P$ Norm

P-norms are mathematical functions measuring the magnitude of vectors or matrices, generalizing the familiar Euclidean distance (L2 norm). Current research focuses on optimizing algorithms for p-norm minimization in various contexts, including sparse learning (L0 and L1 norms), robust regression (L1 and Lp norms), and deep learning (Lp norms for regularization and knowledge distillation). These advancements are crucial for improving the efficiency and robustness of machine learning models, particularly in high-dimensional data settings and applications like autonomous driving and image processing where computational cost and noise resilience are paramount. The development of efficient algorithms for handling different p-norms is driving progress across numerous fields.