نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه ریاضی کاربردی- دانشکده علوم ریاضی ـ دانشگاه مازندران ـ بابلسر ـ ایران
2 گروه ریاضی کاربردی - دانشکده علوم ریاضی ـ دانشگاه مازندران ـ بابلسر ـ ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
In this paper, an efficient conjugate gradient method is introduced for solving unconstrained nonsmooth optimization problems. Conjugate gradient methods are among the most popular methods for solving smooth optimization problems due to their simplicity and low memory requirements; however, their application to nonsmooth problems has received less attention. Therefore, the Lipschitz continuous objective function is first smoothed using the Moreau–Yosida regularization function. Then, a new descent direction is proposed by combining the first-order derivative information of the smoothed function with the previous descent direction. Using an inexact line search technique, it is shown that the generated direction satisfies the sufficient decrease condition and that the new iterate lies within a suitable trust region relative to the previous iterate. Moreover, the global convergence of the proposed method is guaranteed under standard assumptions. Finally, the effectiveness of this method is evaluated in the field of image recovery, and the results demonstrate its superior performance compared to existing methods.
کلیدواژهها [English]
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