报告题目 (Title)：Exploring the Learning-based Optimization Algorithms（基于学习的优化算法）
报告人 (Speaker)：文再文 教授（北京大学）
报告时间 (Time)：2024年8月28日 (周三) 15:00
报告地点 (Place)：校本部GJ303
邀请人(Inviter)：徐姿 教授
报告摘要：This talk will explore new paradigms for integrating data, models, algorithms, and theories in mathematical optimization. Firstly, we try to understand acceleration methods through ordinary differential equations (ODEs). Under convergence and stability conditions, we formulate a learning optimization problem that minimizes stopping time. This involves transforming the rapid convergence observed in continuous-time models into discrete-time iterative methods based on data. Next, we introduce a Monte Carlo strategy optimization algorithm for solving integer programming problems. This approach constructs probabilistic models to learn parameterized strategy distributions from data, enabling the sampling of integer solutions. Lastly, we discuss the vision of advancing automated theorem proving through formalization assisted by artificial intelligence.