How do second-order optimizers improve cost minimization?

Second-order optimizers use curvature information to improve convergence efficiency.

Unlike first-order methods that use gradients, second-order optimizers incorporate Hessian approximations to adjust step sizes based on curvature. This leads to faster convergence in well-conditioned regions but is computationally expensive in deep learning due to large parameter spaces.