Optimization Proxy

Optimization proxies are machine learning models designed to approximate the solutions of computationally expensive optimization problems, aiming to significantly reduce solution times while maintaining solution quality. Current research focuses on developing robust and efficient proxies using various architectures, including Bayesian neural networks, diffusion models, and graph neural networks, often incorporating techniques like meta-learning and self-supervised learning to improve performance and reduce data requirements. This field is crucial for addressing real-world challenges in diverse domains such as energy systems, e-commerce, and federated learning, where rapid and reliable solutions to complex optimization problems are essential. Furthermore, ongoing work addresses issues like proxy discrimination and optimality verification to ensure fairness and trustworthiness in applications.