Asynchronous Iteration

Asynchronous iteration is a computational paradigm that performs iterative updates to a model without requiring strict synchronization between processing units, enabling parallel and distributed computation. Current research focuses on improving the convergence properties of asynchronous algorithms, particularly addressing challenges posed by unbounded delays and out-of-order updates, often within the context of convex optimization and reinforcement learning. This approach is crucial for scaling machine learning and optimization problems to large datasets and complex models, offering significant speedups and enabling the solution of previously intractable problems. Key algorithmic developments include adaptive step-size strategies that respond to varying communication delays and doubly-asynchronous methods that introduce asynchrony into multiple dimensions of the problem.