Per Iteration

"Per-iteration" efficiency in optimization algorithms is a crucial area of research, focusing on minimizing the computational cost of each step in iterative processes used to solve large-scale problems. Current efforts concentrate on developing stochastic optimization methods, leveraging techniques like variance reduction and parallel sampling, particularly within machine learning and inverse problems, and adapting them to various model architectures including diffusion models and neural networks. These advancements are significant because reducing per-iteration costs directly translates to faster training times and improved scalability for a wide range of applications, from image processing and scientific data analysis to large-scale game playing and AI safety.