On dissipative symplectic integration with applications to gradient-based optimization
On dissipative symplectic integration with applications to gradient-based optimization
Abstract Recently, continuous-time dynamical systems have proved useful in providing conceptual and quantitative insights into gradient-based optimization, widely used in modern machine learning and statistics. An important question that arises in this line of work is how to discretize the system in such a way that its stability and rates …