Learning Based Optimization

Learning-based optimization leverages machine learning to enhance the efficiency and effectiveness of optimization algorithms across diverse scientific and engineering domains. Current research focuses on integrating machine learning models, such as neural networks (including LSTMs and graph neural networks), Bayesian optimization, and reinforcement learning, with traditional optimization techniques like simulated annealing and branch-and-bound to solve complex problems, often involving high dimensionality or incomplete information. This approach is proving valuable in various applications, from optimizing industrial processes (e.g., battery manufacturing, nonwoven production) and designing complex systems (e.g., 6G networks, particle accelerators) to improving the performance of numerical solvers. The resulting improvements in speed, accuracy, and robustness are significantly impacting fields where traditional optimization methods fall short.