ISL

ISL, or Iterative Shrinkage-Thresholding Algorithm, is a method used to solve optimization problems, especially in areas like signal processing and machine learning. It helps find solutions that balance fitting the data well while keeping the model simple, often by promoting sparsity (few important features). ISL works by repeatedly updating the solution estimate, gradually "shrinking" certain components toward zero while adjusting towards better data fit. This iterative process continues until a stable, optimal solution is found, making it useful for tasks like compressed sensing, image reconstruction, and feature selection.