Data Driven Optimization

Data-driven optimization (DDO) leverages machine learning to efficiently solve complex optimization problems, aiming to surpass the limitations of traditional methods by learning optimal solutions from data. Current research emphasizes developing general-purpose optimizers using diverse architectures like deep learning models, reinforcement learning, and Bayesian optimization, often incorporating techniques like boosting and mixture-of-experts to enhance performance and address challenges such as the "optimizer's curse" and limited generalization. This field is significant for its potential to accelerate design processes across various domains, from engineering and materials science to operations research and resource management, by providing efficient and reliable solutions to previously intractable problems. Furthermore, research is actively addressing the need for explainability and robustness in DDO methods to foster trust and wider adoption.