  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 Cplex 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.
