A hybrid conjugate gradient method between MLS and FR in nonparametric statistics

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Guefassa Imane; Chaib Yacine; Bechouat Tahar

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Journal Paper

Paper Information

Journal: COMMUNICATIONS IN COMBINATORICS AND OPTIMIZATION Year:2025 | Volume:10 | Issue:2 Page(s): 405-421

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Title

A hybrid conjugate gradient method between MLS and FR in nonparametric statistics

Author(s)

Guefassa Imane | Chaib Yacine | Bechouat Tahar | Issue Writer Certificate

Keywords

Hybrid conjugate gradient method

Strong Wolfe line search

Sufficient descent direction

Global convergence

Numerical comparisons

Abstract

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.

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