New class of hybrid conjugate gradient coefficients with guaranteed descent and efficient line search

Abstract

Hybrid conjugate gradient (CG) techniques are one of the most prominent procedure for obtaining the solution of large-scale unconstrained optimization problems. This is due to its simplicity, global convergence, and low memory requirement. Numerous modifications have been done recently to improve the performance of these methods. In this paper, we proposed new class of hybrid CG coefficients with guaranteed descent under exact line search. Numerical results are presented to illustrate the efficiency of the proposed methodscompared to other classical CG coefficients.