Hard Thresholding

Iterative Hard Thresholding (IHT) is a family of algorithms used to solve sparse optimization problems, aiming to find solutions with a minimal number of non-zero elements. Current research focuses on improving IHT's efficiency and convergence properties, particularly in high-dimensional settings with noisy or incomplete data, through techniques like stochastic variance reduction and adaptive regularization. These advancements are crucial for applications such as compressed sensing, machine learning (including neural network training), and graph sparsity optimization, where efficient sparse solutions are essential for both computational tractability and model interpretability.