A new nonlinear conjugate gradient coefficient for unconstrained optimization

Abstract

In this paper, we suggest a new nonlinear conjugate gradient method for solving large scale unconstrained optimization problems. We prove that the new conjugate gradient coefficient k β with exact line search is globally convergent. Preliminary numerical results with a set of 116 unconstrained optimization problems show that k β is very promising and efficient when compared to the other conjugate gradient coefficients Fletcher-Reeves) (FR and Polak-Ribiere-Polyak) (PRP .

A conjugate gradient method with strong Wolfe-Powell line search for unconstrained optimization

“…During the last decade, much effort has been devoted to developing new modifications of conjugate gradient methods which do not only possess strong convergence properties, but they are also computationally superior to the classical methods. Such methods can be found in [18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Estimating the unemployment rate using least square and conjugate gradient methods

“…There are several of CG methods introduced by previous researchers. Details explanation of them can be found from [12][13][14][15][16][17][18][19]. In this paper, both of the methods mentioned earlier are going to be used in estimating the unemployment rate which will be discuss later in the next sections.…”

The Sufficient Descent Condition of Nonlinear Conjugate Gradient Method

“…Numerous studies have been done recently to improve these methods in order to find the most efficient method. Some CG have been proposed such as Fletcher and Reeves [2], Polak and Ribiere [3], Fletcher [4], Liu and Storey [5], Dai and Yuan [6], Rivaie et al [7], Hamoda et al [8] and Abashar et al [9]. An unconstrained optimization problem is given as min { ( ): ∈ }…”