Convex Minimization

Convex minimization focuses on finding the minimum of a convex function, a problem fundamental to many areas of science and engineering. Current research emphasizes developing efficient algorithms, such as gradient descent variants with adaptive step sizes and line searches, for solving these problems, particularly in distributed and high-dimensional settings, and incorporating machine learning predictions to accelerate convergence. These advancements are crucial for improving the scalability and performance of optimization methods in diverse applications, including machine learning, operations research, and control systems.