Two-versions of descent conjugate gradient methods for large-scale unconstrained optimization

<p>The conjugate gradient methods are noted to be exceedingly valuable for solving large-scale unconstrained optimization problems since it needn't the storage of matrices. Mostly parameter is focus methods. The current paper proposes new type solve optimization. A Hessian approximation in a diagonal matrix form on basis second and third-order Taylor series expansion was employed this study. sufficient descent property proposed algorithm proved. method converged globally. This found competitive fletcher-reeves (FR) number numerical experiments.</p>