Convergence Rate

Convergence rate analysis in machine learning focuses on determining how quickly algorithms approach optimal solutions, a crucial factor for efficiency and scalability. Current research investigates convergence rates across diverse algorithms, including stochastic gradient descent (SGD) and its variants, federated learning methods, and policy gradient approaches, often within specific contexts like high-dimensional optimization or heterogeneous data distributions. Understanding and improving convergence rates is vital for developing more efficient machine learning models and enabling their application to increasingly complex problems, impacting both theoretical understanding and practical deployment.