Which second-order optimization method uses the inverse of the Hessian matrix to determine the update direction?

Newton's Method

Newton's Method converges faster than gradient descent by using local curvature information. However, calculating and inverting the Hessian matrix is computationally expensive, often leading to the use of Quasi-Newton methods like L-BFGS.