Accelerating Optimization via Differentiable Stopping Time
The authors introduce a differentiable stopping time to allow direct gradient-based optimization of how fast an iterative algorithm converges to a target accuracy. They connect discrete iteration steps to continuous dynamics, enabling algorithms to be trained not just for final loss, but for the most efficient path (minimum iterations) to reach that loss. This facilitates faster convergence in applications like L2O and online hyperparameter adaptation.