Conjugate gradient method for solving unconstrained optimization problems: A new investigation and application

Conjugate gradient (CG) is a simple and inexpensive method for solving large-scale unconstrained optimization problems. A new value for the Dai-Liao formula’s parameter is offered based on this property. For the sake of this discussion, the following characteristics are relevant: descent conditions and global convergence may be found for the Wolfe-Powell line. The algorithm provided here outperforms other techniques. The conjugate gradient was also utilized in regression analysis and showed a better result than least squares and trend lines.