Learning to optimize: A tutorial for continuous and mixed-integer optimization

TL;DR

This tutorial introduces Learning to Optimize (L2O), a machine learning approach that enhances traditional optimization methods by exploiting problem structures for faster and more adaptive solutions in continuous and mixed-integer optimization.

Contribution

It provides a comprehensive guide on applying L2O techniques to accelerate, estimate, and reshape optimization problems, bridging optimization and machine learning.

Findings

L2O can significantly speed up optimization algorithms.

L2O enables better solution estimation in complex problems.

The tutorial offers practical insights for applying L2O in real-world scenarios.

Abstract

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.