Multi Level Optimization

Multi-level optimization tackles complex problems by breaking them into nested sub-problems, each optimized sequentially or concurrently. Current research focuses on improving efficiency and scalability through techniques like projection-free variance reduction, neural network surrogates for computationally expensive simulations, and hierarchical optimization strategies across different levels of abstraction (e.g., hardware, algorithm, model). These advancements are impacting diverse fields, enhancing performance in areas such as machine learning model training, robotics (SLAM), and the optimization of complex systems like fluidic injection nozzles. The ultimate goal is to solve large-scale optimization problems more efficiently and effectively than traditional single-level approaches.