Multiplicative Update

Multiplicative updates are iterative optimization methods that adjust model parameters by multiplicative factors, offering advantages in specific contexts like non-negative matrix factorization (NMF) and deep learning. Current research focuses on improving their efficiency and robustness, exploring applications in compressed data processing, online convex optimization across various symmetric cones, and accelerating deep learning training. These advancements enhance the performance and stability of algorithms across diverse machine learning tasks, impacting both theoretical understanding and practical applications in areas such as data analysis and artificial intelligence.