QR Iteration

QR iteration, a fundamental numerical algorithm, is used to find eigenvalues and eigenvectors of matrices, crucial for various applications. Current research focuses on improving its efficiency and applying it in diverse fields, including reservoir computing (optimizing time-shift selection for improved prediction accuracy) and feature selection (using rank-revealing QR factorization for dimensionality reduction and improved classification). These advancements enhance the speed and effectiveness of QR iteration, leading to improvements in machine learning models, computer vision algorithms, and other computationally intensive tasks.