Traditional Optimization

Traditional optimization aims to find the best solution to a problem, often involving complex objective functions and constraints. Current research emphasizes improving efficiency and scalability, particularly for large-scale problems, through techniques like mixed-precision arithmetic and bi-level optimization frameworks incorporating reinforcement learning or surrogate models. These advancements are crucial for tackling computationally expensive tasks in diverse fields, from electric machine design and network control to image processing and generating diverse optimal solutions in machine learning. The development of more efficient and robust optimization methods has significant implications for accelerating scientific discovery and improving real-world applications.