Modified Hestenes - Stiefel Conjugate Gradient (MHS-CG) method for solving unconstrained optimization

Abstract

The conjugate gradient technique is one of the most effective methods for solving and minimizing unconstrained optimization problems, and it is widely utilized. In this research, we introduce a novel nonlinear conjugate gradient approach with excellent convergence for unconstrained minimization problems that is based on the nonlinear conjugate gradient method. The new algorithm has the property of descent as well as global convergence. Results from the numerical evaluations demonstrate that the new technique is very efficient in practical computing and outperforms previous comparable approaches in a wide range of conditions