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.
Abdollahi, F., & Fatemi, M. (2021). An efficient conjugate gradient method with strong convergence properties for non-smooth optimization. Journal of Mathematical Modeling, 9(3), 375-390. doi: 10.22124/jmm.2020.16747.1452
MLA
Fahimeh Abdollahi; Masoud Fatemi. "An efficient conjugate gradient method with strong convergence properties for non-smooth optimization". Journal of Mathematical Modeling, 9, 3, 2021, 375-390. doi: 10.22124/jmm.2020.16747.1452
HARVARD
Abdollahi, F., Fatemi, M. (2021). 'An efficient conjugate gradient method with strong convergence properties for non-smooth optimization', Journal of Mathematical Modeling, 9(3), pp. 375-390. doi: 10.22124/jmm.2020.16747.1452
VANCOUVER
Abdollahi, F., Fatemi, M. An efficient conjugate gradient method with strong convergence properties for non-smooth optimization. Journal of Mathematical Modeling, 2021; 9(3): 375-390. doi: 10.22124/jmm.2020.16747.1452
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