The Performance Analysis of a New Modification of Conjugate Gradient Parameter for Unconstrained Optimization Models

Conjugate Gradient (CG) method is the most prominent iterative mathematical technique that can be useful for optimization of both linear and non-linear systems due to its simplicity, low memory requirement, computational cost, global convergence properties. However, some classical CG methods have drawbacks which include weak convergence, poor numerical performance in terms number iterations CPU time. To overcome these drawbacks, researchers proposed new variants parameters with efficient results nice Some scale method, hybrid spectral three-term many more. The conjugate gradient algorithm among variant class mentioned above. interesting features modifications inherenting properties existing methods. In this paper, we a inherits Rivaie et al. (RMIL*) Dai (RMIL+) generates descent direction under strong Wolfe line search conditions. Preliminary on benchmark problems show promising.