An efficient modified HS conjugate gradient algorithm in machine learning - Gonglin Yuan; Minjie Huang (Electronic Research Archive)
The Hestenes-Stiefe (HS) conjugate gradient method is very effective in resolving larger-scale sophisticated smoothing optimization tasks due to its low computational requirements and high computational efficiency. Additionally, the algorithm has been employed in practical applications to address image restoration and machine learning issues. In this paper, the authors proposed an improved Hestenes-Stiefe conjugate gradient algorithm having characteristics like: ⅰ) The algorithm depicts the decreasing features and trust region properties free of conditionalities. ⅱ) The algorithm satisfies global convergence. ⅲ) The algorithm can be applied to tackle the image restoration problem, monotone nonlinear equations, and machine learning problems. Numerical results revealed that the proffered technique is a competitive method.