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.
Neural Information Processing Systems
Nov-20-2025, 16:02:55 GMT
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