A new family of conjugate gradient methods to solve unconstrained optimization problems

A conjugate optimal coefficient is a crucial characteristic of conjugate gradient algorithms. The idea of accelerating the conjugate gradient by utilizing the conjugacy condition information and quadratic model. The gradient of the objective function is used to specify the search directions for traditional techniques. This work provides nonlinear conjugate gradient algorithms that primarily consider objective function information. One of the ways is as efficient as or more efficient than the conventional methods, according to numerical examples.