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
Oct-7-2024, 09:09:29 GMT
- Country:
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.15)
- Genre:
- Research Report > New Finding (0.46)
- Technology: