On recent descent methods for large-scale optimization

Abstract

In this study, we present a synthesis of various conjugate gradient methods for solving unconstrained optimization problems, where the objective function is nonlinear but continuously differentiable (and possibly non-convex). To illustrate the performance of these methods, numerical experiments are conducted on a set of standard test functions, along with comparative analysis.