Learning to optimize: A tutorial for continuous and mixed-integer optimization
It provides a comprehensive guide for practitioners and researchers working on continuous and mixed-integer optimization problems, though it is incremental as it builds on existing L2O methods.
This tutorial addresses the challenge of enhancing traditional optimization techniques by using machine learning to exploit common structures in real-world problems, aiming to accelerate algorithms, estimate solutions promptly, or reshape problems for better adaptability.
Learning to Optimize (L2O) stands at the intersection of traditional optimization and machine learning, utilizing the capabilities of machine learning to enhance conventional optimization techniques. As real-world optimization problems frequently share common structures, L2O provides a tool to exploit these structures for better or faster solutions. This tutorial dives deep into L2O techniques, introducing how to accelerate optimization algorithms, promptly estimate the solutions, or even reshape the optimization problem itself, making it more adaptive to real-world applications. By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand, this tutorial provides a comprehensive guide for practitioners and researchers alike.