Model Free Optimization

Model-free optimization focuses on finding optimal solutions without requiring explicit knowledge of the underlying system's model, relying instead on direct observation and feedback. Current research emphasizes efficient algorithms like reinforcement learning and metaheuristics (e.g., swarm optimization), as well as active learning strategies for selecting informative data points in applications such as in-context learning for large language models and hyperparameter tuning for machine learning. This approach is significant because it enables optimization of complex systems, including those difficult to model analytically, with applications ranging from optical computing and quantum thermal machines to improving the efficiency of machine learning workflows.