A MODIFIED NONLINEAR CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION AND PORTFOLIO SELECTION PROBLEMS

• Main Authors: Diphofu, T. (Author), Kaelo (Author), Tufa, A.R (Author)
• Format: Article
• Language: English
• Published: EDP Sciences 2023
• Subjects: Conjugate gradient; Conjugate gradient method; Conjugate-gradient method; Finding solutions; global convergence; Global conver-gence; Nonlinear conjugate gradient algorithms; Nonlinear programming; Numerical methods; portfolio selection; Portfolio selection; Portfolio selection problems; strong Wolfe line search; Strong wolfe line search; Unconstrained optimization; Wolfe line searches
• Online Access: View Fulltext in Publisher; View in Scopus

 Conjugate gradient methods play a vital role in finding solutions of large-scale optimization problems due to their simplicity to implement, low memory requirements and as well as their convergence properties. In this paper, we propose a new conjugate gradient method that has a direction satisfying the sufficient descent property. We establish global convergence of the new method under the strong Wolfe line search conditions. Numerical results show that the new method performs better than other relevant methods in the literature. Furthermore, we use the new method to solve a portfolio selection problem. © The authors. Published by EDP Sciences, ROADEF, SMAI 2023.