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.

翻译：学习优化（L2O）处于传统优化与机器学习的交叉领域，它利用机器学习的能力来增强传统优化技术。由于现实世界中的优化问题常常具有共同的结构，L2O提供了一种工具来利用这些结构以获得更好或更快的解决方案。本教程深入探讨L2O技术，介绍如何加速优化算法、快速估计解，甚至重塑优化问题本身，使其更适应实际应用。通过考虑成功应用L2O的前提条件以及当前优化问题的结构，本教程为从业者和研究人员提供了一份全面的指南。