Direct Runge-Kutta Discretization Achieves Acceleration

Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie

Neural Information Processing Systems 

We study gradient-based optimization methods obtained by directly discretizing a second-order ordinary differential equation (ODE) related to the continuous limit of Nesterov's accelerated gradient method. When the function is smooth enough, we show that acceleration can be achieved by a stable discretization of this ODE using standard Runge-Kutta integrators.