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A Hybrid Conjugate Gradient Method Between MLS and FR in Nonparametric Statistics | ||
| Communications in Combinatorics and Optimization | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 12 آذر 1402 اصل مقاله (1.38 M) | ||
| نوع مقاله: Original paper | ||
| شناسه دیجیتال (DOI): 10.22049/cco.2023.28935.1784 | ||
| نویسندگان | ||
| Imane Guefassa* 1؛ Yacine Chaib2؛ Tahar Bechouat3 | ||
| 1Laboratory Informatics and Mathematics (LIM) Mohamed Cherif Messaadia University, Souk Ahras, Algeria | ||
| 2Laboratory Informatics and Mathematics (LIM) Mohamed Cherif Messaadia University, Souk Ahras, Algeria | ||
| 3Mohamed Cherif Messaadia University, Souk Ahras, 41000, Algeria | ||
| چکیده | ||
| This paper proposes a novel hybrid conjugate gradient method for nonparametric statistical inference.The proposed method is a convex combination of the modified linear search (MLS) and Fletcher-Reeves (FR) methods, and it inherits the advantages of both methods. The FR method is known for its fast convergence, while the MLS method is known for its robustness to noise. The proposed method combines these advantages to achieve both fast convergence and robustness to noise. Our method is evaluated on a variety of nonparametric statistical problems, including kernel density estimation, regression, and classification. The results show that the new method outperforms the MLS and FR methods in terms of both accuracy and efficiency. | ||
| کلیدواژه‌ها | ||
| Hybrid conjugate gradient method؛ Strong Wolfe line search؛ Sufficient descent direction؛ Global convergence؛ Numerical comparisons | ||
| مراجع | ||
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