  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.
