Solving large scale unconstrained optimization problems with an efficient conjugate gradient class

Abstract

‎The main goal of this paper is to introduce an appropriate conjugate gradient class to solve unconstrained optimization problems‎. ‎The presented class enjoys the benefits of having three free parameters‎, ‎its directions are descent and it can fulfill the Dai-Liao conjugacy condition‎. ‎Global convergence property of the new class is proved under weak-Wolfe-Powell line search technique‎. ‎Numerical efficiency of the proposed class is confirmed in two sets of experiments including 210 test problems and ten disparate conjugate gradient methods‎. 2020 MSC: 90C06, 90C30, 90C26

Solving Large-Scale Unconstrained Optimization Problems with an Efficient Conjugate Gradient Class

“…We indicate that our class inherits all of the superb properties of methods ( 6) and (7). Furthermore, we illustrate the advantages of using the new class by running multitudinous numerical competitions [38].…”