Distributed Optimization

Distributed optimization aims to leverage the computational power of multiple machines to optimize massive datasets distributed across these machines, in order to achieve the optimal value of a specific objective function. This method can effectively handle large-scale datasets, improve computational efficiency and scalability, and has significant application value in machine learning and data processing under big data environments.