ISTA

ISTA (Iterative Shrinkage-Thresholding Algorithm) is a method used in data analysis and signal processing to solve certain optimization problems involving sparse solutions. It works by repeatedly updating estimates of the desired signal or model, gradually reducing errors while encouraging zeros in the solution to promote simplicity and interpretability. Think of it as a systematic way to find the most efficient, streamlined representation of data, especially when dealing with noisy or incomplete information. ISTA is popular in compressed sensing, machine learning, and image reconstruction because it balances accuracy with computational efficiency.