Base Algorithm

Base algorithms form the foundation of many machine learning and optimization tasks, serving as the core computational engine upon which more sophisticated methods are built. Current research focuses on improving base algorithm performance through techniques like automated learning rate tuning (e.g., using methods inspired by online convex optimization) and intelligent initialization strategies (e.g., leveraging large language models to generate superior starting points). These advancements aim to enhance efficiency and accuracy across diverse applications, from database optimization and deep learning to entity matching and resource allocation problems, ultimately leading to faster and more robust solutions.