提出ID-PAS+框架，让机器学习更好处理混合整数规划中的异构变量。
ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programs
- 设计身份感知机制，让模型识别不同变量类型并针对性预测
- 在真实大规模问题上优于Gurobi和现有PAS方法
- 适合需要高效求解复杂混合整数规划的工业场景
混合整数线性规划（MIPs）是建模各类组合优化问题的强大工具。预测-搜索方法通过预测模型估计有前景的变量赋值，并引导搜索过程寻找高质量解。近期研究证明，将机器学习引入预测-搜索框架可显著提升性能，但现有方法仅适用于纯二值问题，且忽略现实场景中常见的固定变量结构。本文将预测-搜索（PAS）框架扩展至通用参数化MIPs，提出ID-PAS+——一种身份感知学习框架，使机器学习模型更有效地处理异构变量类型。在多个真实世界大规模问题上的实验表明，ID-PAS+在性能上持续优于当前最优求解器Gurobi及现有PAS方法。
原文摘要 · Abstract (English)
Mixed-Integer Linear Programs (MIPs) are powerful and flexible tools for modeling a wide range of real-world combinatorial optimization problems. Predict-and-Search methods operate by using a predictive model to estimate promising variable assignments and then guiding a search procedure toward high-quality solutions. Recent research has demonstrated that incorporating machine learning (ML) into the Predict-and-Search framework significantly enhances its performance. Still, it is restricted to binary-only problems and overlooks the presence of fixed variable structures that commonly arise in real-world settings. This work extends the current Predict-and-Search (PAS) framework to parametric general parametric MIPs and introduces ID-PAS+, an identity-aware learning framework that enables the ML model to handle heterogeneous variable types more effectively. Experiments on several real-world large-scale problems demonstrate that ID-PAS+ consistently achieves superior performance compared to the state-of-the-art solver Gurobi and PAS.
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