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.

翻译：在数字无处不在的时代，跨行业的资源高效管理与决策至关重要。为此，我们全面研究了将机器学习（ML）技术集成到华为云OptVerse AI求解器中的方法，旨在缓解现实世界数学规划实例稀缺的问题，并超越传统优化技术的能力。我们展示了利用生成式模型生成复杂SAT和MILP实例的方法，这些模型能模拟现实问题中复杂的多面结构。此外，我们引入了一种利用增强策略的训练框架，以保持求解器在动态环境中的效用。除数据生成与增强外，我们所提出的方法还包括用于个性化求解器策略的基于ML的新型策略，重点介绍了图卷积网络在初始基选择中的应用以及利用强化学习进行高级预求解与剪枝选择。此外，我们详述了整合最先进的参数调优算法的方式，这些算法显著提升了求解器的性能。与Gurobi和SCIP等传统求解器相比，经ML增强的OptVerse AI求解器在既定基准测试和现实场景中均展现出更优的速度与精度，进一步验证了机器学习技术在数学规划求解器中的实践必要性与有效性。