Masoud Fatemi* | ||
| Department of Mathematics, K. N. Toosi University of Technology, Tehran, Iran | ||
| چکیده | ||
| In this paper, we introduce an efficient conjugate gradient method for solving nonsmooth optimization problems by using the Moreau-Yosida regularization approach. The search directions generated by our proposed procedure satisfy the sufficient descent property, and more importantly, belong to a suitable trust region. Our proposed method is globally convergent under mild assumptions. Our numerical comparative results on a collection of test problems show the efficiency and superiority of our proposed method. We have also examined the ability and the effectiveness of our approach for solving some real-world engineering problems from image processing field. The results confirm better performance of our method. | ||
| کلیدواژه‌ها | ||
| Conjugate gradient method؛ nonsmooth optimization؛ global convergence؛ image processing | ||
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