In an era of digital ubiquity, efficient resource management and
decision-making are paramount across numerous industries. To this end, we
present a comprehensive study on the integration of machine learning (ML)
techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the
scarcity of real-world mathematical programming instances, and to surpass the
capabilities of traditional optimization techniques. We showcase our methods
for generating complex SAT and MILP instances utilizing generative models that
mirror multifaceted structures of real-world problem. Furthermore, we introduce
a training framework leveraging augmentation policies to maintain solvers'
utility in dynamic environments. Besides the data generation and augmentation,
our proposed approaches also include novel ML-driven policies for personalized
solver strategies, with an emphasis on applications like graph convolutional
networks for initial basis selection and reinforcement learning for advanced
presolving and cut selection. Additionally, we detail the incorporation of
state-of-the-art parameter tuning algorithms which markedly elevate solver
performance. Compared with traditional solvers such as Gurobi and SCIP, our
ML-augmented OptVerse AI Solver demonstrates superior speed and precision
across both established benchmarks and real-world scenarios, reinforcing the
practical imperative and effectiveness of machine learning techniques in
mathematical programming solvers.

通过机器学习技术将其整合进华为云的 OptVerse AI Solver，以解决真实世界数学规划实例的稀缺性，并超越传统优化技术的能力，本研究展示了生成复杂 SAT 和 MILP 实例的方法，利用生成模型来模拟真实世界问题的多方面结构。此外，我们还提出了采用增强策略的训练框架，以在动态环境中保持求解器的效用。除了数据生成和增强外，我们还提出了新颖的基于机器学习的策略，用于个性化求解器策略，重点是应用如图卷积网络进行初始基选择和强化学习进行高级预处理和剪切选择。此外，我们详细介绍了最先进的参数调整算法的整合，这显著提高了求解器的性能。与传统的求解器如 Gurobi 和 SCIP 相比，我们的 ML-augmented OptVerse AI Solver 在已建立的基准和真实场景中展示出更高的速度和准确性，这也强调了机器学习技术在数学规划求解器中的实际必要性和有效性。