Hybridization is one of the popular approaches in modifying the conjugate gradient method. In this paper, a new hybrid conjugate gradient is suggested and analyzed in which the parameter k is evaluated as a convex combination of RMIL k while using exact line search. The proposed method is shown to possess both sufficient descent and global convergence properties. Numerical performances show that the proposed method is promising and has overpowered other hybrid conjugate gradient methods in its number of iterations and central processing unit per time
The conjugate gradient technique is a numerical solution strategy for finding minimization in mathem...
AbstractA modification of the Dai–Yuan conjugate gradient algorithm is proposed. Using exact line se...
Nonlinear conjugate gradient (CG) methods are extensively used as an important technique for address...
On some studies a conjugate parameter plays an important role for the conjugate gradient methods. In...
Many researchers are interested for developed and improved the conjugate gradient method for solving...
The nonlinear conjugate gradient algorithms are a very effective way in solving large-scale unconstr...
In this paper, we propose a new hybrid conjugate gradient method for unconstrained optimization prob...
Taking advantage of the attractive features of Hestenes–Stiefel and Dai–Yuan conjugate gradient meth...
Hybridizing self-adjusting approach of Dong et al. and three-term formulation of Zhang et al., a non...
In this paper, we proposed a new hybrid conjugate gradient algorithm for solving unconstrained optim...
Nonlinear conjugate gradient (CG) methods are very important for solving unconstrained optimization ...
Nonlinear conjugate gradient (CG) method holds an important role in solving large-scale unconstraine...
Abstract Conjugate gradient (CG) methods have been practically used to solve large-scale unconstrain...
Conjugate Gradient methods play an important role in solving unconstrained optimization, especially ...
The conjugate gradient method an efficient technique for solving the unconstrained optimization prob...
The conjugate gradient technique is a numerical solution strategy for finding minimization in mathem...
AbstractA modification of the Dai–Yuan conjugate gradient algorithm is proposed. Using exact line se...
Nonlinear conjugate gradient (CG) methods are extensively used as an important technique for address...
On some studies a conjugate parameter plays an important role for the conjugate gradient methods. In...
Many researchers are interested for developed and improved the conjugate gradient method for solving...
The nonlinear conjugate gradient algorithms are a very effective way in solving large-scale unconstr...
In this paper, we propose a new hybrid conjugate gradient method for unconstrained optimization prob...
Taking advantage of the attractive features of Hestenes–Stiefel and Dai–Yuan conjugate gradient meth...
Hybridizing self-adjusting approach of Dong et al. and three-term formulation of Zhang et al., a non...
In this paper, we proposed a new hybrid conjugate gradient algorithm for solving unconstrained optim...
Nonlinear conjugate gradient (CG) methods are very important for solving unconstrained optimization ...
Nonlinear conjugate gradient (CG) method holds an important role in solving large-scale unconstraine...
Abstract Conjugate gradient (CG) methods have been practically used to solve large-scale unconstrain...
Conjugate Gradient methods play an important role in solving unconstrained optimization, especially ...
The conjugate gradient method an efficient technique for solving the unconstrained optimization prob...
The conjugate gradient technique is a numerical solution strategy for finding minimization in mathem...
AbstractA modification of the Dai–Yuan conjugate gradient algorithm is proposed. Using exact line se...
Nonlinear conjugate gradient (CG) methods are extensively used as an important technique for address...