Here \(\mathbf{\nabla}f(\mathbf{x}_{t})\) is the gradient vector
calculated at \(\mathbf{x}_{t}\).
To improve the convergence of first-order methods, second-order methods
use the opposite direction of the gradient vector improved by the
information of the Hessian matrix, which provides second-order
information. The Modified Newton method, for example, adopts a unitary
step size, and the method is described by Eq. (3).