Part I by Elias B. Khalil:

  • 组合优化导论 Introduction to combinatorial optimization & Tutorial overview.
    • Modeling decision-making problems with Mixed Integer Programming (MIP);
    • Complexity and solution approaches (exact and heuristic);
    • Real-world applications;
    • Data-driven algorithm design.

Part 2 by Elias B. Khalil

  • 机器学习方法 The pure ML approach: predicting feasible solutions.
    • Reinforcement learning for combinatorial optimization;
    • Neural network architectures for representing graph problems;
    • Limitations: lack of guarantees, scalability challenges.

Part 3 by Didier Chételat & Maxime Gasse: [slides]

  • 混合方法 The hybrid approach: improving exact solvers with ML.
    • The branch-and-bound framework for mixed-integer linear programs (MIP);
    • Standard approaches to solver engineering;
    • Learning solver search policies: a Markov decision process (MDP) perspective;
    • Overview of tasks of interest;
    • Open challenges for ML/RL.

Part 4 by Giulia Zarpellon & Laurent Charlin

  • 机器学习MIP解决 Machine learning for MIP solving: challenges & literature.
    • Hands-on ML-for-MIP with a focus on the Branching problem;
    • Representations & Features;
    • Generalization notions;
    • Data & Metrics.

Part 5 by Antoine Prouvost

  • Ecole: A python framework for learning in exact MIP solvers.
    • A streamlined interface for doing ML in the open-source MIP solver SCIP, based on OpenAI Gym;
    • Example: "learning to branch'' using Ecole;
    • Easily extending predefined environments for your own research; Performance evaluation and analysis.

Part 6 by Bistra Dilkina 决策 Decision-focused Learning. Integrating LP/MIP combinatorial downstream tasks end-to-end in learning; Integrating graph optimization tasks end-to-end in learning.

Part 7 by Andrea Lodi: [slides]

  • Concluding remarks and new frontiers.
    • Business applications;
    • Recap of various contributions in this area;
    • Evaluation and Challenges going forward.