A NEW CLASS OF NONLINEAR CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION MODELS AND ITS APPLICATION IN PORTFOLIO SELECTION

Abstract

In this paper, we propose a new conjugate gradient method for solving uncon- strained optimization models. By using exact and strong Wolfe line searches, the proposed method possesses the sufficient descent condition and global convergence properties. Numerical results show that the proposed method is efficient at small, medium, and large dimensions for the given test functions. In addition, the proposed method was applied to solve practical application problems in portfolio selection.