Centralized Algorithm

Centralized algorithms aim to solve computational problems by consolidating data and processing power in a single location, offering potentially faster and more consistent solutions compared to distributed approaches. Current research focuses on improving the efficiency and scalability of centralized algorithms, particularly in areas like model predictive control, large language model reasoning, and federated learning, often employing techniques such as iterative synchronization, majorization-minimization, and graph neural networks to address challenges like data privacy and computational complexity. These advancements are significant for diverse applications, including robotics, machine learning, and network optimization, by enabling the efficient processing of large datasets and complex tasks while potentially improving accuracy and reducing computational time.