Collaborative Optimization

Collaborative optimization focuses on coordinating multiple agents or systems to achieve a shared objective, often involving complex, high-dimensional problems. Current research emphasizes developing scalable and robust algorithms, such as those based on multiconvex optimization, Bayesian optimization, and federated learning, to handle diverse data sources and model architectures, including deep neural networks and spatiotemporal graph models. This field is crucial for advancing various applications, from improving the efficiency of distributed machine learning and resource allocation in smart cities to optimizing engineering systems and enhancing the accuracy of data analysis across multiple institutions.