Year: 2024
Type: article
Abstract: \nIn this paper, we introduce a new gradient method called the Diagonal Variable Matrix method. Our proposed method is aimed to minimize Hk+1 over the log-determinant norm subject to weak secant relation. The derived diagonal matrix Hk+1 is the approximation of the inverse Hessian matrix, which enables the calculation of the search direction, dk = −Hk+1gk, where gk denotes the gradient of the objective function. The proposed method is coupled with the backtracking Armijo line search. The proposed method is specifically designed to reduce the number of iterations and training duration, particularly in the context of solving large-dimensional problems. Finally, as a practical illustration, the proposed method is applied to solve the image deblurring problem, and its performance is analyzed using image quality metrics. The results demonstrate that the proposed method outperforms various conjugate gradient (CG) methods and multiple damping gradient method.\n
Authors: Dick Mun Chang, Hong Seng Sim, Yong Kheng Goh, Sing Yee Chua, Wah June Leong
Language: English
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Universiti Tunku Abdul Rahman
OpenAlex ID: W6903103516
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