Iterative Algorithm

Iterative algorithms are computational methods that repeatedly refine an approximate solution until a desired level of accuracy is reached. Current research focuses on improving their efficiency, robustness, and convergence properties, particularly within machine learning (e.g., deep unfolding networks, gradient descent variants), optimization (e.g., proximal gradient methods, approximate message passing), and signal processing. These advancements are crucial for tackling complex problems in various fields, including image processing, time series forecasting, and large-scale data analysis, by enabling faster and more accurate solutions.